From 7620fcbe05df60d3ffd340a524a541bdb7e95f48 Mon Sep 17 00:00:00 2001 From: Ratna Priya Date: Thu, 11 Jul 2019 14:02:03 +0530 Subject: [PATCH] Main File --- Sentiment_Classification_Projects.ipynb | 165023 +++++++++++++++++++++ 1 file changed, 165023 insertions(+) create mode 100644 Sentiment_Classification_Projects.ipynb diff --git a/Sentiment_Classification_Projects.ipynb b/Sentiment_Classification_Projects.ipynb new file mode 100644 index 0000000..6260792 --- /dev/null +++ b/Sentiment_Classification_Projects.ipynb @@ -0,0 +1,165023 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n", + "\n", + "by Andrew Trask\n", + "\n", + "- **Twitter**: @iamtrask\n", + "- **Blog**: http://iamtrask.github.io" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What You Should Already Know\n", + "\n", + "- neural networks, forward and back-propagation\n", + "- stochastic gradient descent\n", + "- mean squared error\n", + "- and train/test splits\n", + "\n", + "### Where to Get Help if You Need it\n", + "- Re-watch previous Udacity Lectures\n", + "- Leverage the recommended Course Reading Material - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) (Check inside your classroom for a discount code)\n", + "- Shoot me a tweet @iamtrask\n", + "\n", + "\n", + "### Tutorial Outline:\n", + "\n", + "- Intro: The Importance of \"Framing a Problem\" (this lesson)\n", + "\n", + "- [Curate a Dataset](#lesson_1)\n", + "- [Developing a \"Predictive Theory\"](#lesson_2)\n", + "- [**PROJECT 1**: Quick Theory Validation](#project_1)\n", + "\n", + "\n", + "- [Transforming Text to Numbers](#lesson_3)\n", + "- [**PROJECT 2**: Creating the Input/Output Data](#project_2)\n", + "\n", + "\n", + "- Putting it all together in a Neural Network (video only - nothing in notebook)\n", + "- [**PROJECT 3**: Building our Neural Network](#project_3)\n", + "\n", + "\n", + "- [Understanding Neural Noise](#lesson_4)\n", + "- [**PROJECT 4**: Making Learning Faster by Reducing Noise](#project_4)\n", + "\n", + "\n", + "- [Analyzing Inefficiencies in our Network](#lesson_5)\n", + "- [**PROJECT 5**: Making our Network Train and Run Faster](#project_5)\n", + "\n", + "\n", + "- [Further Noise Reduction](#lesson_6)\n", + "- [**PROJECT 6**: Reducing Noise by Strategically Reducing the Vocabulary](#project_6)\n", + "\n", + "\n", + "- [Analysis: What's going on in the weights?](#lesson_7)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "nbpresent": { + "id": "56bb3cba-260c-4ebe-9ed6-b995b4c72aa3" + } + }, + "source": [ + "# Lesson: Curate a Dataset\n", + "The cells from here until Project 1 include code Andrew shows in the videos leading up to mini project 1. We've included them so you can run the code along with the videos without having to type in everything." + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": { + "nbpresent": { + "id": "eba2b193-0419-431e-8db9-60f34dd3fe83" + } + }, + "outputs": [], + "source": [ + "def pretty_print_review_and_label(i):\n", + " print(labels[i] + \"\\t:\\t\" + reviews[i][:80] + \"...\")\n", + "\n", + "g = open('reviews.txt','r') # What we know!\n", + "reviews = list(map(lambda x:x[:-1],g.readlines()))\n", + "g.close()\n", + "\n", + "g = open('labels.txt','r') # What we WANT to know!\n", + "labels = list(map(lambda x:x[:-1].upper(),g.readlines()))\n", + "g.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** The data in `reviews.txt` we're using has already been preprocessed a bit and contains only lower case characters. If we were working from raw data, where we didn't know it was all lower case, we would want to add a step here to convert it. That's so we treat different variations of the same word, like `The`, `the`, and `THE`, all the same way." + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25000" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(reviews)" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25000" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "metadata": { + "nbpresent": { + "id": "bb95574b-21a0-4213-ae50-34363cf4f87f" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'bromwell high is a cartoon comedy . it ran at the same time as some other programs about school life such as teachers . my years in the teaching profession lead me to believe that bromwell high s satire is much closer to reality than is teachers . the scramble to survive financially the insightful students who can see right through their pathetic teachers pomp the pettiness of the whole situation all remind me of the schools i knew and their students . when i saw the episode in which a student repeatedly tried to burn down the school i immediately recalled . . . . . . . . . at . . . . . . . . . . high . a classic line inspector i m here to sack one of your teachers . student welcome to bromwell high . i expect that many adults of my age think that bromwell high is far fetched . what a pity that it isn t '" + ] + }, + "execution_count": 190, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reviews[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": { + "nbpresent": { + "id": "e0408810-c424-4ed4-afb9-1735e9ddbd0a" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'POSITIVE'" + ] + }, + "execution_count": 191, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lesson: Develop a Predictive Theory" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "metadata": { + "nbpresent": { + "id": "e67a709f-234f-4493-bae6-4fb192141ee0" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "labels.txt \t : \t reviews.txt\n", + "\n", + "NEGATIVE\t:\tthis movie is terrible but it has some good effects . ...\n", + "POSITIVE\t:\tadrian pasdar is excellent is this film . he makes a fascinating woman . ...\n", + "NEGATIVE\t:\tcomment this movie is impossible . is terrible very improbable bad interpretat...\n", + "POSITIVE\t:\texcellent episode movie ala pulp fiction . days suicides . it doesnt get more...\n", + "NEGATIVE\t:\tdon t bother trying to watch this terrible mini series . it is a six hour bore ...\n", + "POSITIVE\t:\tthis schiffer guy is a real genius the movie is of excellent quality and both e...\n" + ] + } + ], + "source": [ + "print(\"labels.txt \\t : \\t reviews.txt\\n\")\n", + "pretty_print_review_and_label(2137)\n", + "pretty_print_review_and_label(12816)\n", + "pretty_print_review_and_label(6267)\n", + "pretty_print_review_and_label(21934)\n", + "pretty_print_review_and_label(5697)\n", + "pretty_print_review_and_label(4998)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project 1: Quick Theory Validation\n", + "\n", + "There are multiple ways to implement these projects, but in order to get your code closer to what Andrew shows in his solutions, we've provided some hints and starter code throughout this notebook.\n", + "\n", + "You'll find the [Counter](https://docs.python.org/2/library/collections.html#collections.Counter) class to be useful in this exercise, as well as the [numpy](https://docs.scipy.org/doc/numpy/reference/) library." + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import Counter\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll create three `Counter` objects, one for words from postive reviews, one for words from negative reviews, and one for all the words." + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "metadata": {}, + "outputs": [], + "source": [ + "# Create three Counter objects to store positive, negative and total counts\n", + "positive_counts = Counter()\n", + "negative_counts = Counter()\n", + "total_counts = Counter()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**TODO:** Examine all the reviews. For each word in a positive review, increase the count for that word in both your positive counter and the total words counter; likewise, for each word in a negative review, increase the count for that word in both your negative counter and the total words counter.\n", + "\n", + "**Note:** Throughout these projects, you should use `split(' ')` to divide a piece of text (such as a review) into individual words. If you use `split()` instead, you'll get slightly different results than what the videos and solutions show." + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Loop over all the words in all the reviews and increment the counts in the appropriate counter objects\n", + "for i in range(len(reviews)):\n", + " if(labels[i] == 'POSITIVE'):\n", + " for text in reviews[i].split(\" \"):\n", + " positive_counts[text] += 1\n", + " total_counts[text] += 1\n", + " else:\n", + " for text in reviews[i].split(\" \"):\n", + " negative_counts[text] += 1\n", + " total_counts[text] += 1\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following two cells to list the words used in positive reviews and negative reviews, respectively, ordered from most to least commonly used. " + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('', 550468),\n", + " ('the', 173324),\n", + " ('.', 159654),\n", + " ('and', 89722),\n", + " ('a', 83688),\n", + " ('of', 76855),\n", + " ('to', 66746),\n", + " ('is', 57245),\n", + " ('in', 50215),\n", + " ('br', 49235),\n", + " ('it', 48025),\n", + " ('i', 40743),\n", + " ('that', 35630),\n", + " ('this', 35080),\n", + " ('s', 33815),\n", + " ('as', 26308),\n", + " ('with', 23247),\n", + " ('for', 22416),\n", + " ('was', 21917),\n", + " ('film', 20937),\n", + " ('but', 20822),\n", + " ('movie', 19074),\n", + " ('his', 17227),\n", + " ('on', 17008),\n", + " ('you', 16681),\n", + " ('he', 16282),\n", + " ('are', 14807),\n", + " ('not', 14272),\n", + " ('t', 13720),\n", + " ('one', 13655),\n", + " ('have', 12587),\n", + " ('be', 12416),\n", + " ('by', 11997),\n", + " ('all', 11942),\n", + " ('who', 11464),\n", + " ('an', 11294),\n", + " ('at', 11234),\n", + " ('from', 10767),\n", + " ('her', 10474),\n", + " ('they', 9895),\n", + " ('has', 9186),\n", + " ('so', 9154),\n", + " ('like', 9038),\n", + " ('about', 8313),\n", + " ('very', 8305),\n", + " ('out', 8134),\n", + " ('there', 8057),\n", + " ('she', 7779),\n", + " ('what', 7737),\n", + " ('or', 7732),\n", + " ('good', 7720),\n", + " ('more', 7521),\n", + " ('when', 7456),\n", + " ('some', 7441),\n", + " ('if', 7285),\n", + " ('just', 7152),\n", + " ('can', 7001),\n", + " ('story', 6780),\n", + " ('time', 6515),\n", + " ('my', 6488),\n", + " ('great', 6419),\n", + " ('well', 6405),\n", + " ('up', 6321),\n", + " ('which', 6267),\n", + " ('their', 6107),\n", + " ('see', 6026),\n", + " ('also', 5550),\n", + " ('we', 5531),\n", + " ('really', 5476),\n", + " ('would', 5400),\n", + " ('will', 5218),\n", + " ('me', 5167),\n", + " ('had', 5148),\n", + " ('only', 5137),\n", + " ('him', 5018),\n", + " ('even', 4964),\n", + " ('most', 4864),\n", + " ('other', 4858),\n", + " ('were', 4782),\n", + " ('first', 4755),\n", + " ('than', 4736),\n", + " ('much', 4685),\n", + " ('its', 4622),\n", + " ('no', 4574),\n", + " ('into', 4544),\n", + " ('people', 4479),\n", + " ('best', 4319),\n", + " ('love', 4301),\n", + " ('get', 4272),\n", + " ('how', 4213),\n", + " ('life', 4199),\n", + " ('been', 4189),\n", + " ('because', 4079),\n", + " ('way', 4036),\n", + " ('do', 3941),\n", + " ('made', 3823),\n", + " ('films', 3813),\n", + " ('them', 3805),\n", + " ('after', 3800),\n", + " ('many', 3766),\n", + " ('two', 3733),\n", + " ('too', 3659),\n", + " ('think', 3655),\n", + " ('movies', 3586),\n", + " ('characters', 3560),\n", + " ('character', 3514),\n", + " ('don', 3468),\n", + " ('man', 3460),\n", + " ('show', 3432),\n", + " ('watch', 3424),\n", + " ('seen', 3414),\n", + " ('then', 3358),\n", + " ('little', 3341),\n", + " ('still', 3340),\n", + " ('make', 3303),\n", + " ('could', 3237),\n", + " ('never', 3226),\n", + " ('being', 3217),\n", + " ('where', 3173),\n", + " ('does', 3069),\n", + " ('over', 3017),\n", + " ('any', 3002),\n", + " ('while', 2899),\n", + " ('know', 2833),\n", + " ('did', 2790),\n", + " ('years', 2758),\n", + " ('here', 2740),\n", + " ('ever', 2734),\n", + " ('end', 2696),\n", + " ('these', 2694),\n", + " ('such', 2590),\n", + " ('real', 2568),\n", + " ('scene', 2567),\n", + " ('back', 2547),\n", + " ('those', 2485),\n", + " ('though', 2475),\n", + " ('off', 2463),\n", + " ('new', 2458),\n", + " ('your', 2453),\n", + " ('go', 2440),\n", + " ('acting', 2437),\n", + " ('plot', 2432),\n", + " ('world', 2429),\n", + " ('scenes', 2427),\n", + " ('say', 2414),\n", + " ('through', 2409),\n", + " ('makes', 2390),\n", + " ('better', 2381),\n", + " ('now', 2368),\n", + " ('work', 2346),\n", + " ('young', 2343),\n", + " ('old', 2311),\n", + " ('ve', 2307),\n", + " ('find', 2272),\n", + " ('both', 2248),\n", + " ('before', 2177),\n", + " ('us', 2162),\n", + " ('again', 2158),\n", + " ('series', 2153),\n", + " ('quite', 2143),\n", + " 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('audience', 1020),\n", + " ('sense', 1020),\n", + " ('truly', 1017),\n", + " ('special', 1011),\n", + " ('second', 1009),\n", + " ('short', 1009),\n", + " ('fan', 1009),\n", + " ('mind', 1005),\n", + " ('human', 1001),\n", + " ('recommend', 999),\n", + " ('full', 996),\n", + " ('black', 995),\n", + " ('help', 991),\n", + " ('along', 989),\n", + " ('trying', 987),\n", + " ('small', 986),\n", + " ('death', 985),\n", + " ('friends', 981),\n", + " ('remember', 974),\n", + " ('often', 970),\n", + " ('said', 966),\n", + " ('favorite', 962),\n", + " ('heart', 959),\n", + " ('early', 957),\n", + " ('left', 956),\n", + " ('until', 955),\n", + " ('script', 954),\n", + " ('let', 954),\n", + " ('maybe', 937),\n", + " ('today', 936),\n", + " ('live', 934),\n", + " ('less', 934),\n", + " ('moments', 933),\n", + " ('others', 929),\n", + " ('brilliant', 926),\n", + " ('shot', 925),\n", + " ('liked', 923),\n", + " ('become', 916),\n", + " ('won', 915),\n", + " ('used', 910),\n", + " ('style', 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('against', 815),\n", + " ('son', 809),\n", + " ('understand', 808),\n", + " ('lost', 807),\n", + " ('michael', 805),\n", + " ('else', 801),\n", + " ('throughout', 798),\n", + " ('fans', 797),\n", + " ('city', 792),\n", + " ('reason', 789),\n", + " ('written', 787),\n", + " ('production', 787),\n", + " ('several', 784),\n", + " ('school', 783),\n", + " ('based', 781),\n", + " ('rest', 781),\n", + " ('try', 780),\n", + " ('dead', 776),\n", + " ('hope', 775),\n", + " ('strong', 768),\n", + " ('white', 765),\n", + " ('tell', 759),\n", + " ('itself', 758),\n", + " ('half', 753),\n", + " ('person', 749),\n", + " ('sometimes', 746),\n", + " ('past', 744),\n", + " ('start', 744),\n", + " ('genre', 743),\n", + " ('beginning', 739),\n", + " ('final', 739),\n", + " ('town', 738),\n", + " ('art', 734),\n", + " ('humor', 732),\n", + " ('game', 732),\n", + " ('yes', 731),\n", + " ('idea', 731),\n", + " ('late', 730),\n", + " ('becomes', 729),\n", + " ('despite', 729),\n", + " ('able', 726),\n", + " ('case', 726),\n", + " ('money', 723),\n", + " ('child', 721),\n", + " ('completely', 721),\n", + " ('side', 719),\n", + " ('camera', 716),\n", + " ('getting', 714),\n", + " ('instead', 712),\n", + " ('soon', 702),\n", + " ('under', 700),\n", + " ('viewer', 699),\n", + " ('age', 697),\n", + " ('days', 696),\n", + " ('stories', 696),\n", + " ('felt', 694),\n", + " ('simple', 694),\n", + " ('roles', 693),\n", + " ('video', 688),\n", + " ('name', 683),\n", + " ('either', 683),\n", + " ('doing', 677),\n", + " ('turns', 674),\n", + " ('wants', 671),\n", + " ('close', 671),\n", + " ('title', 669),\n", + " ('wrong', 668),\n", + " ('went', 666),\n", + " ('james', 665),\n", + " ('evil', 659),\n", + " ('budget', 657),\n", + " ('episodes', 657),\n", + " ('relationship', 655),\n", + " ('fantastic', 653),\n", + " ('piece', 653),\n", + " ('david', 651),\n", + " ('turn', 648),\n", + " ('murder', 646),\n", + " ('parts', 645),\n", + " ('brother', 644),\n", + " ('absolutely', 643),\n", + " ('head', 643),\n", + " ('experience', 642),\n", + " ('eyes', 641),\n", + " ('sex', 638),\n", + " ('direction', 637),\n", + " ('called', 637),\n", + " ('directed', 636),\n", + " ('lines', 634),\n", + " ('behind', 633),\n", + " ('sort', 632),\n", + " ('actress', 631),\n", + " ('lead', 630),\n", + " ('oscar', 628),\n", + " ('including', 627),\n", + " ('example', 627),\n", + " ('known', 625),\n", + " ('musical', 625),\n", + " ('chance', 621),\n", + " ('score', 620),\n", + " ('already', 619),\n", + " ('feeling', 619),\n", + " ('hit', 619),\n", + " ('voice', 615),\n", + " ('moment', 612),\n", + " ('living', 612),\n", + " ('low', 610),\n", + " ('supporting', 610),\n", + " ('ago', 609),\n", + " ('themselves', 608),\n", + " ('reality', 605),\n", + " ('hilarious', 605),\n", + " ('jack', 604),\n", + " ('told', 603),\n", + " ('hand', 601),\n", + " ('quality', 600),\n", + " ('moving', 600),\n", + " ('dialogue', 600),\n", + " ('song', 599),\n", + " ('happy', 599),\n", + " ('matter', 598),\n", + " ('paul', 598),\n", + " ('light', 594),\n", + " ('future', 593),\n", + " ('entire', 592),\n", + " ('finds', 591),\n", + " ('gave', 589),\n", + " ('laugh', 587),\n", + " ('released', 586),\n", + " ('expect', 584),\n", + " ('fight', 581),\n", + " ('particularly', 580),\n", + " ('cinematography', 579),\n", + " ('police', 579),\n", + " ('whose', 578),\n", + " ('type', 578),\n", + " ('sound', 578),\n", + " ('view', 573),\n", + " ('enjoyable', 573),\n", + " ('number', 572),\n", + " ('romantic', 572),\n", + " ('husband', 572),\n", + " ('daughter', 572),\n", + " ('documentary', 571),\n", + " ('self', 570),\n", + " ('superb', 569),\n", + " ('modern', 569),\n", + " ('took', 569),\n", + " ('robert', 569),\n", + " ('mean', 566),\n", + " ('shown', 563),\n", + " ('coming', 561),\n", + " ('important', 560),\n", + " ('king', 559),\n", + " ('leave', 559),\n", + " ('change', 558),\n", + " ('somewhat', 555),\n", + " ('wanted', 555),\n", + " ('tells', 554),\n", + " ('events', 552),\n", + " ('run', 552),\n", + " 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482),\n", + " ('cool', 481),\n", + " ('release', 479),\n", + " ('anyway', 479),\n", + " ('theme', 479),\n", + " ('opening', 478),\n", + " ('entertainment', 477),\n", + " ('slow', 475),\n", + " ('ends', 475),\n", + " ('unique', 475),\n", + " ('exactly', 475),\n", + " ('easily', 474),\n", + " ('level', 474),\n", + " ('o', 474),\n", + " ('red', 474),\n", + " ('interest', 472),\n", + " ('happen', 471),\n", + " ('crime', 470),\n", + " ('viewing', 468),\n", + " ('sets', 467),\n", + " ('memorable', 467),\n", + " ('stop', 466),\n", + " ('group', 466),\n", + " ('problems', 463),\n", + " ('dance', 463),\n", + " ('working', 463),\n", + " ('sister', 463),\n", + " ('message', 463),\n", + " ('knew', 462),\n", + " ('mystery', 461),\n", + " ('nature', 461),\n", + " ('bring', 460),\n", + " ('believable', 459),\n", + " ('thinking', 459),\n", + " ('brought', 459),\n", + " ('mostly', 458),\n", + " ('disney', 457),\n", + " ('couldn', 457),\n", + " ('society', 456),\n", + " ('lady', 455),\n", + " ('within', 455),\n", + " ('blood', 454),\n", + " ('parents', 453),\n", + " ('upon', 453),\n", + " ('viewers', 453),\n", + " ('meets', 452),\n", + " ('form', 452),\n", + " ('peter', 452),\n", + " ('tom', 452),\n", + " ('usually', 452),\n", + " ('soundtrack', 452),\n", + " ('local', 450),\n", + " ('certain', 448),\n", + " ('follow', 448),\n", + " ('whether', 447),\n", + " ('possible', 446),\n", + " ('emotional', 445),\n", + " ('killed', 444),\n", + " ('above', 444),\n", + " ('de', 444),\n", + " ('god', 443),\n", + " ('middle', 443),\n", + " ('needs', 442),\n", + " ('happens', 442),\n", + " ('flick', 442),\n", + " ('masterpiece', 441),\n", + " ('period', 440),\n", + " ('major', 440),\n", + " ('named', 439),\n", + " ('haven', 439),\n", + " ('particular', 438),\n", + " ('th', 438),\n", + " ('earth', 437),\n", + " ('feature', 437),\n", + " ('stand', 436),\n", + " ('words', 435),\n", + " ('typical', 435),\n", + " ('elements', 433),\n", + " ('obviously', 433),\n", + " ('romance', 431),\n", + " ('jane', 430),\n", + " ('yourself', 427),\n", + " ('showing', 427),\n", + " ('brings', 426),\n", + " ('fantasy', 426),\n", + " ('guess', 423),\n", + " ('america', 423),\n", + " ('unfortunately', 422),\n", + " ('huge', 422),\n", + " ('indeed', 421),\n", + " ('running', 421),\n", + " ('talent', 420),\n", + " ('stage', 419),\n", + " ('started', 418),\n", + " ('leads', 417),\n", + " ('sweet', 417),\n", + " ('japanese', 417),\n", + " ('poor', 416),\n", + " ('deal', 416),\n", + " ('incredible', 413),\n", + " ('personal', 413),\n", + " ('fast', 412),\n", + " ('became', 410),\n", + " ('deep', 410),\n", + " ('hours', 409),\n", + " ('giving', 408),\n", + " ('nearly', 408),\n", + " ('dream', 408),\n", + " ('clearly', 407),\n", + " ('turned', 407),\n", + " ('obvious', 406),\n", + " ('near', 406),\n", + " ('cut', 405),\n", + " ('surprise', 405),\n", + " ('era', 404),\n", + " ('body', 404),\n", + " ('hour', 403),\n", + " ('female', 403),\n", + " ('five', 403),\n", + " ('note', 399),\n", + " ('learn', 398),\n", + " ('truth', 398),\n", + " ('except', 397),\n", + " ('feels', 397),\n", + " ('match', 397),\n", + " ('tony', 397),\n", + " ('filmed', 394),\n", + " ('clear', 394),\n", + " ('complete', 394),\n", + " ('street', 393),\n", + " ('eventually', 393),\n", + " ('keeps', 393),\n", + " ('older', 393),\n", + " ('lots', 393),\n", + " ('buy', 392),\n", + " ('william', 391),\n", + " ('stewart', 391),\n", + " ('fall', 390),\n", + " ('joe', 390),\n", + " ('meet', 390),\n", + " ('unlike', 389),\n", + " ('talking', 389),\n", + " ('shots', 389),\n", + " ('rating', 389),\n", + " ('difficult', 389),\n", + " ('dramatic', 388),\n", + " ('means', 388),\n", + " ('situation', 386),\n", + " ('wonder', 386),\n", + " ('present', 386),\n", + " ('appears', 386),\n", + " ('subject', 386),\n", + " ('comments', 385),\n", + " ('general', 383),\n", + " ('sequences', 383),\n", + " ('lee', 383),\n", + " ('points', 382),\n", + " ('earlier', 382),\n", + " ('gone', 379),\n", + " ('check', 379),\n", + " ('suspense', 378),\n", + " ('recommended', 378),\n", + " ('ten', 378),\n", + " ('third', 377),\n", + " ('business', 377),\n", + " ('talk', 375),\n", + " ('leaves', 375),\n", + " ('beyond', 375),\n", + " ('portrayal', 374),\n", + " ('beautifully', 373),\n", + " ('single', 372),\n", + " ('bill', 372),\n", + " ('plenty', 371),\n", + " ('word', 371),\n", + " ('whom', 370),\n", + " ('falls', 370),\n", + " ('scary', 369),\n", + " ('non', 369),\n", + " ('figure', 369),\n", + " ('battle', 369),\n", + " ('using', 368),\n", + " ('return', 368),\n", + " ('doubt', 367),\n", + " ('add', 367),\n", + " ('hear', 366),\n", + " ('solid', 366),\n", + " ('success', 366),\n", + " ('jokes', 365),\n", + " ('oh', 365),\n", + " ('touching', 365),\n", + " ('political', 365),\n", + " ('hell', 364),\n", + " ('awesome', 364),\n", + " ('boys', 364),\n", + " ('sexual', 362),\n", + " ('recently', 362),\n", + " ('dog', 362),\n", + " ('please', 361),\n", + " ('wouldn', 361),\n", + " ('straight', 361),\n", + " ('features', 361),\n", + " ('forget', 360),\n", + " ('setting', 360),\n", + " ('lack', 360),\n", + " ('married', 359),\n", + " ('mark', 359),\n", + " ('social', 357),\n", + " ('interested', 356),\n", + " ('adventure', 356),\n", + " ('actual', 355),\n", + " ('terrific', 355),\n", + " ('sees', 355),\n", + " ('brothers', 355),\n", + " ('move', 354),\n", + " ('call', 354),\n", + " ('various', 353),\n", + " ('theater', 353),\n", + " ('dr', 353),\n", + " ('animated', 352),\n", + " ('western', 351),\n", + " ('baby', 350),\n", + " ('space', 350),\n", + " ('leading', 348),\n", + " ('disappointed', 348),\n", + " ('portrayed', 346),\n", + " ('aren', 346),\n", + " ('screenplay', 345),\n", + " ('smith', 345),\n", + " ('towards', 344),\n", + " ('hate', 344),\n", + " ('noir', 343),\n", + " ('outstanding', 342),\n", + " ('decent', 342),\n", + " ('kelly', 342),\n", + " ('directors', 341),\n", + " ('journey', 341),\n", + " ('none', 340),\n", + " ('looked', 340),\n", + " ('effective', 340),\n", + " ('storyline', 339),\n", + " ('caught', 339),\n", + " ('sci', 339),\n", + " ('fi', 339),\n", + " ('cold', 339),\n", + " ('mary', 339),\n", + " ('rich', 338),\n", + " ('charming', 338),\n", + " ('popular', 337),\n", + " ('rare', 337),\n", + " ('manages', 337),\n", + " ('harry', 337),\n", + " ('spirit', 336),\n", + " ('appreciate', 335),\n", + " ('open', 335),\n", + " ('moves', 334),\n", + " ('basically', 334),\n", + " ('acted', 334),\n", + " ('inside', 333),\n", + " ('boring', 333),\n", + " ('century', 333),\n", + " ('mention', 333),\n", + " ('deserves', 333),\n", + " ('subtle', 333),\n", + " ('pace', 333),\n", + " ('familiar', 332),\n", + " ('background', 332),\n", + " ('ben', 331),\n", + " ('creepy', 330),\n", + " ('supposed', 330),\n", + " ('secret', 329),\n", + " ('die', 328),\n", + " ('jim', 328),\n", + " ('question', 327),\n", + " ('effect', 327),\n", + " ('natural', 327),\n", + " ('impressive', 326),\n", + " ('rate', 326),\n", + " ('language', 326),\n", + " ('saying', 325),\n", + " ('intelligent', 325),\n", + " ('telling', 324),\n", + " ('realize', 324),\n", + " ('material', 324),\n", + " ('scott', 324),\n", + " ('singing', 323),\n", + " ('dancing', 322),\n", + " ('visual', 321),\n", + " ('adult', 321),\n", + " ('imagine', 321),\n", + " ('kept', 320),\n", + " ('office', 320),\n", + " ('uses', 319),\n", + " ('pure', 318),\n", + " ('wait', 318),\n", + " ('stunning', 318),\n", + " ('review', 317),\n", + " ('previous', 317),\n", + " ('copy', 317),\n", + " ('seriously', 317),\n", + " ('reading', 316),\n", + " ('create', 316),\n", + " ('hot', 316),\n", + " ('created', 316),\n", + " ('magic', 316),\n", + " ('somehow', 316),\n", + " ('stay', 315),\n", + " ('attempt', 315),\n", + " ('escape', 315),\n", + " ('crazy', 315),\n", + " ('air', 315),\n", + " ('frank', 315),\n", + " ('hands', 314),\n", + " ('filled', 313),\n", + " ('expected', 312),\n", + " ('average', 312),\n", + " ('surprisingly', 312),\n", + " ('complex', 311),\n", + " ('quickly', 310),\n", + " ('successful', 310),\n", + " ('studio', 310),\n", + " ('plus', 309),\n", + " ('male', 309),\n", + " ('co', 307),\n", + " ('images', 306),\n", + " ('casting', 306),\n", + " ('following', 306),\n", + " ('minute', 306),\n", + " ('exciting', 306),\n", + " ('members', 305),\n", + " ('follows', 305),\n", + " ('themes', 305),\n", + " ('german', 305),\n", + " ('reasons', 305),\n", + " ('e', 305),\n", + " ('touch', 304),\n", + " ('edge', 304),\n", + " ('free', 304),\n", + " ('cute', 304),\n", + " ('genius', 304),\n", + " ('outside', 303),\n", + " ('reviews', 302),\n", + " ('admit', 302),\n", + " ('ok', 302),\n", + " ('younger', 302),\n", + " ('fighting', 301),\n", + " ('odd', 301),\n", + " ('master', 301),\n", + " ('recent', 300),\n", + " ('thanks', 300),\n", + " ('break', 300),\n", + " ('comment', 300),\n", + " ('apart', 299),\n", + " ('emotions', 298),\n", + " ('lovely', 298),\n", + " ('begin', 298),\n", + " ('doctor', 297),\n", + " ('party', 297),\n", + " ('italian', 297),\n", + " ('la', 296),\n", + " ('missed', 296),\n", + " ...]" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Examine the counts of the most common words in positive reviews\n", + "positive_counts.most_common()" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('', 561462),\n", + " ('.', 167538),\n", + " ('the', 163389),\n", + " ('a', 79321),\n", + " ('and', 74385),\n", + " ('of', 69009),\n", + " ('to', 68974),\n", + " ('br', 52637),\n", + " ('is', 50083),\n", + " ('it', 48327),\n", + " ('i', 46880),\n", + " ('in', 43753),\n", + " ('this', 40920),\n", + " ('that', 37615),\n", + " ('s', 31546),\n", + " ('was', 26291),\n", + " ('movie', 24965),\n", + " ('for', 21927),\n", + " ('but', 21781),\n", + " ('with', 20878),\n", + " ('as', 20625),\n", + " ('t', 20361),\n", + " ('film', 19218),\n", + " ('you', 17549),\n", + " ('on', 17192),\n", + " ('not', 16354),\n", + " ('have', 15144),\n", + " ('are', 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332),\n", + " ('wrote', 331),\n", + " ('villain', 331),\n", + " ('gun', 331),\n", + " ('working', 331),\n", + " ('island', 330),\n", + " ('strong', 330),\n", + " ('open', 330),\n", + " ('realize', 330),\n", + " ('positive', 329),\n", + " ('disappointing', 329),\n", + " ('yeah', 329),\n", + " ('quickly', 329),\n", + " ('weren', 328),\n", + " ('release', 328),\n", + " ('simple', 328),\n", + " ('honestly', 328),\n", + " ('eventually', 327),\n", + " ('period', 327),\n", + " ('tells', 327),\n", + " ('kills', 327),\n", + " ('doctor', 327),\n", + " ('nowhere', 326),\n", + " ('list', 326),\n", + " ('acted', 326),\n", + " ('herself', 326),\n", + " ('dog', 326),\n", + " ('walk', 325),\n", + " ('air', 324),\n", + " ('apart', 324),\n", + " ('makers', 323),\n", + " ('subject', 323),\n", + " ('learn', 322),\n", + " ('fi', 322),\n", + " ('sci', 319),\n", + " ('bother', 319),\n", + " ('admit', 319),\n", + " ('jack', 318),\n", + " ('disappointment', 318),\n", + " ('hands', 318),\n", + " ('note', 318),\n", + " ('certain', 317),\n", + " ('e', 317),\n", + " ('value', 317),\n", + " ('casting', 317),\n", + " ('grade', 316),\n", + " ('peter', 316),\n", + " ('suddenly', 315),\n", + " ('missing', 315),\n", + " ('form', 313),\n", + " ('stick', 313),\n", + " ('previous', 313),\n", + " ('break', 313),\n", + " ('soundtrack', 312),\n", + " ('surprised', 311),\n", + " ('front', 311),\n", + " ('expecting', 311),\n", + " ('parents', 310),\n", + " ('surprise', 310),\n", + " ('relationship', 310),\n", + " ('shoot', 309),\n", + " ('today', 309),\n", + " ('painful', 308),\n", + " ('ways', 308),\n", + " ('leaves', 308),\n", + " ('ended', 308),\n", + " ('creepy', 308),\n", + " ('concept', 308),\n", + " ('somewhere', 308),\n", + " ('vampire', 308),\n", + " ('spend', 307),\n", + " ('th', 307),\n", + " ('future', 306),\n", + " ('difficult', 306),\n", + " ('effect', 306),\n", + " ('fighting', 306),\n", + " ('street', 306),\n", + " ('c', 305),\n", + " ('america', 305),\n", + " ('accent', 304),\n", + " ('truth', 302),\n", + " ('project', 302),\n", + " ('joe', 301),\n", + " ('f', 301),\n", + " ('deal', 301),\n", + " ('indeed', 301),\n", + " ('biggest', 300),\n", + " ('rate', 300),\n", + " ('paul', 299),\n", + " ('japanese', 299),\n", + " ('utterly', 298),\n", + " ('begins', 298),\n", + " ('redeeming', 298),\n", + " ('college', 298),\n", + " ('york', 297),\n", + " ('fairly', 297),\n", + " ('disney', 297),\n", + " ('crew', 296),\n", + " ('create', 296),\n", + " ('cartoon', 296),\n", + " ('revenge', 296),\n", + " ('co', 295),\n", + " ('outside', 295),\n", + " ('computer', 295),\n", + " ('interested', 295),\n", + " ('stage', 295),\n", + " ('considering', 294),\n", + " ('speak', 294),\n", + " ('among', 294),\n", + " ('towards', 293),\n", + " ('channel', 293),\n", + " ('sick', 293),\n", + " ('talented', 292),\n", + " ('cause', 292),\n", + " ('particular', 292),\n", + " ('van', 292),\n", + " ('hair', 292),\n", + " ('bottom', 291),\n", + " ('reasons', 291),\n", + " ('mediocre', 290),\n", + " ('cat', 290),\n", + " ('telling', 290),\n", + " ('supporting', 289),\n", + " ('store', 289),\n", + " ('hoping', 288),\n", + " ('waiting', 288),\n", + " ...]" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Examine the counts of the most common words in negative reviews\n", + "negative_counts.most_common()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, common words like \"the\" appear very often in both positive and negative reviews. Instead of finding the most common words in positive or negative reviews, what you really want are the words found in positive reviews more often than in negative reviews, and vice versa. To accomplish this, you'll need to calculate the **ratios** of word usage between positive and negative reviews.\n", + "\n", + "**TODO:** Check all the words you've seen and calculate the ratio of postive to negative uses and store that ratio in `pos_neg_ratios`. \n", + ">Hint: the positive-to-negative ratio for a given word can be calculated with `positive_counts[word] / float(negative_counts[word]+1)`. Notice the `+1` in the denominator – that ensures we don't divide by zero for words that are only seen in positive reviews." + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Counter object to store positive/negative ratios\n", + "pos_neg_ratios = Counter()\n", + "# TODO: Calculate the ratios of positive and negative uses of the most common words\n", + "# Consider words to be \"common\" if they've been used at least 100 times\n", + "\n", + "for term,cnt in list(total_counts.most_common()):\n", + " if(cnt > 100):\n", + " pos_neg_ratios[term] = positive_counts[term] / float(negative_counts[term]+1)\n", + " #pos_neg_ratios[term] = pos_neg_ratio\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examine the ratios you've calculated for a few words:" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pos-to-neg ratio for 'the' = 1.0607993145235326\n", + "Pos-to-neg ratio for 'amazing' = 4.022813688212928\n", + "Pos-to-neg ratio for 'terrible' = 0.17744252873563218\n" + ] + } + ], + "source": [ + "print(\"Pos-to-neg ratio for 'the' = {}\".format(pos_neg_ratios[\"the\"]))\n", + "print(\"Pos-to-neg ratio for 'amazing' = {}\".format(pos_neg_ratios[\"amazing\"]))\n", + "print(\"Pos-to-neg ratio for 'terrible' = {}\".format(pos_neg_ratios[\"terrible\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looking closely at the values you just calculated, we see the following:\n", + "\n", + "* Words that you would expect to see more often in positive reviews – like \"amazing\" – have a ratio greater than 1. The more skewed a word is toward postive, the farther from 1 its positive-to-negative ratio will be.\n", + "* Words that you would expect to see more often in negative reviews – like \"terrible\" – have positive values that are less than 1. The more skewed a word is toward negative, the closer to zero its positive-to-negative ratio will be.\n", + "* Neutral words, which don't really convey any sentiment because you would expect to see them in all sorts of reviews – like \"the\" – have values very close to 1. A perfectly neutral word – one that was used in exactly the same number of positive reviews as negative reviews – would be almost exactly 1. The `+1` we suggested you add to the denominator slightly biases words toward negative, but it won't matter because it will be a tiny bias and later we'll be ignoring words that are too close to neutral anyway.\n", + "\n", + "Ok, the ratios tell us which words are used more often in postive or negative reviews, but the specific values we've calculated are a bit difficult to work with. A very positive word like \"amazing\" has a value above 4, whereas a very negative word like \"terrible\" has a value around 0.18. Those values aren't easy to compare for a couple of reasons:\n", + "\n", + "* Right now, 1 is considered neutral, but the absolute value of the postive-to-negative rations of very postive words is larger than the absolute value of the ratios for the very negative words. So there is no way to directly compare two numbers and see if one word conveys the same magnitude of positive sentiment as another word conveys negative sentiment. So we should center all the values around netural so the absolute value fro neutral of the postive-to-negative ratio for a word would indicate how much sentiment (positive or negative) that word conveys.\n", + "* When comparing absolute values it's easier to do that around zero than one. \n", + "\n", + "To fix these issues, we'll convert all of our ratios to new values using logarithms.\n", + "\n", + "**TODO:** Go through all the ratios you calculated and convert them to logarithms. (i.e. use `np.log(ratio)`)\n", + "\n", + "In the end, extremely positive and extremely negative words will have positive-to-negative ratios with similar magnitudes but opposite signs." + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Convert ratios to logs\n", + "for text,ratio in pos_neg_ratios.most_common():\n", + " pos_neg_ratios[text] = np.log(ratio)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examine the new ratios you've calculated for the same words from before:" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pos-to-neg ratio for 'the' = 0.05902269426102881\n", + "Pos-to-neg ratio for 'amazing' = 1.3919815802404802\n", + "Pos-to-neg ratio for 'terrible' = -1.7291085042663878\n" + ] + } + ], + "source": [ + "print(\"Pos-to-neg ratio for 'the' = {}\".format(pos_neg_ratios[\"the\"]))\n", + "print(\"Pos-to-neg ratio for 'amazing' = {}\".format(pos_neg_ratios[\"amazing\"]))\n", + "print(\"Pos-to-neg ratio for 'terrible' = {}\".format(pos_neg_ratios[\"terrible\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If everything worked, now you should see neutral words with values close to zero. In this case, \"the\" is near zero but slightly positive, so it was probably used in more positive reviews than negative reviews. But look at \"amazing\"'s ratio - it's above `1`, showing it is clearly a word with positive sentiment. And \"terrible\" has a similar score, but in the opposite direction, so it's below `-1`. It's now clear that both of these words are associated with specific, opposing sentiments.\n", + "\n", + "Now run the following cells to see more ratios. \n", + "\n", + "The first cell displays all the words, ordered by how associated they are with postive reviews. (Your notebook will most likely truncate the output so you won't actually see *all* the words in the list.)\n", + "\n", + "The second cell displays the 30 words most associated with negative reviews by reversing the order of the first list and then looking at the first 30 words. (If you want the second cell to display all the words, ordered by how associated they are with negative reviews, you could just write `reversed(pos_neg_ratios.most_common())`.)\n", + "\n", + "You should continue to see values similar to the earlier ones we checked – neutral words will be close to `0`, words will get more positive as their ratios approach and go above `1`, and words will get more negative as their ratios approach and go below `-1`. That's why we decided to use the logs instead of the raw ratios." + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('edie', 4.6913478822291435),\n", + " ('paulie', 4.07753744390572),\n", + " ('felix', 3.152736022363656),\n", + " ('polanski', 2.8233610476132043),\n", + " ('matthau', 2.80672172860924),\n", + " ('victoria', 2.681021528714291),\n", + " ('mildred', 2.6026896854443837),\n", + " ('gandhi', 2.538973871058276),\n", + " ('flawless', 2.451005098112319),\n", + " ('superbly', 2.26002547857525),\n", + " ('perfection', 2.159484249353372),\n", + " ('astaire', 2.1400661634962708),\n", + " ('captures', 2.038619547159581),\n", + " ('voight', 2.030170492673053),\n", + " ('wonderfully', 2.0218960560332353),\n", + " ('powell', 1.978345424808467),\n", + " ('brosnan', 1.9547990964725592),\n", + " ('lily', 1.9203768470501485),\n", + " ('bakshi', 1.9029851043382795),\n", + " ('lincoln', 1.9014583864844796),\n", 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('broadway', 0.5903376866937243),\n", + " ('anna', 0.5877866649021191),\n", + " ('peace', 0.5862840350175841),\n", + " ('visually', 0.5853943192634992),\n", + " ('morality', 0.5852582185487603),\n", + " ('falk', 0.5852582185487603),\n", + " ('growing', 0.5846665375658754),\n", + " ('experiences', 0.5831462853456169),\n", + " ('stood', 0.5831462853456169),\n", + " ('touch', 0.58122926435596),\n", + " ('lives', 0.5810976767513224),\n", + " ('kubrick', 0.5806691971332549),\n", + " ('timing', 0.5804740180558324),\n", + " ('expressions', 0.5798184952529422),\n", + " ('struggles', 0.5798184952529422),\n", + " ('authentic', 0.5784842722398056),\n", + " ('helen', 0.5776342934381009),\n", + " ('pre', 0.5770075306472918),\n", + " ('quirky', 0.5753641449035618),\n", + " ('young', 0.5753167234453431),\n", + " ('inner', 0.5745414381520985),\n", + " ('mexico', 0.5744308737205633),\n", + " ('clint', 0.5738004229273791),\n", + " ('sisters', 0.5728610146854434),\n", + " ('realism', 0.5722652889994956),\n", + " ('french', 0.5720692490067093),\n", + " ('personalities', 0.5720692490067093),\n", + " ('surprises', 0.5711322299969818),\n", + " ('adventures', 0.5711322299969818),\n", + " ('overcome', 0.5697681593994407),\n", + " ('timothy', 0.5695332245927687),\n", + " ('tales', 0.5690945318899664),\n", + " ('war', 0.5684331730278168),\n", + " ('civil', 0.5679840376059393),\n", + " ('countries', 0.5673777932709119),\n", + " ('streep', 0.5671064596645803),\n", + " ('tradition', 0.5668534552356532),\n", + " ('oliver', 0.5667332557042867),\n", + " ('australia', 0.5658077581833438),\n", + " ('understanding', 0.5653138090500605),\n", + " ('players', 0.5650952537000482),\n", + " ('knowing', 0.5648928450362665),\n", + " ('rogers', 0.5642134971840521),\n", + " ('suspenseful', 0.5636891133230585),\n", + " ('variety', 0.5636891133230585),\n", + " ('true', 0.5628152518081007),\n", + " ('jr', 0.5622098231124694),\n", + " ('psychological', 0.5610874585468789),\n", + " ('sent', 0.5596157879354227),\n", + " ('grand', 0.5596157879354227),\n", + " ('branagh', 0.5596157879354227),\n", + " ('reminiscent', 0.5596157879354227),\n", + " ('performing', 0.5596157879354227),\n", + " ('wealth', 0.5596157879354227),\n", + " ('overwhelming', 0.5596157879354227),\n", + " ('odds', 0.5596157879354227),\n", + " ('brothers', 0.5589118104336285),\n", + " ('howard', 0.5581108967560025),\n", + " ('david', 0.5569312225647537),\n", + " ('generation', 0.556287997842748),\n", + " ('grow', 0.5561253829956542),\n", + " ('survival', 0.5559460590464603),\n", + " ('mainstream', 0.5557473111575023),\n", + " ('dick', 0.5543107357057295),\n", + " ('charm', 0.5528817557540786),\n", + " ('kirk', 0.5527898228650229),\n", + " ('twists', 0.5524472984568102),\n", + " ('gangster', 0.5520685823000399),\n", + " ('jeff', 0.5517930622542137),\n", + " ('family', 0.5511624451006553),\n", + " ('tend', 0.5505330733611034),\n", + " ('thanks', 0.5504908801584222),\n", + " ('world', 0.5474423472343264),\n", + " ('sutherland', 0.5474353693785516),\n", + " ('life', 0.5469551443495992),\n", + " ('disc', 0.5465437063680699),\n", + " ('bug', 0.5465437063680699),\n", + " ('tribute', 0.5455111817538808),\n", + " ('europe', 0.5452270504833231),\n", + " ('sacrifice', 0.5443015529623801),\n", + " ('color', 0.5440512713943111),\n", + " ('superior', 0.5433349023312852),\n", + " ('york', 0.5431823586653651),\n", + " ('pulls', 0.5426662296216495),\n", + " ('jackson', 0.5423242908253617),\n", + " ('hearts', 0.5423242908253617),\n", + " ('enjoy', 0.5412428513590611),\n", + " ('redemption', 0.5405675929647282),\n", + " ('madness', 0.540384426007535),\n", + " ('stands', 0.5389965007326869),\n", + " ('trial', 0.5389965007326869),\n", + " ('greek', 0.5389965007326869),\n", + " ('hamilton', 0.5389965007326869),\n", + " ('each', 0.5388212312554177),\n", + " ('faithful', 0.5377330766859151),\n", + " ('received', 0.5372768098531604),\n", + " ('documentaries', 0.537142932083364),\n", + " ('jealous', 0.537142932083364),\n", + " ('different', 0.5370986068246082),\n", + " ('describes', 0.5368011101692514),\n", + " ('shorts', 0.5359615970375329),\n", + " ('brilliance', 0.5355182363563621),\n", + " ('mountains', 0.5349231753450512),\n", + " ('share', 0.5340824859302579),\n", + " ('dealt', 0.5340824859302579),\n", + " ('providing', 0.5332984796180493),\n", + " ('explore', 0.5332984796180493),\n", + " ('series', 0.5325809226575603),\n", + " ('fellow', 0.5323318289869543),\n", + " ('loves', 0.5306282510621704),\n", + " ('revolution', 0.5306282510621704),\n", + " ('olivier', 0.5306282510621704),\n", + " ('roman', 0.5306282510621704),\n", + " ('century', 0.5300278307499267),\n", + " ('musical', 0.5296687115674706),\n", + " ('heroic', 0.5292593254548287),\n", + " ('approach', 0.5280674302004967),\n", + " ('ironically', 0.5280674302004967),\n", + " ('temple', 0.5280674302004967),\n", + " ('moves', 0.5279372642387119),\n", + " ('gift', 0.5270203096859714),\n", + " ('julie', 0.5260930958967791),\n", + " ('tells', 0.52415107836314),\n", + " ('radio', 0.5239467117286878),\n", + " ('uncle', 0.5235443961737654),\n", + " ('union', 0.5232481437645479),\n", + " ('deep', 0.523095716357805),\n", + " ('reminds', 0.5215784155422524),\n", + " ('famous', 0.5211884108015372),\n", + " ('jazz', 0.5205344378929515),\n", + " ('dennis', 0.5198754592859086),\n", + " ('epic', 0.5191938734365074),\n", + " ('adult', 0.519167695083386),\n", + " ('shows', 0.519153222203753),\n", + " ('performed', 0.5191244265806858),\n", + " ('demons', 0.5191244265806858),\n", + " ('discovered', 0.5187937934151675),\n", + " ('eric', 0.5187937934151675),\n", + " ('youth', 0.5185626062681431),\n", + " ('human', 0.5185141122498709),\n", + " ('tarzan', 0.5181382706122772),\n", + " ('ourselves', 0.5179430915348546),\n", + " ('wwii', 0.5175824062288704),\n", + " ('passion', 0.5162164724008671),\n", + " ('desire', 0.5160749796521344),\n", + " ('pays', 0.5158131652770298),\n", + " ('dirty', 0.5155762265245886),\n", + " ('fox', 0.5155762265245886),\n", + " ('sympathetic', 0.5154660033224929),\n", + " ('symbolism', 0.5154660033224929),\n", + " ('attitude', 0.5153099362133193),\n", + " ('appearances', 0.5146644000731564),\n", + " ('jeremy', 0.5146644000731564),\n", + " ('fun', 0.5143906899304869),\n", + " ('south', 0.5142097217502312),\n", + " ('arrives', 0.5140989491109599),\n", + " ('present', 0.5134196589430373),\n", + " ('com', 0.5132616785638717),\n", + " ('smile', 0.5126588048476517),\n", + " ('alan', 0.5108256237659907),\n", + " ('ring', 0.5108256237659907),\n", + " ('visit', 0.5108256237659907),\n", + " ('fits', 0.5108256237659907),\n", + " ('provided', 0.5108256237659907),\n", + " ('carter', 0.5108256237659907),\n", + " ('aging', 0.5108256237659907),\n", + " ('countryside', 0.5108256237659907),\n", + " ('begins', 0.5101565036339665),\n", + " ('success', 0.5090057870490047),\n", + " ('japan', 0.5090057870490047),\n", + " ('accurate', 0.5089547158301789),\n", + " ('proud', 0.5080047474243493),\n", + " ('daily', 0.5075946031845443),\n", + " ('karloff', 0.5072478024181067),\n", + " ('atmospheric', 0.5072478024181067),\n", + " ('recently', 0.5071491490366821),\n", + " ('fu', 0.5070449009260847),\n", + " ('horrors', 0.5065612249795332),\n", + " ('finding', 0.5063712734166104),\n", + " ('lust', 0.5059356384717989),\n", + " ('hitchcock', 0.50574947073413),\n", + " ('among', 0.5033400495133273),\n", + " ('viewing', 0.5030213982744091),\n", + " ('investigation', 0.5026288565618122),\n", + " ('shining', 0.5026288565618122),\n", + " ('duo', 0.5020919437972361),\n", + " ('cameron', 0.5020919437972361),\n", + " ('finds', 0.501283031005398),\n", + " ('contemporary', 0.5007752879124892),\n", + " ('genuine', 0.500462836730444),\n", + " ('frightening', 0.49995595152908684),\n", + " ('plays', 0.49975983848890226),\n", + " ('age', 0.49941323171424595),\n", + " ('position', 0.4989911661189878),\n", + " ('continues', 0.4986303506721724),\n", + " ('roles', 0.4983971655075218),\n", + " ('james', 0.498372162694704),\n", + " ('individuals', 0.4982468415591305),\n", + " ('brought', 0.49783842823917956),\n", + " ('hilarious', 0.4971455198619106),\n", + " ('brutal', 0.49681488669639234),\n", + " ('appropriate', 0.49643688631389105),\n", + " ('dance', 0.4958199831481205),\n", + " ('league', 0.49578774640145024),\n", + " ('helping', 0.49578774640145024),\n", + " ('answers', 0.49578774640145024),\n", + " ('stunts', 0.49561620510246196),\n", + " ('traveling', 0.4953214372300254),\n", + " ('thoroughly', 0.49414593456733524),\n", + " ('depicted', 0.4931706885272699),\n", + " ('combination', 0.49247648509779424),\n", + " ('honor', 0.49247648509779424),\n", + " ('differences', 0.49247648509779424),\n", + " ('fully', 0.4921334907538381),\n", + " ('tracy', 0.49159426183810306),\n", + " ('battles', 0.4914075379088891),\n", + " ('possibility', 0.4911205526866582),\n", + " ('romance', 0.4901589869574316),\n", + " ('initially', 0.49002249613622745),\n", + " ('happy', 0.4898997500608791),\n", + " ('crime', 0.48977221456815834),\n", + " ('singing', 0.4893852925281213),\n", + " ('especially', 0.48901267837860624),\n", + " ('shakespeare', 0.4875479388966451),\n", + " ('hugh', 0.4872951263557966),\n", + " ('detail', 0.4860948425082735),\n", + " ('julia', 0.4855078157817008),\n", + " ('san', 0.4855078157817008),\n", + " ('guide', 0.4855078157817008),\n", + " ('desperation', 0.4855078157817008),\n", + " ('companion', 0.4855078157817008),\n", + " ('strongly', 0.48460242866688824),\n", + " ('necessary', 0.48302334245403883),\n", + " ('humanity', 0.48265474679929443),\n", + " ('drama', 0.48221998493060503),\n", + " ('nonetheless', 0.4818380868927384),\n", + " ('intrigue', 0.4818380868927384),\n", + " ('warming', 0.4818380868927384),\n", + " ('cuba', 0.4818380868927384),\n", + " ('planned', 0.4795730802618863),\n", + " ('pictures', 0.4792993701192168),\n", + " ('broadcast', 0.4784902431230542),\n", + " ('nine', 0.47803580094299974),\n", + " ('settings', 0.47743860773325364),\n", + " ('history', 0.4773296693378085),\n", + " ('ordinary', 0.4772588001269074),\n", + " ('trade', 0.47692407209030935),\n", + " ('official', 0.4760826753221178),\n", + " ('primary', 0.4760826753221178),\n", + " ('episode', 0.4752962026115043),\n", + " ('role', 0.47520268270188676),\n", + " ('spirit', 0.4747769079983932),\n", + " ('grey', 0.4740936144972607),\n", + " ('ways', 0.47323464982718205),\n", + " ('cup', 0.472604410945793),\n", + " ('piano', 0.472604410945793),\n", + " ('familiar', 0.4724161756511195),\n", + " ('sinister', 0.4719857904497268),\n", + " ('reveal', 0.47171449364936496),\n", + " ('max', 0.4715085204251558),\n", + " ('dated', 0.4712164856709448),\n", + " ('losing', 0.47000362924573563),\n", + " ('discovery', 0.47000362924573563),\n", + " ('vicious', 0.47000362924573563),\n", + " ('genuinely', 0.46871413841586385),\n", + " ('hatred', 0.46734051182625186),\n", + " ('mistaken', 0.4670230011075978),\n", + " ('dream', 0.46608972992459924),\n", + " ('challenge', 0.46608972992459924),\n", + " ('crisis', 0.46575733836428446),\n", + " ('photographed', 0.4648885285789651),\n", + " ('critics', 0.4643056081310978),\n", + " ('bird', 0.4643056081310978),\n", + " ('machines', 0.4643056081310978),\n", + " ('born', 0.4641138351896721),\n", + " ('detective', 0.4636633473511525),\n", + " ('higher', 0.46328467899699055),\n", + " ('remains', 0.46262352194811296),\n", + " ('inevitable', 0.46262352194811296),\n", + " ('soviet', 0.4618180446592961),\n", + " ('ryan', 0.461345566502621),\n", + " ('african', 0.46112595521371813),\n", + " ('smaller', 0.46081520319132935),\n", + " ('techniques', 0.46052488529119184),\n", + " ('information', 0.4603417183339986),\n", + " ('deserved', 0.45999798712841444),\n", + " ('lynch', 0.45953232937844013),\n", + " ('spielberg', 0.45953232937844013),\n", + " ('cynical', 0.45953232937844013),\n", + " ('tour', 0.45953232937844013),\n", + " ('francisco', 0.45953232937844013),\n", + " ('struggle', 0.45911782160048453),\n", + " ('language', 0.4590212125771265),\n", + " ('visual', 0.4582351440882285),\n", + " ('warner', 0.45724137763188427),\n", + " ('social', 0.45720078250735313),\n", + " ('reality', 0.45719346885019546),\n", + " ('hidden', 0.4567584024957149),\n", + " ('breaking', 0.4560173872709956),\n", + " ('sometimes', 0.45563021171182794),\n", + " ('modern', 0.45500247579345005),\n", + " ('surfing', 0.4542552722775964),\n", + " ('popular', 0.45410691533051023),\n", + " ('surprised', 0.4534409399850382),\n", + " ('follows', 0.4524536175440835),\n", + " ('keeps', 0.45234869400701483),\n", + " ('john', 0.4520909494482197),\n", + " ('mixed', 0.4519851237430572),\n", + " ('defeat', 0.4519851237430572),\n", + " ('justice', 0.4514272436728002),\n", + " ('treasure', 0.45083371313801535),\n", + " ('presents', 0.44973793178615257),\n", + " ('years', 0.4491919703210497),\n", + " ('chief', 0.4489502200479032),\n", + " ('shadows', 0.44802472252696035),\n", + " ('closely', 0.4470141110210369),\n", + " ('segments', 0.4470141110210369),\n", + " ('lose', 0.446583355037637),\n", + " ('caine', 0.44628710262841953),\n", + " ('caught', 0.4461027538399907),\n", + " ('hamlet', 0.44558510189758965),\n", + " ('chinese', 0.4450742462032102),\n", + " ('welcome', 0.4443805243578379),\n", + " ('birth', 0.4436863209283622),\n", + " ('represents', 0.44320543609101143),\n", + " ('puts', 0.4427910657208508),\n", + " ('visuals', 0.44183275227903923),\n", + " ('fame', 0.44183275227903923),\n", + " ('closer', 0.44183275227903923),\n", + " ('web', 0.44183275227903923),\n", + " ('criminal', 0.4412745608048752),\n", + " ('minor', 0.4409224199448939),\n", + " ('jon', 0.44086703515908027),\n", + " ('liked', 0.4407499151402072),\n", + " ('restaurant', 0.44031183943833246),\n", + " ('de', 0.4398327516123722),\n", + " ('flaws', 0.4398327516123722),\n", + " ('searching', 0.4393666597838457),\n", + " ('rap', 0.4389130421757044),\n", + " ('light', 0.4388443301819989),\n", + " ('elizabeth', 0.4387223298646468),\n", + " ('marry', 0.4386173154250649),\n", + " ('learned', 0.4382549309311553),\n", + " ('controversial', 0.4382549309311553),\n", + " ('oz', 0.4382549309311553),\n", + " ('slowly', 0.4378566038993998),\n", + " ('comedic', 0.43721380642274466),\n", + " ('wayne', 0.43721380642274466),\n", + " ('thrilling', 0.43721380642274466),\n", + " ('bridge', 0.43721380642274466),\n", + " ('married', 0.4365850168219689),\n", + " ('nazi', 0.4361020775700542),\n", + " ('murder', 0.4353180712578455),\n", + " ('physical', 0.4353180712578455),\n", + " ('johnny', 0.43483971678806865),\n", + " ('michelle', 0.4344526449814167),\n", + " ('wallace', 0.4340384805522204),\n", + " ('comedies', 0.43395706390247063),\n", + " ('silent', 0.43395706390247063),\n", + " ('played', 0.43387244114515305),\n", + " ('international', 0.43363598507486073),\n", + " ('vision', 0.4328640822962789),\n", + " ('intelligent', 0.431967048853671),\n", + " ('shop', 0.43078291609245434),\n", + " ('also', 0.4303672020976917),\n", + " ('levels', 0.4302451371066513),\n", + " ('miss', 0.4300642671215322),\n", + " ('movement', 0.4295626596872249),\n", + " ...]" + ] + }, + "execution_count": 202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# words most frequently seen in a review with a \"POSITIVE\" label\n", + "pos_neg_ratios.most_common()" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('boll', -4.969813299576001),\n", + " ('uwe', -4.624972813284271),\n", + " ('seagal', -3.644143560272545),\n", + " ('unwatchable', -3.258096538021482),\n", + " ('stinker', -3.2088254890146994),\n", + " ('mst', -2.9502698994772336),\n", + " ('incoherent', -2.9368917735310576),\n", + " ('unfunny', -2.6922395950755678),\n", + " ('waste', -2.6193845640165536),\n", + " ('blah', -2.5704288232261625),\n", + " ('horrid', -2.4849066497880004),\n", + " ('pointless', -2.4553061800117097),\n", + " ('atrocious', -2.4259083090260445),\n", + " ('redeeming', -2.3682390632154826),\n", + " ('prom', -2.3608540011180215),\n", + " ('drivel', -2.3470368555648795),\n", + " ('lousy', -2.307572634505085),\n", + " ('worst', -2.286987896180378),\n", + " ('laughable', -2.264363880173848),\n", + " ('awful', -2.227194247027435)]" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# words most frequently seen in a review with a \"NEGATIVE\" label\n", + "list(reversed(pos_neg_ratios.most_common()))[0:20]\n", + "\n", + "# Note: Above is the code Andrew uses in his solution video, \n", + "# so we've included it here to avoid confusion.\n", + "# If you explore the documentation for the Counter class, \n", + "# you will see you could also find the 30 least common\n", + "# words like this: pos_neg_ratios.most_common()[:-31:-1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# End of Project 1. \n", + "## Watch the next video to see Andrew's solution, then continue on to the next lesson.\n", + "\n", + "# Transforming Text into Numbers\n", + "The cells here include code Andrew shows in the next video. We've included it so you can run the code along with the video without having to type in everything." + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "\n", + "review = \"This was a horrible, terrible movie.\"\n", + "\n", + "Image(filename='sentiment_network.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 205, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "review = \"The movie was excellent\"\n", + "\n", + "Image(filename='sentiment_network_pos.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project 2: Creating the Input/Output Data\n", + "\n", + "**TODO:** Create a [set](https://docs.python.org/3/tutorial/datastructures.html#sets) named `vocab` that contains every word in the vocabulary." + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'',\n", + " 'innocuously',\n", + " 'uav',\n", + " 'perscription',\n", + " 'enforcers',\n", + " 'notification',\n", + " 'karsis',\n", + " 'ruban',\n", + " 'envy',\n", + " 'huggaland',\n", + " 'persbrandt',\n", + " 'verboten',\n", + " 'organizes',\n", + " 'graaff',\n", + " 'murkwood',\n", + " 'fenech',\n", + " 'interrogations',\n", + " 'lothlorien',\n", + " 'responders',\n", + " 'burned',\n", + " 'aired',\n", + " 'nirupa',\n", + " 'inaugurate',\n", + " 'nicktoons',\n", + " 'shrunken',\n", + " 'bauerisch',\n", + " 'adjective',\n", + " 'campground',\n", + " 'visitations',\n", + " 'artery',\n", + " 'noone',\n", + " 'stimulates',\n", + " 'huggie',\n", + " 'benji',\n", + " 'biochemical',\n", + " 'unveiling',\n", + " 'chalant',\n", + " 'pastoral',\n", + " 'pittors',\n", + " 'valga',\n", + " 'nooooo',\n", + " 'brinda',\n", + " 'competent',\n", + " 'halifax',\n", + " 'etiquette',\n", + " 'bearded',\n", + " 'ewen',\n", + " 'slit',\n", + " 'cero',\n", + " 'waverly',\n", + " 'jullian',\n", + " 'hesitating',\n", + " 'squirmy',\n", + " 'extrapolating',\n", + " 'uncanonical',\n", + " 'habitually',\n", + " 'unloving',\n", + " 'mccallum',\n", + " 'yearnings',\n", + " 'beter',\n", + " 'hongurai',\n", + " 'westcourt',\n", + " 'nablus',\n", + " 'cannell',\n", + " 'ahmadinejad',\n", + " 'titillatingly',\n", + " 'combined',\n", + " 'virgina',\n", + " 'thieson',\n", + " 'puling',\n", + " 'mcgovernisms',\n", + " 'independant',\n", + " 'gusher',\n", + " 'rigomortis',\n", + " 'guerriri',\n", + " 'almoust',\n", + " 'landscaper',\n", + " 'cladon',\n", + " 'fantafestival',\n", + " 'sneha',\n", + " 'spitted',\n", + " 'warhol',\n", + " 'coworkers',\n", + " 'photographic',\n", + " 'nesher',\n", + " 'blended',\n", + " 'pandemonium',\n", + " 'bitch',\n", + " 'grudging',\n", + " 'mcdermott',\n", + " 'clot',\n", + " 'brattiness',\n", + " 'pachyderms',\n", + " 'vacations',\n", + " 'deolali',\n", + " 'viscous',\n", + " 'shouldn',\n", + " 'dearest',\n", + " 'irak',\n", + " 'arthur',\n", + " 'poorer',\n", + " 'outdid',\n", + " 'surgeons',\n", + " 'rambos',\n", + " 'potente',\n", + " 'enlighten',\n", + " 'showering',\n", + " 'quips',\n", + " 'adorning',\n", + " 'counts',\n", + " 'glynnis',\n", + " 'statue',\n", + " 'kiel',\n", + " 'unfailing',\n", + " 'layman',\n", + " 'miscommunication',\n", + " 'gentil',\n", + " 'brutalized',\n", + " 'uncompromisingly',\n", + " 'laughtracks',\n", + " 'invigorated',\n", + " 'ambivalence',\n", + " 'densely',\n", + " 'colorlessly',\n", + " 'grandstand',\n", + " 'seats',\n", + " 'naoto',\n", + " 'backwards',\n", + " 'shita',\n", + " 'kerr',\n", + " 'cele',\n", + " 'moomins',\n", + " 'uncomfortableness',\n", + " 'deserving',\n", + " 'jrvet',\n", + " 'arnim',\n", + " 'meats',\n", + " 'imaginably',\n", + " 'coincidences',\n", + " 'overpraise',\n", + " 'balboa',\n", + " 'upped',\n", + " 'tiku',\n", + " 'corbetts',\n", + " 'alarik',\n", + " 'fiancs',\n", + " 'vengence',\n", + " 'baruc',\n", + " 'pavlovian',\n", + " 'situationally',\n", + " 'messenger',\n", + " 'betterment',\n", + " 'stomachs',\n", + " 'exo',\n", + " 'weirded',\n", + " 'handed',\n", + " 'consistently',\n", + " 'gretel',\n", + " 'phrasing',\n", + " 'janning',\n", + " 'presumable',\n", + " 'burns',\n", + " 'creepier',\n", + " 'devoreaux',\n", + " 'declaiming',\n", + " 'homevideo',\n", + " 'charlo',\n", + " 'jardine',\n", + " 'shagged',\n", + " 'beta',\n", + " 'misjudges',\n", + " 'malta',\n", + " 'polution',\n", + " 'albans',\n", + " 'grifters',\n", + " 'acct',\n", + " 'iisimba',\n", + " 'gargantua',\n", + " 'collera',\n", + " 'flavorful',\n", + " 'neutrality',\n", + " 'zack',\n", + " 'furthest',\n", + " 'audience',\n", + " 'campiness',\n", + " 'surfs',\n", + " 'blinders',\n", + " 'cocksure',\n", + " 'sweatshops',\n", + " 'muldaur',\n", + " 'carter',\n", + " 'eliminating',\n", + " 'gorcey',\n", + " 'scalisi',\n", + " 'atan',\n", + " 'breslin',\n", + " 'profusely',\n", + " 'departs',\n", + " 'homemaker',\n", + " 'fish',\n", + " 'bigg',\n", + " 'flagrant',\n", + " 'commentating',\n", + " 'cartoonishness',\n", + " 'educational',\n", + " 'tayback',\n", + " 'atchison',\n", + " 'mccullers',\n", + " 'mikshelt',\n", + " 'superbad',\n", + " 'stroessner',\n", + " 'moroder',\n", + " 'univeral',\n", + " 'sanitised',\n", + " 'drafted',\n", + " 'unchristian',\n", + " 'sponsored',\n", + " 'parter',\n", + " 'shootings',\n", + " 'damone',\n", + " 'effie',\n", + " 'sandbox',\n", + " 'pratically',\n", + " 'boyens',\n", + " 'gina',\n", + " 'stinkeye',\n", + " 'queues',\n", + " 'kwong',\n", + " 'trips',\n", + " 'european',\n", + " 'giraudot',\n", + " 'pendanski',\n", + " 'moonbeast',\n", + " 'defended',\n", + " 'ridiculously',\n", + " 'wrongs',\n", + " 'bankol',\n", + " 'linton',\n", + " 'newscaster',\n", + " 'released',\n", + " 'screenplays',\n", + " 'yoon',\n", + " 'unshakably',\n", + " 'andrienne',\n", + " 'grindhouse',\n", + " 'trogar',\n", + " 'simonson',\n", + " 'tenements',\n", + " 'notre',\n", + " 'unfortunates',\n", + " 'lieving',\n", + " 'illegal',\n", + " 'relieving',\n", + " 'unforgivably',\n", + " 'moly',\n", + " 'jocelyn',\n", + " 'patterns',\n", + " 'pimps',\n", + " 'demolitions',\n", + " 'frock',\n", + " 'scaling',\n", + " 'philippa',\n", + " 'thunderhead',\n", + " 'consist',\n", + " 'bilardo',\n", + " 'inverse',\n", + " 'yakusyo',\n", + " 'reinterpretation',\n", + " 'pineyro',\n", + " 'bevy',\n", + " 'annis',\n", + " 'remo',\n", + " 'players',\n", + " 'mercedes',\n", + " 'glimmers',\n", + " 'margaux',\n", + " 'broadcast',\n", + " 'almeria',\n", + " 'waaaaaayyyy',\n", + " 'vertiginous',\n", + " 'steal',\n", + " 'flashdance',\n", + " 'frisch',\n", + " 'bumpuses',\n", + " 'focuses',\n", + " 'caf',\n", + " 'practicalities',\n", + " 'grittier',\n", + " 'roeh',\n", + " 'rumbling',\n", + " 'salo',\n", + " 'assortment',\n", + " 'unhinged',\n", + " 'horsell',\n", + " 'hadass',\n", + " 'aquitane',\n", + " 'whichlegend',\n", + " 'withe',\n", + " 'manitoba',\n", + " 'misquotes',\n", + " 'dynamite',\n", + " 'representation',\n", + " 'brooklyners',\n", + " 'pflug',\n", + " 'genetic',\n", + " 'pres',\n", + " 'halen',\n", + " 'passersby',\n", + " 'tropes',\n", + " 'solemnity',\n", + " 'girdle',\n", + " 'internationalist',\n", + " 'giger',\n", + " 'tidied',\n", + " 'imprimatur',\n", + " 'supersadlysoftie',\n", + " 'saltwater',\n", + " 'anspach',\n", + " 'vigilantism',\n", + " 'mantraps',\n", + " 'lousier',\n", + " 'susanne',\n", + " 'exectioner',\n", + " 'stalinism',\n", + " 'foxs',\n", + " 'obtrusively',\n", + " 'costard',\n", + " 'neatly',\n", + " 'quake',\n", + " 'irs',\n", + " 'logistical',\n", + " 'fingerprinting',\n", + " 'wow',\n", + " 'pretagonist',\n", + " 'quests',\n", + " 'fruits',\n", + " 'truckstops',\n", + " 'uninfected',\n", + " 'bruneau',\n", + " 'wong',\n", + " 'yells',\n", + " 'jianxiang',\n", + " 'hertzfeldt',\n", + " 'degenerating',\n", + " 'yeh',\n", + " 'bandmates',\n", + " 'inmho',\n", + " 'happend',\n", + " 'meetings',\n", + " 'distanced',\n", + " 'smirk',\n", + " 'jour',\n", + " 'manifestly',\n", + " 'strings',\n", + " 'milwall',\n", + " 'dappled',\n", + " 'subtlely',\n", + " 'autre',\n", + " 'marushka',\n", + " 'rubell',\n", + " 'untastful',\n", + " 'durn',\n", + " 'prehysteria',\n", + " 'rappel',\n", + " 'traynor',\n", + " 'phenoms',\n", + " 'scored',\n", + " 'boards',\n", + " 'intermixed',\n", + " 'devistation',\n", + " 'wachtang',\n", + " 'diazes',\n", + " 'acre',\n", + " 'leered',\n", + " 'darkest',\n", + " 'ruler',\n", + " 'latterday',\n", + " 'david',\n", + " 'aforesaid',\n", + " 'ichii',\n", + " 'ratt',\n", + " 'firmly',\n", + " 'disslikes',\n", + " 'distancing',\n", + " 'betting',\n", + " 'marcia',\n", + " 'ketchim',\n", + " 'emit',\n", + " 'assery',\n", + " 'symbolising',\n", + " 'selznick',\n", + " 'meridian',\n", + " 'whippet',\n", + " 'audry',\n", + " 'arcs',\n", + " 'strongest',\n", + " 'gwyne',\n", + " 'joanie',\n", + " 'leggage',\n", + " 'cuervo',\n", + " 'servo',\n", + " 'website',\n", + " 'hurls',\n", + " 'unmindful',\n", + " 'stupidities',\n", + " 'bernhards',\n", + " 'tustin',\n", + " 'pquerette',\n", + " 'podunksville',\n", + " 'sincronicity',\n", + " 'oh',\n", + " 'masseratti',\n", + " 'dragoncon',\n", + " 'intermarriage',\n", + " 'hat',\n", + " 'entwistle',\n", + " 'sisson',\n", + " 'underprivileged',\n", + " 'eerie',\n", + " 'nameless',\n", + " 'adotped',\n", + " 'pised',\n", + " 'bromidic',\n", + " 'marker',\n", + " 'aldiss',\n", + " 'beo',\n", + " 'burgerlers',\n", + " 'sloppier',\n", + " 'kenobi',\n", + " 'klieg',\n", + " 'nang',\n", + " 'chemotherapy',\n", + " 'vette',\n", + " 'refugee',\n", + " 'circumcision',\n", + " 'aaaaah',\n", + " 'days',\n", + " 'charasmatic',\n", + " 'helena',\n", + " 'avenues',\n", + " 'knuckled',\n", + " 'my',\n", + " 'belied',\n", + " 'anglican',\n", + " 'davison',\n", + " 'suspenseful',\n", + " 'weberian',\n", + " 'swilling',\n", + " 'mst',\n", + " 'ballad',\n", + " 'vase',\n", + " 'anansie',\n", + " 'tatum',\n", + " 'kirschner',\n", + " 'werewolve',\n", + " 'conjoined',\n", + " 'quietness',\n", + " 'sinner',\n", + " 'spoilerphobic',\n", + " 'dishy',\n", + " 'calibrated',\n", + " 'cinephilia',\n", + " 'fantastically',\n", + " 'jayce',\n", + " 'sentient',\n", + " 'charlesmanson',\n", + " 'deflector',\n", + " 'shazza',\n", + " 'pities',\n", + " 'tlahuac',\n", + " 'tashed',\n", + " 'shades',\n", + " 'abhisheh',\n", + " 'faramir',\n", + " 'sala',\n", + " 'splinters',\n", + " 'toxicology',\n", + " 'lightfoot',\n", + " 'opfergang',\n", + " 'graboids',\n", + " 'serafinowicz',\n", + " 'lgbt',\n", + " 'anorexic',\n", + " 'jaqui',\n", + " 'juscar',\n", + " 'fries',\n", + " 'poseiden',\n", + " 'babbitt',\n", + " 'clamps',\n", + " 'burrows',\n", + " 'depravation',\n", + " 'wesa',\n", + " 'levin',\n", + " 'tibbett',\n", + " 'reiser',\n", + " 'reba',\n", + " 'swte',\n", + " 'diddy',\n", + " 'hanley',\n", + " 'unexpectedness',\n", + " 'stadiums',\n", + " 'chuckie',\n", + " 'rutting',\n", + " 'crossed',\n", + " 'jermaine',\n", + " 'accordingly',\n", + " 'donations',\n", + " 'landlady',\n", + " 'scots',\n", + " 'boning',\n", + " 'jailhouse',\n", + " 'voce',\n", + " 'alarm',\n", + " 'platfrom',\n", + " 'objectors',\n", + " 'salomaa',\n", + " 'barrio',\n", + " 'clergymen',\n", + " 'intangibility',\n", + " 'vannoordlet',\n", + " 'insectoids',\n", + " 'dissing',\n", + " 'horlicks',\n", + " 'idylls',\n", + " 'ludicrously',\n", + " 'briggitta',\n", + " 'dolman',\n", + " 'joe',\n", + " 'standardized',\n", + " 'impresses',\n", + " 'raju',\n", + " 'ladys',\n", + " 'grard',\n", + " 'sapping',\n", + " 'nibelungs',\n", + " 'freckles',\n", + " 'snog',\n", + " 'redefine',\n", + " 'fiscal',\n", + " 'disappearance',\n", + " 'civilizing',\n", + " 'megaphone',\n", + " 'acquired',\n", + " 'expedient',\n", + " 'generalities',\n", + " 'bennet',\n", + " 'joyrides',\n", + " 'enguled',\n", + " 'pallio',\n", + " 'irrelevancy',\n", + " 'corrected',\n", + " 'sparsely',\n", + " 'pushers',\n", + " 'amicable',\n", + " 'reveres',\n", + " 'sodomised',\n", + " 'simulate',\n", + " 'tribeswomen',\n", + " 'enacts',\n", + " 'ennia',\n", + " 'sorbonne',\n", + " 'kilpatrick',\n", + " 'bleaching',\n", + " 'curt',\n", + " 'mutate',\n", + " 'shipwrecked',\n", + " 'mei',\n", + " 'bootleggers',\n", + " 'fumblingly',\n", + " 'signifiers',\n", + " 'hellbent',\n", + " 'mxyzptlk',\n", + " 'segun',\n", + " 'inane',\n", + " 'franics',\n", + " 'arc',\n", + " 'dreaful',\n", + " 'sultan',\n", + " 'procedure',\n", + " 'pier',\n", + " 'clinker',\n", + " 'laziest',\n", + " 'ise',\n", + " 'fkin',\n", + " 'probe',\n", + " 'blathered',\n", + " 'raton',\n", + " 'reprint',\n", + " 'memorization',\n", + " 'amick',\n", + " 'elope',\n", + " 'lando',\n", + " 'grossed',\n", + " 'ravindra',\n", + " 'consigned',\n", + " 'rapp',\n", + " 'misfocused',\n", + " 'strano',\n", + " 'vitae',\n", + " 'booie',\n", + " 'pushed',\n", + " 'chafed',\n", + " 'seaward',\n", + " 'motherland',\n", + " 'anjane',\n", + " 'beautifule',\n", + " 'cottages',\n", + " 'predicate',\n", + " 'rewatching',\n", + " 'slob',\n", + " 'spaces',\n", + " 'springboard',\n", + " 'irving',\n", + " 'crushes',\n", + " 'disassemble',\n", + " 'bendrix',\n", + " 'brogado',\n", + " 'guest',\n", + " 'mars',\n", + " 'blaise',\n", + " 'heists',\n", + " 'thc',\n", + " 'objectivity',\n", + " 'bird',\n", + " 'guards',\n", + " 'frogs',\n", + " 'stackhouse',\n", + " 'camerons',\n", + " 'apaches',\n", + " 'cover',\n", + " 'novelizations',\n", + " 'tah',\n", + " 'garam',\n", + " 'ranna',\n", + " 'snappy',\n", + " 'ain',\n", + " 'unscheduled',\n", + " 'steinbeck',\n", + " 'nonintentional',\n", + " 'kaspar',\n", + " 'nrw',\n", + " 'skinned',\n", + " 'obsolescence',\n", + " 'hemel',\n", + " 'acception',\n", + " 'comteg',\n", + " 'guarantee',\n", + " 'unnerve',\n", + " 'posa',\n", + " 'parekh',\n", + " 'fifi',\n", + " 'duquenne',\n", + " 'bearand',\n", + " 'coincidentially',\n", + " 'recurrent',\n", + " 'sven',\n", + " 'hen',\n", + " 'obsurdly',\n", + " 'corners',\n", + " 'humiliation',\n", + " 'homeland',\n", + " 'everage',\n", + " 'sly',\n", + " 'synopses',\n", + " 'kaal',\n", + " 'amores',\n", + " 'terminals',\n", + " 'swam',\n", + " 'feiss',\n", + " 'prideful',\n", + " 'confucian',\n", + " 'historical',\n", + " 'vicissitude',\n", + " 'dominators',\n", + " 'xizhao',\n", + " 'ekland',\n", + " 'wufei',\n", + " 'callan',\n", + " 'twink',\n", + " 'manoven',\n", + " 'andreas',\n", + " 'heap',\n", + " 'transmit',\n", + " 'grumpiest',\n", + " 'masturbated',\n", + " 'updating',\n", + " 'amend',\n", + " 'dupia',\n", + " 'landslide',\n", + " 'deedlit',\n", + " 'lightoller',\n", + " 'buxomed',\n", + " 'snowmonton',\n", + " 'inskip',\n", + " 'peter',\n", + " 'generals',\n", + " 'appointment',\n", + " 'lathan',\n", + " 'dixen',\n", + " 'lemons',\n", + " 'brilliantly',\n", + " 'scarfing',\n", + " 'svankmajer',\n", + " 'enunciated',\n", + " 'furthering',\n", + " 'outstandingly',\n", + " 'selecting',\n", + " 'gees',\n", + " 'vacuousness',\n", + " 'loggins',\n", + " 'unconcious',\n", + " 'placings',\n", + " 'bdus',\n", + " 'invierno',\n", + " 'staging',\n", + " 'walcott',\n", + " 'bismillahhirrahmannirrahim',\n", + " 'falsehood',\n", + " 'fidelity',\n", + " 'gritter',\n", + " 'jacuzzi',\n", + " 'feardotcom',\n", + " 'sodomizing',\n", + " 'strenghths',\n", + " 'crocodile',\n", + " 'otherworldly',\n", + " 'wets',\n", + " 'springs',\n", + " 'klan',\n", + " 'sloppish',\n", + " 'mycenaean',\n", + " 'dunsmore',\n", + " 'bookends',\n", + " 'clarifying',\n", + " 'mina',\n", + " 'simultaneously',\n", + " 'canibalising',\n", + " 'junkies',\n", + " 'communinity',\n", + " 'reinventing',\n", + " 'ladybug',\n", + " 'gone',\n", + " 'disgracing',\n", + " 'holly',\n", + " 'unfindable',\n", + " 'cregar',\n", + " 'shameometer',\n", + " 'hiring',\n", + " 'ipcress',\n", + " 'preconceptions',\n", + " 'delightful',\n", + " 'ebano',\n", + " 'freezes',\n", + " 'maturely',\n", + " 'spritely',\n", + " 'surya',\n", + " 'creates',\n", + " 'cavett',\n", + " 'friedmans',\n", + " 'mcgovern',\n", + " 'graciousness',\n", + " 'cosell',\n", + " 'godawfull',\n", + " 'cludia',\n", + " 'timelines',\n", + " 'bounders',\n", + " 'twists',\n", + " 'considerable',\n", + " 'strange',\n", + " 'subjection',\n", + " 'padded',\n", + " 'veden',\n", + " 'amplifies',\n", + " 'tooling',\n", + " 'pristine',\n", + " 'jurgen',\n", + " 'manu',\n", + " 'rino',\n", + " 'preemptively',\n", + " 'rikki',\n", + " 'backrounds',\n", + " 'irreverent',\n", + " 'backdropped',\n", + " 'alotta',\n", + " 'schlub',\n", + " 'dratch',\n", + " 'gaped',\n", + " 'gracefully',\n", + " 'carley',\n", + " 'jami',\n", + " 'globalizing',\n", + " 'mouthings',\n", + " 'gb',\n", + " 'treasured',\n", + " 'fredo',\n", + " 'cugat',\n", + " 'ij',\n", + " 'headlights',\n", + " 'sculpts',\n", + " 'frokost',\n", + " 'plan',\n", + " 'efficiency',\n", + " 'blanzee',\n", + " 'massively',\n", + " 'veoh',\n", + " 'teleporting',\n", + " 'angers',\n", + " 'corollary',\n", + " 'srbljanovic',\n", + " 'rassendyll',\n", + " 'rely',\n", + " 'diamantino',\n", + " 'commentaries',\n", + " 'bin',\n", + " 'practicable',\n", + " 'adventure',\n", + " 'ukranian',\n", + " 'blames',\n", + " 'swiped',\n", + " 'gelatin',\n", + " 'paynes',\n", + " 'jbl',\n", + " 'zang',\n", + " 'whotta',\n", + " 'lizitis',\n", + " 'bianco',\n", + " 'duplicated',\n", + " 'focussing',\n", + " 'gissing',\n", + " 'struggled',\n", + " 'expositive',\n", + " 'blimps',\n", + " 'aquilae',\n", + " 'presnell',\n", + " 'massenet',\n", + " 'nonsenses',\n", + " 'syncer',\n", + " 'percolated',\n", + " 'algernon',\n", + " 'wainwright',\n", + " 'chatty',\n", + " 'hytner',\n", + " 'shh',\n", + " 'obv',\n", + " 'settlers',\n", + " 'dec',\n", + " 'parabens',\n", + " 'candolis',\n", + " 'controller',\n", + " 'bosch',\n", + " 'dw',\n", + " 'saleem',\n", + " 'electric',\n", + " 'baaaaaaaaaaad',\n", + " 'jalees',\n", + " 'nomolos',\n", + " 'inequities',\n", + " 'prophet',\n", + " 'ffwd',\n", + " 'penitently',\n", + " 'copulation',\n", + " 'betrothal',\n", + " 'gojira',\n", + " 'revolver',\n", + " 'sawant',\n", + " 'bromell',\n", + " 'burnstyn',\n", + " 'tess',\n", + " 'underwater',\n", + " 'spenny',\n", + " 'tomreynolds',\n", + " 'performed',\n", + " 'cited',\n", + " 'approachable',\n", + " 'veronika',\n", + " 'binev',\n", + " 'valientes',\n", + " 'deducts',\n", + " 'florrette',\n", + " 'elena',\n", + " 'oless',\n", + " 'distributers',\n", + " 'resetting',\n", + " 'characterize',\n", + " 'knicker',\n", + " 'ramadan',\n", + " 'goldie',\n", + " 'con',\n", + " 'galland',\n", + " 'chakraborty',\n", + " 'popeil',\n", + " 'relieve',\n", + " 'boulevards',\n", + " 'mistook',\n", + " 'testi',\n", + " 'ashe',\n", + " 'exploitative',\n", + " 'gadgets',\n", + " 'introspection',\n", + " 'staller',\n", + " 'hometown',\n", + " 'glowed',\n", + " 'unflatteringly',\n", + " 'goo',\n", + " 'trimmer',\n", + " 'danson',\n", + " 'overqualified',\n", + " 'fridge',\n", + " 'congrats',\n", + " 'supervisor',\n", + " 'evade',\n", + " 'intrepid',\n", + " 'badest',\n", + " 'posing',\n", + " 'freindship',\n", + " 'diario',\n", + " 'sewer',\n", + " 'kalasaki',\n", + " 'osaka',\n", + " 'confining',\n", + " 'hardy',\n", + " 'netlesk',\n", + " 'mandates',\n", + " 'wicked',\n", + " 'fours',\n", + " 'bragana',\n", + " 'streetlights',\n", + " 'founds',\n", + " 'mythology',\n", + " 'subscriber',\n", + " 'pussed',\n", + " 'fixx',\n", + " 'redfield',\n", + " 'optic',\n", + " 'sifting',\n", + " 'kindergartener',\n", + " 'ewell',\n", + " 'overreact',\n", + " 'fanfilm',\n", + " 'viking',\n", + " 'abashidze',\n", + " 'ahamd',\n", + " 'mediators',\n", + " 'sternness',\n", + " 'editor',\n", + " 'indiscreet',\n", + " 'stabbed',\n", + " 'explantation',\n", + " 'resold',\n", + " 'gye',\n", + " 'khiladiyon',\n", + " 'mere',\n", + " 'humour',\n", + " 'alton',\n", + " 'imo',\n", + " 'schwarzenbach',\n", + " 'exploited',\n", + " 'schtupping',\n", + " 'raphel',\n", + " 'stalls',\n", + " 'elegant',\n", + " 'bullshit',\n", + " 'excerpted',\n", + " 'footage',\n", + " 'blending',\n", + " 'dialectical',\n", + " 'sacco',\n", + " 'streamers',\n", + " 'telekinetic',\n", + " 'midkoff',\n", + " 'hugh',\n", + " 'warlords',\n", + " 'manckiewitz',\n", + " 'reversals',\n", + " 'danvers',\n", + " 'jackasses',\n", + " 'arshia',\n", + " 'overlookable',\n", + " 'habit',\n", + " 'manure',\n", + " 'honestly',\n", + " 'vanhook',\n", + " 'multiplex',\n", + " 'saxophone',\n", + " 'dreamless',\n", + " 'pintos',\n", + " 'sab',\n", + " 'fllm',\n", + " 'cable',\n", + " 'irritate',\n", + " 'deconstructing',\n", + " 'glued',\n", + " 'nuyorican',\n", + " 'caparzo',\n", + " 'huffman',\n", + " 'invoking',\n", + " 'ermlaughs',\n", + " 'shots',\n", + " 'faxes',\n", + " 'imaginings',\n", + " ...}" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TODO: Create set named \"vocab\" containing all of the words from all of the reviews\n", + "vocab = set(total_counts.keys())\n", + "vocab" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to check your vocabulary size. If everything worked correctly, it should print **74074**" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "74074\n" + ] + } + ], + "source": [ + "vocab_size = len(vocab)\n", + "print(vocab_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Take a look at the following image. It represents the layers of the neural network you'll be building throughout this notebook. `layer_0` is the input layer, `layer_1` is a hidden layer, and `layer_2` is the output layer." + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='sentiment_network_2.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**TODO:** Create a numpy array called `layer_0` and initialize it to all zeros. You will find the [zeros](https://docs.scipy.org/doc/numpy/reference/generated/numpy.zeros.html) function particularly helpful here. Be sure you create `layer_0` as a 2-dimensional matrix with 1 row and `vocab_size` columns. " + ] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0., 0., 0., ..., 0., 0., 0.]])" + ] + }, + "execution_count": 209, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# TODO: Create layer_0 matrix with dimensions 1 by vocab_size, initially filled with zeros\n", + "layer_0 = np.zeros((1,vocab_size))\n", + "layer_0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell. It should display `(1, 74074)`" + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 74074)" + ] + }, + "execution_count": 210, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "layer_0.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 211, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='sentiment_network.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`layer_0` contains one entry for every word in the vocabulary, as shown in the above image. We need to make sure we know the index of each word, so run the following cell to create a lookup table that stores the index of every word." + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'': 0,\n", + " 'innocuously': 1,\n", + " 'uav': 2,\n", + " 'perscription': 3,\n", + " 'enforcers': 4,\n", + " 'notification': 5,\n", + " 'karsis': 6,\n", + " 'ruban': 7,\n", + " 'envy': 8,\n", + " 'huggaland': 9,\n", + " 'persbrandt': 10,\n", + " 'verboten': 11,\n", + " 'organizes': 12,\n", + " 'graaff': 13,\n", + " 'murkwood': 14,\n", + " 'fenech': 15,\n", + " 'interrogations': 16,\n", + " 'lothlorien': 17,\n", + " 'responders': 18,\n", + " 'burned': 19,\n", + " 'aired': 20,\n", + " 'nirupa': 21,\n", + " 'inaugurate': 22,\n", + " 'nicktoons': 23,\n", + " 'shrunken': 24,\n", + " 'bauerisch': 25,\n", + " 'adjective': 26,\n", + " 'campground': 27,\n", + " 'visitations': 28,\n", + " 'artery': 29,\n", + " 'noone': 30,\n", + " 'stimulates': 31,\n", + " 'huggie': 32,\n", + " 'benji': 33,\n", + " 'biochemical': 34,\n", + " 'unveiling': 35,\n", + " 'chalant': 36,\n", + " 'pastoral': 37,\n", + " 'pittors': 38,\n", + " 'valga': 39,\n", + " 'nooooo': 40,\n", + " 'brinda': 41,\n", + " 'competent': 42,\n", + " 'halifax': 43,\n", + " 'etiquette': 44,\n", + " 'bearded': 45,\n", + " 'ewen': 46,\n", + " 'slit': 47,\n", + " 'cero': 48,\n", + " 'waverly': 49,\n", + " 'jullian': 50,\n", + " 'hesitating': 51,\n", + " 'squirmy': 52,\n", + " 'extrapolating': 53,\n", + " 'uncanonical': 54,\n", + " 'habitually': 55,\n", + " 'unloving': 56,\n", + " 'mccallum': 57,\n", + " 'yearnings': 58,\n", + " 'beter': 59,\n", + " 'hongurai': 60,\n", + " 'westcourt': 61,\n", + " 'nablus': 62,\n", + " 'cannell': 63,\n", + " 'ahmadinejad': 64,\n", + " 'titillatingly': 65,\n", + " 'combined': 66,\n", + " 'virgina': 67,\n", + " 'thieson': 68,\n", + " 'puling': 69,\n", + " 'mcgovernisms': 70,\n", + " 'independant': 71,\n", + " 'gusher': 72,\n", + " 'rigomortis': 73,\n", + " 'guerriri': 74,\n", + " 'almoust': 75,\n", + " 'landscaper': 76,\n", + " 'cladon': 77,\n", + " 'fantafestival': 78,\n", + " 'sneha': 79,\n", + " 'spitted': 80,\n", + " 'warhol': 81,\n", + " 'coworkers': 82,\n", + " 'photographic': 83,\n", + " 'nesher': 84,\n", + " 'blended': 85,\n", + " 'pandemonium': 86,\n", + " 'bitch': 87,\n", + " 'grudging': 88,\n", + " 'mcdermott': 89,\n", + " 'clot': 90,\n", + " 'brattiness': 91,\n", + " 'pachyderms': 92,\n", + " 'vacations': 93,\n", + " 'deolali': 94,\n", + " 'viscous': 95,\n", + " 'shouldn': 96,\n", + " 'dearest': 97,\n", + " 'irak': 98,\n", + " 'arthur': 99,\n", + " 'poorer': 100,\n", + " 'outdid': 101,\n", + " 'surgeons': 102,\n", + " 'rambos': 103,\n", + " 'potente': 104,\n", + " 'enlighten': 105,\n", + " 'showering': 106,\n", + " 'quips': 107,\n", + " 'adorning': 108,\n", + " 'counts': 109,\n", + " 'glynnis': 110,\n", + " 'statue': 111,\n", + " 'kiel': 112,\n", + " 'unfailing': 113,\n", + " 'layman': 114,\n", + " 'miscommunication': 115,\n", + " 'gentil': 116,\n", + " 'brutalized': 117,\n", + " 'uncompromisingly': 118,\n", + " 'laughtracks': 119,\n", + " 'invigorated': 120,\n", + " 'ambivalence': 121,\n", + " 'densely': 122,\n", + " 'colorlessly': 123,\n", + " 'grandstand': 124,\n", + " 'seats': 125,\n", + " 'naoto': 126,\n", + " 'backwards': 127,\n", + " 'shita': 128,\n", + " 'kerr': 129,\n", + " 'cele': 130,\n", + " 'moomins': 131,\n", + " 'uncomfortableness': 132,\n", + " 'deserving': 133,\n", + " 'jrvet': 134,\n", + " 'arnim': 135,\n", + " 'meats': 136,\n", + " 'imaginably': 137,\n", + " 'coincidences': 138,\n", + " 'overpraise': 139,\n", + " 'balboa': 140,\n", + " 'upped': 141,\n", + " 'tiku': 142,\n", + " 'corbetts': 143,\n", + " 'alarik': 144,\n", + " 'fiancs': 145,\n", + " 'vengence': 146,\n", + " 'baruc': 147,\n", + " 'pavlovian': 148,\n", + " 'situationally': 149,\n", + " 'messenger': 150,\n", + " 'betterment': 151,\n", + " 'stomachs': 152,\n", + " 'exo': 153,\n", + " 'weirded': 154,\n", + " 'handed': 155,\n", + " 'consistently': 156,\n", + " 'gretel': 157,\n", + " 'phrasing': 158,\n", + " 'janning': 159,\n", + " 'presumable': 160,\n", + " 'burns': 161,\n", + " 'creepier': 162,\n", + " 'devoreaux': 163,\n", + " 'declaiming': 164,\n", + " 'homevideo': 165,\n", + " 'charlo': 166,\n", + " 'jardine': 167,\n", + " 'shagged': 168,\n", + " 'beta': 169,\n", + " 'misjudges': 170,\n", + " 'malta': 171,\n", + " 'polution': 172,\n", + " 'albans': 173,\n", + " 'grifters': 174,\n", + " 'acct': 175,\n", + " 'iisimba': 176,\n", + " 'gargantua': 177,\n", + " 'collera': 178,\n", + " 'flavorful': 179,\n", + " 'neutrality': 180,\n", + " 'zack': 181,\n", + " 'furthest': 182,\n", + " 'audience': 183,\n", + " 'campiness': 184,\n", + " 'surfs': 185,\n", + " 'blinders': 186,\n", + " 'cocksure': 187,\n", + " 'sweatshops': 188,\n", + " 'muldaur': 189,\n", + " 'carter': 190,\n", + " 'eliminating': 191,\n", + " 'gorcey': 192,\n", + " 'scalisi': 193,\n", + " 'atan': 194,\n", + " 'breslin': 195,\n", + " 'profusely': 196,\n", + " 'departs': 197,\n", + " 'homemaker': 198,\n", + " 'fish': 199,\n", + " 'bigg': 200,\n", + " 'flagrant': 201,\n", + " 'commentating': 202,\n", + " 'cartoonishness': 203,\n", + " 'educational': 204,\n", + " 'tayback': 205,\n", + " 'atchison': 206,\n", + " 'mccullers': 207,\n", + " 'mikshelt': 208,\n", + " 'superbad': 209,\n", + " 'stroessner': 210,\n", + " 'moroder': 211,\n", + " 'univeral': 212,\n", + " 'sanitised': 213,\n", + " 'drafted': 214,\n", + " 'unchristian': 215,\n", + " 'sponsored': 216,\n", + " 'parter': 217,\n", + " 'shootings': 218,\n", + " 'damone': 219,\n", + " 'effie': 220,\n", + " 'sandbox': 221,\n", + " 'pratically': 222,\n", + " 'boyens': 223,\n", + " 'gina': 224,\n", + " 'stinkeye': 225,\n", + " 'queues': 226,\n", + " 'kwong': 227,\n", + " 'trips': 228,\n", + " 'european': 229,\n", + " 'giraudot': 230,\n", + " 'pendanski': 231,\n", + " 'moonbeast': 232,\n", + " 'defended': 233,\n", + " 'ridiculously': 234,\n", + " 'wrongs': 235,\n", + " 'bankol': 236,\n", + " 'linton': 237,\n", + " 'newscaster': 238,\n", + " 'released': 239,\n", + " 'screenplays': 240,\n", + " 'yoon': 241,\n", + " 'unshakably': 242,\n", + " 'andrienne': 243,\n", + " 'grindhouse': 244,\n", + " 'trogar': 245,\n", + " 'simonson': 246,\n", + " 'tenements': 247,\n", + " 'notre': 248,\n", + " 'unfortunates': 249,\n", + " 'lieving': 250,\n", + " 'illegal': 251,\n", + " 'relieving': 252,\n", + " 'unforgivably': 253,\n", + " 'moly': 254,\n", + " 'jocelyn': 255,\n", + " 'patterns': 256,\n", + " 'pimps': 257,\n", + " 'demolitions': 258,\n", + " 'frock': 259,\n", + " 'scaling': 260,\n", + " 'philippa': 261,\n", + " 'thunderhead': 262,\n", + " 'consist': 263,\n", + " 'bilardo': 264,\n", + " 'inverse': 265,\n", + " 'yakusyo': 266,\n", + " 'reinterpretation': 267,\n", + " 'pineyro': 268,\n", + " 'bevy': 269,\n", + " 'annis': 270,\n", + " 'remo': 271,\n", + " 'players': 272,\n", + " 'mercedes': 273,\n", + " 'glimmers': 274,\n", + " 'margaux': 275,\n", + " 'broadcast': 276,\n", + " 'almeria': 277,\n", + " 'waaaaaayyyy': 278,\n", + " 'vertiginous': 279,\n", + " 'steal': 280,\n", + " 'flashdance': 281,\n", + " 'frisch': 282,\n", + " 'bumpuses': 283,\n", + " 'focuses': 284,\n", + " 'caf': 285,\n", + " 'practicalities': 286,\n", + " 'grittier': 287,\n", + " 'roeh': 288,\n", + " 'rumbling': 289,\n", + " 'salo': 290,\n", + " 'assortment': 291,\n", + " 'unhinged': 292,\n", + " 'horsell': 293,\n", + " 'hadass': 294,\n", + " 'aquitane': 295,\n", + " 'whichlegend': 296,\n", + " 'withe': 297,\n", + " 'manitoba': 298,\n", + " 'misquotes': 299,\n", + " 'dynamite': 300,\n", + " 'representation': 301,\n", + " 'brooklyners': 302,\n", + " 'pflug': 303,\n", + " 'genetic': 304,\n", + " 'pres': 305,\n", + " 'halen': 306,\n", + " 'passersby': 307,\n", + " 'tropes': 308,\n", + " 'solemnity': 309,\n", + " 'girdle': 310,\n", + " 'internationalist': 311,\n", + " 'giger': 312,\n", + " 'tidied': 313,\n", + " 'imprimatur': 314,\n", + " 'supersadlysoftie': 315,\n", + " 'saltwater': 316,\n", + " 'anspach': 317,\n", + " 'vigilantism': 318,\n", + " 'mantraps': 319,\n", + " 'lousier': 320,\n", + " 'susanne': 321,\n", + " 'exectioner': 322,\n", + " 'stalinism': 323,\n", + " 'foxs': 324,\n", + " 'obtrusively': 325,\n", + " 'costard': 326,\n", + " 'neatly': 327,\n", + " 'quake': 328,\n", + " 'irs': 329,\n", + " 'logistical': 330,\n", + " 'fingerprinting': 331,\n", + " 'wow': 332,\n", + " 'pretagonist': 333,\n", + " 'quests': 334,\n", + " 'fruits': 335,\n", + " 'truckstops': 336,\n", + " 'uninfected': 337,\n", + " 'bruneau': 338,\n", + " 'wong': 339,\n", + " 'yells': 340,\n", + " 'jianxiang': 341,\n", + " 'hertzfeldt': 342,\n", + " 'degenerating': 343,\n", + " 'yeh': 344,\n", + " 'bandmates': 345,\n", + " 'inmho': 346,\n", + " 'happend': 347,\n", + " 'meetings': 348,\n", + " 'distanced': 349,\n", + " 'smirk': 350,\n", + " 'jour': 351,\n", + " 'manifestly': 352,\n", + " 'strings': 353,\n", + " 'milwall': 354,\n", + " 'dappled': 355,\n", + " 'subtlely': 356,\n", + " 'autre': 357,\n", + " 'marushka': 358,\n", + " 'rubell': 359,\n", + " 'untastful': 360,\n", + " 'durn': 361,\n", + " 'prehysteria': 362,\n", + " 'rappel': 363,\n", + " 'traynor': 364,\n", + " 'phenoms': 365,\n", + " 'scored': 366,\n", + " 'boards': 367,\n", + " 'intermixed': 368,\n", + " 'devistation': 369,\n", + " 'wachtang': 370,\n", + " 'diazes': 371,\n", + " 'acre': 372,\n", + " 'leered': 373,\n", + " 'darkest': 374,\n", + " 'ruler': 375,\n", + " 'latterday': 376,\n", + " 'david': 377,\n", + " 'aforesaid': 378,\n", + " 'ichii': 379,\n", + " 'ratt': 380,\n", + " 'firmly': 381,\n", + " 'disslikes': 382,\n", + " 'distancing': 383,\n", + " 'betting': 384,\n", + " 'marcia': 385,\n", + " 'ketchim': 386,\n", + " 'emit': 387,\n", + " 'assery': 388,\n", + " 'symbolising': 389,\n", + " 'selznick': 390,\n", + " 'meridian': 391,\n", + " 'whippet': 392,\n", + " 'audry': 393,\n", + " 'arcs': 394,\n", + " 'strongest': 395,\n", + " 'gwyne': 396,\n", + " 'joanie': 397,\n", + " 'leggage': 398,\n", + " 'cuervo': 399,\n", + " 'servo': 400,\n", + " 'website': 401,\n", + " 'hurls': 402,\n", + " 'unmindful': 403,\n", + " 'stupidities': 404,\n", + " 'bernhards': 405,\n", + " 'tustin': 406,\n", + " 'pquerette': 407,\n", + " 'podunksville': 408,\n", + " 'sincronicity': 409,\n", + " 'oh': 410,\n", + " 'masseratti': 411,\n", + " 'dragoncon': 412,\n", + " 'intermarriage': 413,\n", + " 'hat': 414,\n", + " 'entwistle': 415,\n", + " 'sisson': 416,\n", + " 'underprivileged': 417,\n", + " 'eerie': 418,\n", + " 'nameless': 419,\n", + " 'adotped': 420,\n", + " 'pised': 421,\n", + " 'bromidic': 422,\n", + " 'marker': 423,\n", + " 'aldiss': 424,\n", + " 'beo': 425,\n", + " 'burgerlers': 426,\n", + " 'sloppier': 427,\n", + " 'kenobi': 428,\n", + " 'klieg': 429,\n", + " 'nang': 430,\n", + " 'chemotherapy': 431,\n", + " 'vette': 432,\n", + " 'refugee': 433,\n", + " 'circumcision': 434,\n", + " 'aaaaah': 435,\n", + " 'days': 436,\n", + " 'charasmatic': 437,\n", + " 'helena': 438,\n", + " 'avenues': 439,\n", + " 'knuckled': 440,\n", + " 'my': 441,\n", + " 'belied': 442,\n", + " 'anglican': 443,\n", + " 'davison': 444,\n", + " 'suspenseful': 445,\n", + " 'weberian': 446,\n", + " 'swilling': 447,\n", + " 'mst': 448,\n", + " 'ballad': 449,\n", + " 'vase': 450,\n", + " 'anansie': 451,\n", + " 'tatum': 452,\n", + " 'kirschner': 453,\n", + " 'werewolve': 454,\n", + " 'conjoined': 455,\n", + " 'quietness': 456,\n", + " 'sinner': 457,\n", + " 'spoilerphobic': 458,\n", + " 'dishy': 459,\n", + " 'calibrated': 460,\n", + " 'cinephilia': 461,\n", + " 'fantastically': 462,\n", + " 'jayce': 463,\n", + " 'sentient': 464,\n", + " 'charlesmanson': 465,\n", + " 'deflector': 466,\n", + " 'shazza': 467,\n", + " 'pities': 468,\n", + " 'tlahuac': 469,\n", + " 'tashed': 470,\n", + " 'shades': 471,\n", + " 'abhisheh': 472,\n", + " 'faramir': 473,\n", + " 'sala': 474,\n", + " 'splinters': 475,\n", + " 'toxicology': 476,\n", + " 'lightfoot': 477,\n", + " 'opfergang': 478,\n", + " 'graboids': 479,\n", + " 'serafinowicz': 480,\n", + " 'lgbt': 481,\n", + " 'anorexic': 482,\n", + " 'jaqui': 483,\n", + " 'juscar': 484,\n", + " 'fries': 485,\n", + " 'poseiden': 486,\n", + " 'babbitt': 487,\n", + " 'clamps': 488,\n", + " 'burrows': 489,\n", + " 'depravation': 490,\n", + " 'wesa': 491,\n", + " 'levin': 492,\n", + " 'tibbett': 493,\n", + " 'reiser': 494,\n", + " 'reba': 495,\n", + " 'swte': 496,\n", + " 'diddy': 497,\n", + " 'hanley': 498,\n", + " 'unexpectedness': 499,\n", + " 'stadiums': 500,\n", + " 'chuckie': 501,\n", + " 'rutting': 502,\n", + " 'crossed': 503,\n", + " 'jermaine': 504,\n", + " 'accordingly': 505,\n", + " 'donations': 506,\n", + " 'landlady': 507,\n", + " 'scots': 508,\n", + " 'boning': 509,\n", + " 'jailhouse': 510,\n", + " 'voce': 511,\n", + " 'alarm': 512,\n", + " 'platfrom': 513,\n", + " 'objectors': 514,\n", + " 'salomaa': 515,\n", + " 'barrio': 516,\n", + " 'clergymen': 517,\n", + " 'intangibility': 518,\n", + " 'vannoordlet': 519,\n", + " 'insectoids': 520,\n", + " 'dissing': 521,\n", + " 'horlicks': 522,\n", + " 'idylls': 523,\n", + " 'ludicrously': 524,\n", + " 'briggitta': 525,\n", + " 'dolman': 526,\n", + " 'joe': 527,\n", + " 'standardized': 528,\n", + " 'impresses': 529,\n", + " 'raju': 530,\n", + " 'ladys': 531,\n", + " 'grard': 532,\n", + " 'sapping': 533,\n", + " 'nibelungs': 534,\n", + " 'freckles': 535,\n", + " 'snog': 536,\n", + " 'redefine': 537,\n", + " 'fiscal': 538,\n", + " 'disappearance': 539,\n", + " 'civilizing': 540,\n", + " 'megaphone': 541,\n", + " 'acquired': 542,\n", + " 'expedient': 543,\n", + " 'generalities': 544,\n", + " 'bennet': 545,\n", + " 'joyrides': 546,\n", + " 'enguled': 547,\n", + " 'pallio': 548,\n", + " 'irrelevancy': 549,\n", + " 'corrected': 550,\n", + " 'sparsely': 551,\n", + " 'pushers': 552,\n", + " 'amicable': 553,\n", + " 'reveres': 554,\n", + " 'sodomised': 555,\n", + " 'simulate': 556,\n", + " 'tribeswomen': 557,\n", + " 'enacts': 558,\n", + " 'ennia': 559,\n", + " 'sorbonne': 560,\n", + " 'kilpatrick': 561,\n", + " 'bleaching': 562,\n", + " 'curt': 563,\n", + " 'mutate': 564,\n", + " 'shipwrecked': 565,\n", + " 'mei': 566,\n", + " 'bootleggers': 567,\n", + " 'fumblingly': 568,\n", + " 'signifiers': 569,\n", + " 'hellbent': 570,\n", + " 'mxyzptlk': 571,\n", + " 'segun': 572,\n", + " 'inane': 573,\n", + " 'franics': 574,\n", + " 'arc': 575,\n", + " 'dreaful': 576,\n", + " 'sultan': 577,\n", + " 'procedure': 578,\n", + " 'pier': 579,\n", + " 'clinker': 580,\n", + " 'laziest': 581,\n", + " 'ise': 582,\n", + " 'fkin': 583,\n", + " 'probe': 584,\n", + " 'blathered': 585,\n", + " 'raton': 586,\n", + " 'reprint': 587,\n", + " 'memorization': 588,\n", + " 'amick': 589,\n", + " 'elope': 590,\n", + " 'lando': 591,\n", + " 'grossed': 592,\n", + " 'ravindra': 593,\n", + " 'consigned': 594,\n", + " 'rapp': 595,\n", + " 'misfocused': 596,\n", + " 'strano': 597,\n", + " 'vitae': 598,\n", + " 'booie': 599,\n", + " 'pushed': 600,\n", + " 'chafed': 601,\n", + " 'seaward': 602,\n", + " 'motherland': 603,\n", + " 'anjane': 604,\n", + " 'beautifule': 605,\n", + " 'cottages': 606,\n", + " 'predicate': 607,\n", + " 'rewatching': 608,\n", + " 'slob': 609,\n", + " 'spaces': 610,\n", + " 'springboard': 611,\n", + " 'irving': 612,\n", + " 'crushes': 613,\n", + " 'disassemble': 614,\n", + " 'bendrix': 615,\n", + " 'brogado': 616,\n", + " 'guest': 617,\n", + " 'mars': 618,\n", + " 'blaise': 619,\n", + " 'heists': 620,\n", + " 'thc': 621,\n", + " 'objectivity': 622,\n", + " 'bird': 623,\n", + " 'guards': 624,\n", + " 'frogs': 625,\n", + " 'stackhouse': 626,\n", + " 'camerons': 627,\n", + " 'apaches': 628,\n", + " 'cover': 629,\n", + " 'novelizations': 630,\n", + " 'tah': 631,\n", + " 'garam': 632,\n", + " 'ranna': 633,\n", + " 'snappy': 634,\n", + " 'ain': 635,\n", + " 'unscheduled': 636,\n", + " 'steinbeck': 637,\n", + " 'nonintentional': 638,\n", + " 'kaspar': 639,\n", + " 'nrw': 640,\n", + " 'skinned': 641,\n", + " 'obsolescence': 642,\n", + " 'hemel': 643,\n", + " 'acception': 644,\n", + " 'comteg': 645,\n", + " 'guarantee': 646,\n", + " 'unnerve': 647,\n", + " 'posa': 648,\n", + " 'parekh': 649,\n", + " 'fifi': 650,\n", + " 'duquenne': 651,\n", + " 'bearand': 652,\n", + " 'coincidentially': 653,\n", + " 'recurrent': 654,\n", + " 'sven': 655,\n", + " 'hen': 656,\n", + " 'obsurdly': 657,\n", + " 'corners': 658,\n", + " 'humiliation': 659,\n", + " 'homeland': 660,\n", + " 'everage': 661,\n", + " 'sly': 662,\n", + " 'synopses': 663,\n", + " 'kaal': 664,\n", + " 'amores': 665,\n", + " 'terminals': 666,\n", + " 'swam': 667,\n", + " 'feiss': 668,\n", + " 'prideful': 669,\n", + " 'confucian': 670,\n", + " 'historical': 671,\n", + " 'vicissitude': 672,\n", + " 'dominators': 673,\n", + " 'xizhao': 674,\n", + " 'ekland': 675,\n", + " 'wufei': 676,\n", + " 'callan': 677,\n", + " 'twink': 678,\n", + " 'manoven': 679,\n", + " 'andreas': 680,\n", + " 'heap': 681,\n", + " 'transmit': 682,\n", + " 'grumpiest': 683,\n", + " 'masturbated': 684,\n", + " 'updating': 685,\n", + " 'amend': 686,\n", + " 'dupia': 687,\n", + " 'landslide': 688,\n", + " 'deedlit': 689,\n", + " 'lightoller': 690,\n", + " 'buxomed': 691,\n", + " 'snowmonton': 692,\n", + " 'inskip': 693,\n", + " 'peter': 694,\n", + " 'generals': 695,\n", + " 'appointment': 696,\n", + " 'lathan': 697,\n", + " 'dixen': 698,\n", + " 'lemons': 699,\n", + " 'brilliantly': 700,\n", + " 'scarfing': 701,\n", + " 'svankmajer': 702,\n", + " 'enunciated': 703,\n", + " 'furthering': 704,\n", + " 'outstandingly': 705,\n", + " 'selecting': 706,\n", + " 'gees': 707,\n", + " 'vacuousness': 708,\n", + " 'loggins': 709,\n", + " 'unconcious': 710,\n", + " 'placings': 711,\n", + " 'bdus': 712,\n", + " 'invierno': 713,\n", + " 'staging': 714,\n", + " 'walcott': 715,\n", + " 'bismillahhirrahmannirrahim': 716,\n", + " 'falsehood': 717,\n", + " 'fidelity': 718,\n", + " 'gritter': 719,\n", + " 'jacuzzi': 720,\n", + " 'feardotcom': 721,\n", + " 'sodomizing': 722,\n", + " 'strenghths': 723,\n", + " 'crocodile': 724,\n", + " 'otherworldly': 725,\n", + " 'wets': 726,\n", + " 'springs': 727,\n", + " 'klan': 728,\n", + " 'sloppish': 729,\n", + " 'mycenaean': 730,\n", + " 'dunsmore': 731,\n", + " 'bookends': 732,\n", + " 'clarifying': 733,\n", + " 'mina': 734,\n", + " 'simultaneously': 735,\n", + " 'canibalising': 736,\n", + " 'junkies': 737,\n", + " 'communinity': 738,\n", + " 'reinventing': 739,\n", + " 'ladybug': 740,\n", + " 'gone': 741,\n", + " 'disgracing': 742,\n", + " 'holly': 743,\n", + " 'unfindable': 744,\n", + " 'cregar': 745,\n", + " 'shameometer': 746,\n", + " 'hiring': 747,\n", + " 'ipcress': 748,\n", + " 'preconceptions': 749,\n", + " 'delightful': 750,\n", + " 'ebano': 751,\n", + " 'freezes': 752,\n", + " 'maturely': 753,\n", + " 'spritely': 754,\n", + " 'surya': 755,\n", + " 'creates': 756,\n", + " 'cavett': 757,\n", + " 'friedmans': 758,\n", + " 'mcgovern': 759,\n", + " 'graciousness': 760,\n", + " 'cosell': 761,\n", + " 'godawfull': 762,\n", + " 'cludia': 763,\n", + " 'timelines': 764,\n", + " 'bounders': 765,\n", + " 'twists': 766,\n", + " 'considerable': 767,\n", + " 'strange': 768,\n", + " 'subjection': 769,\n", + " 'padded': 770,\n", + " 'veden': 771,\n", + " 'amplifies': 772,\n", + " 'tooling': 773,\n", + " 'pristine': 774,\n", + " 'jurgen': 775,\n", + " 'manu': 776,\n", + " 'rino': 777,\n", + " 'preemptively': 778,\n", + " 'rikki': 779,\n", + " 'backrounds': 780,\n", + " 'irreverent': 781,\n", + " 'backdropped': 782,\n", + " 'alotta': 783,\n", + " 'schlub': 784,\n", + " 'dratch': 785,\n", + " 'gaped': 786,\n", + " 'gracefully': 787,\n", + " 'carley': 788,\n", + " 'jami': 789,\n", + " 'globalizing': 790,\n", + " 'mouthings': 791,\n", + " 'gb': 792,\n", + " 'treasured': 793,\n", + " 'fredo': 794,\n", + " 'cugat': 795,\n", + " 'ij': 796,\n", + " 'headlights': 797,\n", + " 'sculpts': 798,\n", + " 'frokost': 799,\n", + " 'plan': 800,\n", + " 'efficiency': 801,\n", + " 'blanzee': 802,\n", + " 'massively': 803,\n", + " 'veoh': 804,\n", + " 'teleporting': 805,\n", + " 'angers': 806,\n", + " 'corollary': 807,\n", + " 'srbljanovic': 808,\n", + " 'rassendyll': 809,\n", + " 'rely': 810,\n", + " 'diamantino': 811,\n", + " 'commentaries': 812,\n", + " 'bin': 813,\n", + " 'practicable': 814,\n", + " 'adventure': 815,\n", + " 'ukranian': 816,\n", + " 'blames': 817,\n", + " 'swiped': 818,\n", + " 'gelatin': 819,\n", + " 'paynes': 820,\n", + " 'jbl': 821,\n", + " 'zang': 822,\n", + " 'whotta': 823,\n", + " 'lizitis': 824,\n", + " 'bianco': 825,\n", + " 'duplicated': 826,\n", + " 'focussing': 827,\n", + " 'gissing': 828,\n", + " 'struggled': 829,\n", + " 'expositive': 830,\n", + " 'blimps': 831,\n", + " 'aquilae': 832,\n", + " 'presnell': 833,\n", + " 'massenet': 834,\n", + " 'nonsenses': 835,\n", + " 'syncer': 836,\n", + " 'percolated': 837,\n", + " 'algernon': 838,\n", + " 'wainwright': 839,\n", + " 'chatty': 840,\n", + " 'hytner': 841,\n", + " 'shh': 842,\n", + " 'obv': 843,\n", + " 'settlers': 844,\n", + " 'dec': 845,\n", + " 'parabens': 846,\n", + " 'candolis': 847,\n", + " 'controller': 848,\n", + " 'bosch': 849,\n", + " 'dw': 850,\n", + " 'saleem': 851,\n", + " 'electric': 852,\n", + " 'baaaaaaaaaaad': 853,\n", + " 'jalees': 854,\n", + " 'nomolos': 855,\n", + " 'inequities': 856,\n", + " 'prophet': 857,\n", + " 'ffwd': 858,\n", + " 'penitently': 859,\n", + " 'copulation': 860,\n", + " 'betrothal': 861,\n", + " 'gojira': 862,\n", + " 'revolver': 863,\n", + " 'sawant': 864,\n", + " 'bromell': 865,\n", + " 'burnstyn': 866,\n", + " 'tess': 867,\n", + " 'underwater': 868,\n", + " 'spenny': 869,\n", + " 'tomreynolds': 870,\n", + " 'performed': 871,\n", + " 'cited': 872,\n", + " 'approachable': 873,\n", + " 'veronika': 874,\n", + " 'binev': 875,\n", + " 'valientes': 876,\n", + " 'deducts': 877,\n", + " 'florrette': 878,\n", + " 'elena': 879,\n", + " 'oless': 880,\n", + " 'distributers': 881,\n", + " 'resetting': 882,\n", + " 'characterize': 883,\n", + " 'knicker': 884,\n", + " 'ramadan': 885,\n", + " 'goldie': 886,\n", + " 'con': 887,\n", + " 'galland': 888,\n", + " 'chakraborty': 889,\n", + " 'popeil': 890,\n", + " 'relieve': 891,\n", + " 'boulevards': 892,\n", + " 'mistook': 893,\n", + " 'testi': 894,\n", + " 'ashe': 895,\n", + " 'exploitative': 896,\n", + " 'gadgets': 897,\n", + " 'introspection': 898,\n", + " 'staller': 899,\n", + " 'hometown': 900,\n", + " 'glowed': 901,\n", + " 'unflatteringly': 902,\n", + " 'goo': 903,\n", + " 'trimmer': 904,\n", + " 'danson': 905,\n", + " 'overqualified': 906,\n", + " 'fridge': 907,\n", + " 'congrats': 908,\n", + " 'supervisor': 909,\n", + " 'evade': 910,\n", + " 'intrepid': 911,\n", + " 'badest': 912,\n", + " 'posing': 913,\n", + " 'freindship': 914,\n", + " 'diario': 915,\n", + " 'sewer': 916,\n", + " 'kalasaki': 917,\n", + " 'osaka': 918,\n", + " 'confining': 919,\n", + " 'hardy': 920,\n", + " 'netlesk': 921,\n", + " 'mandates': 922,\n", + " 'wicked': 923,\n", + " 'fours': 924,\n", + " 'bragana': 925,\n", + " 'streetlights': 926,\n", + " 'founds': 927,\n", + " 'mythology': 928,\n", + " 'subscriber': 929,\n", + " 'pussed': 930,\n", + " 'fixx': 931,\n", + " 'redfield': 932,\n", + " 'optic': 933,\n", + " 'sifting': 934,\n", + " 'kindergartener': 935,\n", + " 'ewell': 936,\n", + " 'overreact': 937,\n", + " 'fanfilm': 938,\n", + " 'viking': 939,\n", + " 'abashidze': 940,\n", + " 'ahamd': 941,\n", + " 'mediators': 942,\n", + " 'sternness': 943,\n", + " 'editor': 944,\n", + " 'indiscreet': 945,\n", + " 'stabbed': 946,\n", + " 'explantation': 947,\n", + " 'resold': 948,\n", + " 'gye': 949,\n", + " 'khiladiyon': 950,\n", + " 'mere': 951,\n", + " 'humour': 952,\n", + " 'alton': 953,\n", + " 'imo': 954,\n", + " 'schwarzenbach': 955,\n", + " 'exploited': 956,\n", + " 'schtupping': 957,\n", + " 'raphel': 958,\n", + " 'stalls': 959,\n", + " 'elegant': 960,\n", + " 'bullshit': 961,\n", + " 'excerpted': 962,\n", + " 'footage': 963,\n", + " 'blending': 964,\n", + " 'dialectical': 965,\n", + " 'sacco': 966,\n", + " 'streamers': 967,\n", + " 'telekinetic': 968,\n", + " 'midkoff': 969,\n", + " 'hugh': 970,\n", + " 'warlords': 971,\n", + " 'manckiewitz': 972,\n", + " 'reversals': 973,\n", + " 'danvers': 974,\n", + " 'jackasses': 975,\n", + " 'arshia': 976,\n", + " 'overlookable': 977,\n", + " 'habit': 978,\n", + " 'manure': 979,\n", + " 'honestly': 980,\n", + " 'vanhook': 981,\n", + " 'multiplex': 982,\n", + " 'saxophone': 983,\n", + " 'dreamless': 984,\n", + " 'pintos': 985,\n", + " 'sab': 986,\n", + " 'fllm': 987,\n", + " 'cable': 988,\n", + " 'irritate': 989,\n", + " 'deconstructing': 990,\n", + " 'glued': 991,\n", + " 'nuyorican': 992,\n", + " 'caparzo': 993,\n", + " 'huffman': 994,\n", + " 'invoking': 995,\n", + " 'ermlaughs': 996,\n", + " 'shots': 997,\n", + " 'faxes': 998,\n", + " 'imaginings': 999,\n", + " ...}" + ] + }, + "execution_count": 212, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Create a dictionary of words in the vocabulary mapped to index positions\n", + "# (to be used in layer_0)\n", + "word2index = {}\n", + "for i,word in enumerate(vocab): # Enumerate adds counter to an iterable and returns it in form of enumerate object\n", + " word2index[word] = i\n", + " \n", + "# display the map of words to indices\n", + "word2index" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**TODO:** Complete the implementation of `update_input_layer`. It should count \n", + " how many times each word is used in the given review, and then store\n", + " those counts at the appropriate indices inside `layer_0`." + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [], + "source": [ + "def update_input_layer(review):\n", + " \"\"\" Modify the global layer_0 to represent the vector form of review.\n", + " The element at a given index of layer_0 should represent\n", + " how many times the given word occurs in the review.\n", + " Args:\n", + " review(string) - the string of the review\n", + " Returns:\n", + " None\n", + " \"\"\"\n", + " \n", + " global layer_0\n", + " \n", + " # clear out previous state, reset the layer to be all 0s\n", + " layer_0 *= 0\n", + " \n", + " # count how many times each word is used in the given review and store the results in layer_0 \n", + " \n", + " try:\n", + " for word in review.split(\" \"):\n", + " layer_0[0][word2index[word]] += 1\n", + " except KeyError:\n", + " print(KeyError)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to test updating the input layer with the first review. The indices assigned may not be the same as in the solution, but hopefully you'll see some non-zero values in `layer_0`. " + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[40., 0., 0., ..., 0., 0., 0.]])" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "update_input_layer(reviews[2875])\n", + "layer_0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**TODO:** Complete the implementation of `get_target_for_labels`. It should return `0` or `1`, \n", + " depending on whether the given label is `NEGATIVE` or `POSITIVE`, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [], + "source": [ + "def get_target_for_label(label):\n", + " \"\"\"Convert a label to `0` or `1`.\n", + " Args:\n", + " label(string) - Either \"POSITIVE\" or \"NEGATIVE\".\n", + " Returns:\n", + " `0` or `1`.\n", + " \"\"\"\n", + " if label == 'POSITIVE':\n", + " return 1\n", + " else:\n", + " return 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following two cells. They should print out`'POSITIVE'` and `1`, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'POSITIVE'" + ] + }, + "execution_count": 216, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_target_for_label(labels[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following two cells. They should print out `'NEGATIVE'` and `0`, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'NEGATIVE'" + ] + }, + "execution_count": 218, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels[1]" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 219, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_target_for_label(labels[1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# End of Project 2. \n", + "## Watch the next video to see Andrew's solution, then continue on to the next lesson." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project 3: Building a Neural Network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**TODO:** We've included the framework of a class called `SentimentNetork`. Implement all of the items marked `TODO` in the code. These include doing the following:\n", + "- Create a basic neural network much like the networks you've seen in earlier lessons and in Project 1, with an input layer, a hidden layer, and an output layer. \n", + "- Do **not** add a non-linearity in the hidden layer. That is, do not use an activation function when calculating the hidden layer outputs.\n", + "- Re-use the code from earlier in this notebook to create the training data (see `TODO`s in the code)\n", + "- Implement the `pre_process_data` function to create the vocabulary for our training data generating functions\n", + "- Ensure `train` trains over the entire corpus" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Where to Get Help if You Need it\n", + "- Re-watch earlier Udacity lectures\n", + "- Chapters 3-5 - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) - (Check inside your classroom for a discount code)" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "import sys\n", + "import numpy as np\n", + "\n", + "# Encapsulate our neural network in a class\n", + "class SentimentNetwork:\n", + " def __init__(self, reviews, labels, hidden_nodes = 10, learning_rate = 0.1):\n", + " \"\"\"Create a Sentiment Network with the given settings\n", + " Args:\n", + " reviews(list) - List of reviews used for training\n", + " labels(list) - List of POSITIVE/NEGATIVE labels associated with the given reviews\n", + " hidden_nodes(int) - Number of nodes to create in the hidden layer\n", + " learning_rate(float) - Learning rate to use while training\n", + " \n", + " \"\"\"\n", + " # Assign a seed to our random number generator to ensure we get\n", + " # reproducable results during development \n", + " np.random.seed(1)\n", + "\n", + " # process the reviews and their associated labels so that everything\n", + " # is ready for training\n", + " self.pre_process_data(reviews, labels)\n", + " \n", + " # Build the network to have the number of hidden nodes and the learning rate that\n", + " # were passed into this initializer. Make the same number of input nodes as\n", + " # there are vocabulary words and create a single output node.\n", + " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", + "\n", + " def pre_process_data(self, reviews, labels):\n", + " \n", + " review_vocab = set()\n", + " # TODO: populate review_vocab with all of the words in the given reviews\n", + " # Remember to split reviews into individual words \n", + " # using \"split(' ')\" instead of \"split()\".\n", + " \n", + " for text in reviews:\n", + " for word in text.split(\" \"):\n", + " review_vocab.add(word)\n", + " \n", + " \n", + " # Convert the vocabulary set to a list so we can access words via indices\n", + " self.review_vocab = list(review_vocab)\n", + " \n", + " label_vocab = set()\n", + " # TODO: populate label_vocab with all of the words in the given labels.\n", + " # There is no need to split the labels because each one is a single word.\n", + " for label in labels:\n", + " label_vocab.add(label)\n", + " \n", + " # Convert the label vocabulary set to a list so we can access labels via indices\n", + " self.label_vocab = list(label_vocab)\n", + " \n", + " # Store the sizes of the review and label vocabularies.\n", + " self.review_vocab_size = len(self.review_vocab)\n", + " self.label_vocab_size = len(self.label_vocab)\n", + " \n", + " # Create a dictionary of words in the vocabulary mapped to index positions\n", + " self.word2index = {}\n", + " # TODO: populate self.word2index with indices for all the words in self.review_vocab\n", + " # like you saw earlier in the notebook\n", + " for i, word in enumerate(self.review_vocab):\n", + " self.word2index[word] = i\n", + " \n", + " # Create a dictionary of labels mapped to index positions\n", + " self.label2index = {}\n", + " # TODO: do the same thing you did for self.word2index and self.review_vocab, \n", + " # but for self.label2index and self.label_vocab instead\n", + " for i, labl in enumerate(self.label_vocab):\n", + " self.label2index[labl] = i\n", + " \n", + " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", + " # Store the number of nodes in input, hidden, and output layers.\n", + " self.input_nodes = input_nodes\n", + " self.hidden_nodes = hidden_nodes\n", + " self.output_nodes = output_nodes\n", + "\n", + " # Store the learning rate\n", + " self.learning_rate = learning_rate\n", + "\n", + " # Initialize weights\n", + " \n", + " # TODO: initialize self.weights_0_1 as a matrix of zeros. These are the weights between\n", + " # the input layer and the hidden layer.\n", + " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", + " \n", + " # TODO: initialize self.weights_1_2 as a matrix of random values. \n", + " # These are the weights between the hidden layer and the output layer.\n", + " self.weights_1_2 = np.zeros((self.hidden_nodes,self.output_nodes))\n", + " \n", + " # TODO: Create the input layer, a two-dimensional matrix with shape \n", + " # 1 x input_nodes, with all values initialized to zero\n", + " self.layer_0 = np.zeros((1,input_nodes))\n", + " \n", + " \n", + " def update_input_layer(self,review):\n", + " # TODO: You can copy most of the code you wrote for update_input_layer \n", + " # earlier in this notebook. \n", + " #\n", + " # However, MAKE SURE YOU CHANGE ALL VARIABLES TO REFERENCE\n", + " # THE VERSIONS STORED IN THIS OBJECT, NOT THE GLOBAL OBJECTS.\n", + " # For example, replace \"layer_0 *= 0\" with \"self.layer_0 *= 0\"\n", + " self.layer_0 *= 0\n", + " for word in review.split(\" \"):\n", + " if(word in self.word2index.keys()):\n", + " self.layer_0[0][self.word2index[word]] += 1\n", + " \n", + " def get_target_for_label(self,label):\n", + " # TODO: Copy the code you wrote for get_target_for_label \n", + " # earlier in this notebook. \n", + " \n", + " if(label == \"POSITIVE\"):\n", + " return 1\n", + " else:\n", + " return 0\n", + " \n", + " def sigmoid(self,x):\n", + " # TODO: Return the result of calculating the sigmoid activation function\n", + " # shown in the lectures\n", + " return 1/(1 + np.exp(-x))\n", + " \n", + " def sigmoid_output_2_derivative(self,output):\n", + " # TODO: Return the derivative of the sigmoid activation function, \n", + " # where \"output\" is the original output from the sigmoid fucntion \n", + " return output * (1 - output)\n", + "\n", + " def train(self, training_reviews, training_labels):\n", + " \n", + " # make sure out we have a matching number of reviews and labels\n", + " assert(len(training_reviews) == len(training_labels))\n", + " \n", + " # Keep track of correct predictions to display accuracy during training \n", + " correct_so_far = 0\n", + " \n", + " # Remember when we started for printing time statistics\n", + " start = time.time()\n", + "\n", + " # loop through all the given reviews and run a forward and backward pass,\n", + " # updating weights for every item\n", + " for i in range(len(training_reviews)):\n", + " \n", + " # TODO: Get the next review and its correct label\n", + " \n", + " review = training_reviews[i]\n", + " label = training_labels[i]\n", + " \n", + " # TODO: Implement the forward pass through the network. \n", + " # That means use the given review to update the input layer, \n", + " # then calculate values for the hidden layer,\n", + " # and finally calculate the output layer.\n", + " # \n", + " # Do not use an activation function for the hidden layer,\n", + " # but use the sigmoid activation function for the output layer.\n", + " \n", + " # // Implementing Forward Bias // \n", + " ''' Input Layer '''\n", + " self.update_input_layer(review)\n", + " \n", + " ''' Hidden Layer '''\n", + " layer_1 = self.layer_0.dot(self.weights_0_1)\n", + " \n", + " ''' Output Layer '''\n", + " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", + " \n", + " \n", + " \n", + " # TODO: Implement the back propagation pass here. \n", + " # That means calculate the error for the forward pass's prediction\n", + " # and update the weights in the network according to their\n", + " # contributions toward the error, as calculated via the\n", + " # gradient descent and back propagation algorithms you \n", + " # learned in class.\n", + " \n", + " # // Implemnting Back Propagation //\n", + " ''' Output layer is difference between output came and actual output '''\n", + " layer_2_error = layer_2 - self.get_target_for_label(label)\n", + " layer_2_delta = layer_2 * self.sigmoid_output_2_derivative(layer_2)\n", + " \n", + " ''' Backpropagated errors '''\n", + " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated in to hidden layer\n", + " layer_1_delta = layer_1_error # hidden_layer gradients - no nonliearity so it's the same as the error\n", + " \n", + " ''' Updating the weights '''\n", + " self.weights_1_2 -= layer_1.T.dot(layer_2_delta)*self.learning_rate\n", + " self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta)*self.learning_rate\n", + " \n", + " \n", + " # TODO: Keep track of correct predictions. To determine if the prediction was\n", + " # correct, check that the absolute value of the output error \n", + " # is less than 0.5. If so, add one to the correct_so_far count.\n", + " \n", + " if(layer_2 >= 0.5 and label == 'POSITIVE'):\n", + " correct_so_far += 1\n", + " elif(layer_2 < 0.5 and label == 'NEGATIVE'):\n", + " correct_so_far += 1\n", + " \n", + " \n", + " # For debug purposes, print out our prediction accuracy and speed \n", + " # throughout the training process. \n", + "\n", + " elapsed_time = float(time.time() - start)\n", + " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", + " \n", + " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] \\\n", + " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", + " + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) \\\n", + " + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", + " if(i % 2500 == 0):\n", + " print(\"\")\n", + " \n", + " def test(self, testing_reviews, testing_labels):\n", + " \"\"\"\n", + " Attempts to predict the labels for the given testing_reviews,\n", + " and uses the test_labels to calculate the accuracy of those predictions.\n", + " \"\"\"\n", + " \n", + " # keep track of how many correct predictions we make\n", + " correct = 0\n", + "\n", + " # we'll time how many predictions per second we make\n", + " start = time.time()\n", + "\n", + " # Loop through each of the given reviews and call run to predict\n", + " # its label. \n", + " for i in range(len(testing_reviews)):\n", + " pred = self.run(testing_reviews[i])\n", + " if(pred == testing_labels[i]):\n", + " correct += 1\n", + " \n", + " # For debug purposes, print out our prediction accuracy and speed \n", + " # throughout the prediction process. \n", + "\n", + " elapsed_time = float(time.time() - start)\n", + " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", + " \n", + " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", + " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", + " + \" #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) \\\n", + " + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", + " \n", + " def run(self, review):\n", + " \"\"\"\n", + " Returns a POSITIVE or NEGATIVE prediction for the given review.\n", + " \"\"\"\n", + " # TODO: Run a forward pass through the network, like you did in the\n", + " # \"train\" function. That means use the given review to \n", + " # update the input layer, then calculate values for the hidden layer,\n", + " # and finally calculate the output layer.\n", + " # // Implementing Forward Bias // \n", + " ''' Input Layer '''\n", + " self.update_input_layer(review.lower())\n", + " \n", + " ''' Hidden Layer '''\n", + " layer_1 = self.layer_0.dot(self.weights_0_1)\n", + " \n", + " ''' Output Layer '''\n", + " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", + " \n", + " \n", + " #\n", + " # Note: The review passed into this function for prediction \n", + " # might come from anywhere, so you should convert it \n", + " # to lower case prior to using it.\n", + " \n", + " # TODO: The output layer should now contain a prediction. \n", + " # Return `POSITIVE` for predictions greater-than-or-equal-to `0.5`, \n", + " # and `NEGATIVE` otherwise.\n", + " if(layer_2[0] >= 0.5):\n", + " return \"POSITIVE\"\n", + " else:\n", + " return \"NEGATIVE\"\n", + " \n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to create a `SentimentNetwork` that will train on all but the last 1000 reviews (we're saving those for testing). Here we use a learning rate of `0.1`." + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [], + "source": [ + "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [], + "source": [ + "mlp.pre_process_data(reviews[-1000:],labels[-1000:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to test the network's performance against the last 1000 reviews (the ones we held out from our training set). \n", + "\n", + "**We have not trained the model yet, so the results should be about 50% as it will just be guessing and there are only two possible values to choose from.**" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Tested:1 Testing Accuracy:100.%\r", + "Progress:0.1% Speed(reviews/sec):422.8 #Correct:1 #Tested:2 Testing Accuracy:50.0%\r", + "Progress:0.2% Speed(reviews/sec):651.0 #Correct:2 #Tested:3 Testing Accuracy:66.6%\r", + "Progress:0.3% Speed(reviews/sec):771.3 #Correct:2 #Tested:4 Testing Accuracy:50.0%\r", + "Progress:0.4% Speed(reviews/sec):866.1 #Correct:3 #Tested:5 Testing Accuracy:60.0%\r", + "Progress:0.5% Speed(reviews/sec):850.6 #Correct:3 #Tested:6 Testing Accuracy:50.0%\r", + "Progress:0.6% Speed(reviews/sec):875.9 #Correct:4 #Tested:7 Testing Accuracy:57.1%\r", + "Progress:0.7% Speed(reviews/sec):883.5 #Correct:4 #Tested:8 Testing Accuracy:50.0%\r", + "Progress:0.8% Speed(reviews/sec):893.2 #Correct:5 #Tested:9 Testing Accuracy:55.5%\r", + "Progress:0.9% Speed(reviews/sec):844.8 #Correct:5 #Tested:10 Testing Accuracy:50.0%\r", + "Progress:1.0% Speed(reviews/sec):867.2 #Correct:6 #Tested:11 Testing Accuracy:54.5%\r", + "Progress:1.1% Speed(reviews/sec):896.7 #Correct:6 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Speed(reviews/sec):982.9 #Correct:499 #Tested:998 Testing Accuracy:50.0%\r", + "Progress:99.8% Speed(reviews/sec):982.9 #Correct:500 #Tested:999 Testing Accuracy:50.0%\r", + "Progress:99.9% Speed(reviews/sec):983.1 #Correct:500 #Tested:1000 Testing Accuracy:50.0%" + ] + } + ], + "source": [ + "mlp.test(reviews[-1000:],labels[-1000:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to actually train the network. During training, it will display the model's accuracy repeatedly as it trains so you can see how well it's doing." + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n", + "Progress:10.4% Speed(reviews/sec):197.9 #Correct:1251 #Trained:2501 Training Accuracy:50.0%\n", + "Progress:20.8% Speed(reviews/sec):196.4 #Correct:2501 #Trained:5001 Training Accuracy:50.0%\n", + "Progress:31.2% Speed(reviews/sec):201.2 #Correct:3751 #Trained:7501 Training Accuracy:50.0%\n", + "Progress:41.6% Speed(reviews/sec):204.1 #Correct:5001 #Trained:10001 Training Accuracy:50.0%\n", + "Progress:52.0% Speed(reviews/sec):203.8 #Correct:6251 #Trained:12501 Training Accuracy:50.0%\n", + "Progress:62.5% Speed(reviews/sec):200.5 #Correct:7501 #Trained:15001 Training Accuracy:50.0%\n", + "Progress:72.9% Speed(reviews/sec):200.7 #Correct:8751 #Trained:17501 Training Accuracy:50.0%\n", + "Progress:83.3% Speed(reviews/sec):201.0 #Correct:10001 #Trained:20001 Training Accuracy:50.0%\n", + "Progress:93.7% Speed(reviews/sec):202.3 #Correct:11251 #Trained:22501 Training Accuracy:50.0%\n", + "Progress:99.9% Speed(reviews/sec):203.0 #Correct:12000 #Trained:24000 Training Accuracy:50.0%" + ] + } + ], + "source": [ + "mlp.train(reviews[:-1000],labels[:-1000])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That most likely didn't train very well. Part of the reason may be because the learning rate is too high. Run the following cell to recreate the network with a smaller learning rate, `0.01`, and then train the new network." + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n", + "Progress:10.4% Speed(reviews/sec):235.5 #Correct:1251 #Trained:2501 Training Accuracy:50.0%\n", + "Progress:20.8% Speed(reviews/sec):233.7 #Correct:2501 #Trained:5001 Training Accuracy:50.0%\n", + "Progress:31.2% Speed(reviews/sec):230.4 #Correct:3751 #Trained:7501 Training Accuracy:50.0%\n", + "Progress:41.6% Speed(reviews/sec):228.9 #Correct:5001 #Trained:10001 Training Accuracy:50.0%\n", + "Progress:52.0% Speed(reviews/sec):229.1 #Correct:6251 #Trained:12501 Training Accuracy:50.0%\n", + "Progress:62.5% Speed(reviews/sec):229.6 #Correct:7501 #Trained:15001 Training Accuracy:50.0%\n", + "Progress:72.9% Speed(reviews/sec):228.0 #Correct:8751 #Trained:17501 Training Accuracy:50.0%\n", + "Progress:83.3% Speed(reviews/sec):228.5 #Correct:10001 #Trained:20001 Training Accuracy:50.0%\n", + "Progress:93.7% Speed(reviews/sec):223.5 #Correct:11251 #Trained:22501 Training Accuracy:50.0%\n", + "Progress:99.9% Speed(reviews/sec):223.0 #Correct:12000 #Trained:24000 Training Accuracy:50.0%" + ] + } + ], + "source": [ + "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01)\n", + "mlp.train(reviews[:-1000],labels[:-1000])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That probably wasn't much different. Run the following cell to recreate the network one more time with an even smaller learning rate, `0.001`, and then train the new network." + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n", + "Progress:10.4% Speed(reviews/sec):189.5 #Correct:1251 #Trained:2501 Training Accuracy:50.0%\n", + "Progress:20.8% Speed(reviews/sec):196.5 #Correct:2501 #Trained:5001 Training Accuracy:50.0%\n", + "Progress:31.2% Speed(reviews/sec):199.6 #Correct:3751 #Trained:7501 Training Accuracy:50.0%\n", + "Progress:41.6% Speed(reviews/sec):201.2 #Correct:5001 #Trained:10001 Training Accuracy:50.0%\n", + "Progress:52.0% Speed(reviews/sec):200.2 #Correct:6251 #Trained:12501 Training Accuracy:50.0%\n", + "Progress:62.5% Speed(reviews/sec):201.5 #Correct:7501 #Trained:15001 Training Accuracy:50.0%\n", + "Progress:72.9% Speed(reviews/sec):203.5 #Correct:8751 #Trained:17501 Training Accuracy:50.0%\n", + "Progress:83.3% Speed(reviews/sec):206.0 #Correct:10001 #Trained:20001 Training Accuracy:50.0%\n", + "Progress:93.7% Speed(reviews/sec):207.3 #Correct:11251 #Trained:22501 Training Accuracy:50.0%\n", + "Progress:99.9% Speed(reviews/sec):208.3 #Correct:12000 #Trained:24000 Training Accuracy:50.0%" + ] + } + ], + "source": [ + "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)\n", + "mlp.train(reviews[:-1000],labels[:-1000])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With a learning rate of `0.001`, the network should finall have started to improve during training. It's still not very good, but it shows that this solution has potential. We will improve it in the next lesson." + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [], + "source": [ + "mlp = SentimentNetwork(reviews[:],labels[:], learning_rate=0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [], + "source": [ + "mlp.pre_process_data(reviews[:],labels[:])" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Tested:1 Testing Accuracy:100.%\r", + "Progress:0.00% Speed(reviews/sec):160.4 #Correct:1 #Tested:2 Testing Accuracy:50.0%\r", + "Progress:0.00% Speed(reviews/sec):254.0 #Correct:2 #Tested:3 Testing Accuracy:66.6%\r", + "Progress:0.01% Speed(reviews/sec):294.9 #Correct:2 #Tested:4 Testing Accuracy:50.0%\r", + "Progress:0.01% Speed(reviews/sec):357.1 #Correct:3 #Tested:5 Testing Accuracy:60.0%\r", + 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Accuracy:50.0%\r", + "Progress:99.9% Speed(reviews/sec):862.2 #Correct:12499 #Tested:24997 Testing Accuracy:50.0%\r", + "Progress:99.9% Speed(reviews/sec):862.2 #Correct:12499 #Tested:24998 Testing Accuracy:50.0%\r", + "Progress:99.9% Speed(reviews/sec):862.2 #Correct:12500 #Tested:24999 Testing Accuracy:50.0%\r", + "Progress:99.9% Speed(reviews/sec):862.2 #Correct:12500 #Tested:25000 Testing Accuracy:50.0%" + ] + } + ], + "source": [ + "mlp.test(reviews[:],labels[:])" + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n", + "Progress:10.0% Speed(reviews/sec):195.6 #Correct:1251 #Trained:2501 Training Accuracy:50.0%\n", + "Progress:20.0% Speed(reviews/sec):195.0 #Correct:2501 #Trained:5001 Training Accuracy:50.0%\n", + "Progress:30.0% Speed(reviews/sec):197.4 #Correct:3751 #Trained:7501 Training Accuracy:50.0%\n", + "Progress:40.0% Speed(reviews/sec):194.8 #Correct:5001 #Trained:10001 Training Accuracy:50.0%\n", + "Progress:50.0% Speed(reviews/sec):193.2 #Correct:6251 #Trained:12501 Training Accuracy:50.0%\n", + "Progress:60.0% Speed(reviews/sec):198.7 #Correct:7501 #Trained:15001 Training Accuracy:50.0%\n", + "Progress:70.0% Speed(reviews/sec):199.4 #Correct:8751 #Trained:17501 Training Accuracy:50.0%\n", + "Progress:80.0% Speed(reviews/sec):201.0 #Correct:10001 #Trained:20001 Training Accuracy:50.0%\n", + "Progress:90.0% Speed(reviews/sec):202.2 #Correct:11251 #Trained:22501 Training Accuracy:50.0%\n", + "Progress:99.9% Speed(reviews/sec):200.4 #Correct:12500 #Trained:25000 Training Accuracy:50.0%" + ] + } + ], + "source": [ + "mlp.train(reviews[:],labels[:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# End of Project 3. \n", + "## Watch the next video to see Andrew's solution, then continue on to the next lesson." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Understanding Neural Noise\n", + "\n", + "The following cells include includes the code Andrew shows in the next video. We've included it here so you can run the cells along with the video without having to type in everything." + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 235, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import Image\n", + "Image(filename='sentiment_network.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [], + "source": [ + "def update_input_layer(review):\n", + " \n", + " global layer_0\n", + " \n", + " # clear out previous state, reset the layer to be all 0s\n", + " layer_0 *= 0\n", + " for word in review.split(\" \"):\n", + " layer_0[0][word2index[word]] += 1\n", + "\n", + "update_input_layer(reviews[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[18., 0., 0., ..., 0., 0., 0.]])" + ] + }, + "execution_count": 244, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "layer_0" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [], + "source": [ + "review_counter = Counter()" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": {}, + "outputs": [], + "source": [ + "for word in reviews[0].split(\" \"):\n", + " review_counter[word] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('.', 27),\n", + " ('', 18),\n", + " ('the', 9),\n", + " ('to', 6),\n", + " ('high', 5),\n", + " ('i', 5),\n", + " ('bromwell', 4),\n", + " ('is', 4),\n", + " ('a', 4),\n", + " ('teachers', 4),\n", + " ('that', 4),\n", + " ('of', 4),\n", + " ('it', 2),\n", + " ('at', 2),\n", + " ('as', 2),\n", + " ('school', 2),\n", + " ('my', 2),\n", + " ('in', 2),\n", + " ('me', 2),\n", + " ('students', 2),\n", + " ('their', 2),\n", + " ('student', 2),\n", + " ('cartoon', 1),\n", + " ('comedy', 1),\n", + " ('ran', 1),\n", + " ('same', 1),\n", + " ('time', 1),\n", + " ('some', 1),\n", + " ('other', 1),\n", + " ('programs', 1),\n", + " ('about', 1),\n", + " ('life', 1),\n", + " ('such', 1),\n", + " ('years', 1),\n", + " ('teaching', 1),\n", + " ('profession', 1),\n", + " ('lead', 1),\n", + " ('believe', 1),\n", + " ('s', 1),\n", + " ('satire', 1),\n", + " ('much', 1),\n", + " ('closer', 1),\n", + " ('reality', 1),\n", + " ('than', 1),\n", + " ('scramble', 1),\n", + " ('survive', 1),\n", + " ('financially', 1),\n", + " ('insightful', 1),\n", + " ('who', 1),\n", + " ('can', 1),\n", + " ('see', 1),\n", + " ('right', 1),\n", + " ('through', 1),\n", + " ('pathetic', 1),\n", + " ('pomp', 1),\n", + " ('pettiness', 1),\n", + " ('whole', 1),\n", + " ('situation', 1),\n", + " ('all', 1),\n", + " ('remind', 1),\n", + " ('schools', 1),\n", + " ('knew', 1),\n", + " ('and', 1),\n", + " ('when', 1),\n", + " ('saw', 1),\n", + " ('episode', 1),\n", + " ('which', 1),\n", + " ('repeatedly', 1),\n", + " ('tried', 1),\n", + " ('burn', 1),\n", + " ('down', 1),\n", + " ('immediately', 1),\n", + " ('recalled', 1),\n", + " ('classic', 1),\n", + " ('line', 1),\n", + " ('inspector', 1),\n", + " ('m', 1),\n", + " ('here', 1),\n", + " ('sack', 1),\n", + " ('one', 1),\n", + " ('your', 1),\n", + " ('welcome', 1),\n", + " ('expect', 1),\n", + " ('many', 1),\n", + " ('adults', 1),\n", + " ('age', 1),\n", + " ('think', 1),\n", + " ('far', 1),\n", + " ('fetched', 1),\n", + " ('what', 1),\n", + " ('pity', 1),\n", + " ('isn', 1),\n", + " ('t', 1)]" + ] + }, + "execution_count": 247, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "review_counter.most_common()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project 4: Reducing Noise in Our Input Data\n", + "\n", + "**TODO:** Attempt to reduce the noise in the input data like Andrew did in the previous video. Specifically, do the following:\n", + "* Copy the `SentimentNetwork` class you created earlier into the following cell.\n", + "* Modify `update_input_layer` so it does not count how many times each word is used, but rather just stores whether or not a word was used. " + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: -Copy the SentimentNetwork class from Projet 3 lesson\n", + "# -Modify it to reduce noise, like in the video \n", + "import time\n", + "import sys\n", + "import numpy as np\n", + "\n", + "# Encapsulate our neural network in a class\n", + "class SentimentNetwork:\n", + " def __init__(self, reviews, labels, hidden_nodes = 10, learning_rate = 0.1):\n", + " \"\"\"Create a Sentiment Network with the given settings\n", + " Args:\n", + " reviews(list) - List of reviews used for training\n", + " labels(list) - List of POSITIVE/NEGATIVE labels associated with the given reviews\n", + " hidden_nodes(int) - Number of nodes to create in the hidden layer\n", + " learning_rate(float) - Learning rate to use while training\n", + " \n", + " \"\"\"\n", + " # Assign a seed to our random number generator to ensure we get\n", + " # reproducable results during development \n", + " np.random.seed(1)\n", + "\n", + " # process the reviews and their associated labels so that everything\n", + " # is ready for training\n", + " self.pre_process_data(reviews, labels)\n", + " \n", + " # Build the network to have the number of hidden nodes and the learning rate that\n", + " # were passed into this initializer. Make the same number of input nodes as\n", + " # there are vocabulary words and create a single output node.\n", + " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", + "\n", + " def pre_process_data(self, reviews, labels):\n", + " \n", + " review_vocab = set()\n", + " # TODO: populate review_vocab with all of the words in the given reviews\n", + " # Remember to split reviews into individual words \n", + " # using \"split(' ')\" instead of \"split()\".\n", + " \n", + " for text in reviews:\n", + " for word in text.split(\" \"):\n", + " review_vocab.add(word)\n", + " \n", + " \n", + " # Convert the vocabulary set to a list so we can access words via indices\n", + " self.review_vocab = list(review_vocab)\n", + " \n", + " label_vocab = set()\n", + " # TODO: populate label_vocab with all of the words in the given labels.\n", + " # There is no need to split the labels because each one is a single word.\n", + " for label in labels:\n", + " label_vocab.add(label)\n", + " \n", + " # Convert the label vocabulary set to a list so we can access labels via indices\n", + " self.label_vocab = list(label_vocab)\n", + " \n", + " # Store the sizes of the review and label vocabularies.\n", + " self.review_vocab_size = len(self.review_vocab)\n", + " self.label_vocab_size = len(self.label_vocab)\n", + " \n", + " # Create a dictionary of words in the vocabulary mapped to index positions\n", + " self.word2index = {}\n", + " # TODO: populate self.word2index with indices for all the words in self.review_vocab\n", + " # like you saw earlier in the notebook\n", + " for i, word in enumerate(self.review_vocab):\n", + " self.word2index[word] = i\n", + " \n", + " # Create a dictionary of labels mapped to index positions\n", + " self.label2index = {}\n", + " # TODO: do the same thing you did for self.word2index and self.review_vocab, \n", + " # but for self.label2index and self.label_vocab instead\n", + " for i, labl in enumerate(self.label_vocab):\n", + " self.label2index[labl] = i\n", + " \n", + " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", + " # Store the number of nodes in input, hidden, and output layers.\n", + " self.input_nodes = input_nodes\n", + " self.hidden_nodes = hidden_nodes\n", + " self.output_nodes = output_nodes\n", + "\n", + " # Store the learning rate\n", + " self.learning_rate = learning_rate\n", + "\n", + " # Initialize weights\n", + " \n", + " # TODO: initialize self.weights_0_1 as a matrix of zeros. These are the weights between\n", + " # the input layer and the hidden layer.\n", + " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", + " \n", + " # TODO: initialize self.weights_1_2 as a matrix of random values. \n", + " # These are the weights between the hidden layer and the output layer.\n", + " self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n", + " (self.hidden_nodes, self.output_nodes)) # MARKED AS WRONG\n", + " \n", + " # TODO: Create the input layer, a two-dimensional matrix with shape \n", + " # 1 x input_nodes, with all values initialized to zero\n", + " self.layer_0 = np.zeros((1,input_nodes))\n", + " \n", + " \n", + " def update_input_layer(self,review):\n", + " # TODO: You can copy most of the code you wrote for update_input_layer \n", + " # earlier in this notebook. \n", + " #\n", + " # However, MAKE SURE YOU CHANGE ALL VARIABLES TO REFERENCE\n", + " # THE VERSIONS STORED IN THIS OBJECT, NOT THE GLOBAL OBJECTS.\n", + " # For example, replace \"layer_0 *= 0\" with \"self.layer_0 *= 0\"\n", + " self.layer_0 *= 0\n", + " for word in review.split(\" \"):\n", + " if(word in self.word2index.keys()):\n", + " self.layer_0[0][self.word2index[word]] = 1\n", + " \n", + " def get_target_for_label(self,label):\n", + " # TODO: Copy the code you wrote for get_target_for_label \n", + " # earlier in this notebook. \n", + " \n", + " if(label == \"POSITIVE\"):\n", + " return 1\n", + " else:\n", + " return 0\n", + " \n", + " def sigmoid(self,x):\n", + " # TODO: Return the result of calculating the sigmoid activation function\n", + " # shown in the lectures\n", + " return 1/(1 + np.exp(-x))\n", + " \n", + " def sigmoid_output_2_derivative(self,output):\n", + " # TODO: Return the derivative of the sigmoid activation function, \n", + " # where \"output\" is the original output from the sigmoid fucntion \n", + " return output * (1 - output)\n", + "\n", + " def train(self, training_reviews, training_labels):\n", + " \n", + " # make sure out we have a matching number of reviews and labels\n", + " assert(len(training_reviews) == len(training_labels))\n", + " \n", + " # Keep track of correct predictions to display accuracy during training \n", + " correct_so_far = 0\n", + " \n", + " # Remember when we started for printing time statistics\n", + " start = time.time()\n", + "\n", + " # loop through all the given reviews and run a forward and backward pass,\n", + " # updating weights for every item\n", + " for i in range(len(training_reviews)):\n", + " \n", + " # TODO: Get the next review and its correct label\n", + " \n", + " review = training_reviews[i]\n", + " label = training_labels[i]\n", + " \n", + " # TODO: Implement the forward pass through the network. \n", + " # That means use the given review to update the input layer, \n", + " # then calculate values for the hidden layer,\n", + " # and finally calculate the output layer.\n", + " # \n", + " # Do not use an activation function for the hidden layer,\n", + " # but use the sigmoid activation function for the output layer.\n", + " \n", + " # // Implementing Forward Bias // \n", + " ''' Input Layer '''\n", + " self.update_input_layer(review)\n", + " \n", + " ''' Hidden Layer '''\n", + " layer_1 = self.layer_0.dot(self.weights_0_1)\n", + " \n", + " ''' Output Layer '''\n", + " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", + " \n", + " \n", + " \n", + " # TODO: Implement the back propagation pass here. \n", + " # That means calculate the error for the forward pass's prediction\n", + " # and update the weights in the network according to their\n", + " # contributions toward the error, as calculated via the\n", + " # gradient descent and back propagation algorithms you \n", + " # learned in class.\n", + " \n", + " # // Implemnting Back Propagation //\n", + " ''' Output layer is difference between output came and actual output '''\n", + " layer_2_error = layer_2 - self.get_target_for_label(label)\n", + " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2) #MARKED WRONG\n", + " \n", + " ''' Backpropagated errors '''\n", + " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated in to hidden layer\n", + " layer_1_delta = layer_1_error # hidden_layer gradients - no nonliearity so it's the same as the error\n", + " \n", + " ''' Updating the weights '''\n", + " self.weights_1_2 -= layer_1.T.dot(layer_2_delta)*self.learning_rate\n", + " self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta)*self.learning_rate\n", + " \n", + " \n", + " # TODO: Keep track of correct predictions. To determine if the prediction was\n", + " # correct, check that the absolute value of the output error \n", + " # is less than 0.5. If so, add one to the correct_so_far count.\n", + " \n", + " if(layer_2 >= 0.5 and label == 'POSITIVE'):\n", + " correct_so_far += 1\n", + " elif(layer_2 < 0.5 and label == 'NEGATIVE'):\n", + " correct_so_far += 1\n", + " \n", + " \n", + " # For debug purposes, print out our prediction accuracy and speed \n", + " # throughout the training process. \n", + "\n", + " elapsed_time = float(time.time() - start)\n", + " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", + " \n", + " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] \\\n", + " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", + " + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) \\\n", + " + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", + " if(i % 2500 == 0):\n", + " print(\"\")\n", + " \n", + " def test(self, testing_reviews, testing_labels):\n", + " \"\"\"\n", + " Attempts to predict the labels for the given testing_reviews,\n", + " and uses the test_labels to calculate the accuracy of those predictions.\n", + " \"\"\"\n", + " \n", + " # keep track of how many correct predictions we make\n", + " correct = 0\n", + "\n", + " # we'll time how many predictions per second we make\n", + " start = time.time()\n", + "\n", + " # Loop through each of the given reviews and call run to predict\n", + " # its label. \n", + " for i in range(len(testing_reviews)):\n", + " pred = self.run(testing_reviews[i])\n", + " if(pred == testing_labels[i]):\n", + " correct += 1\n", + " \n", + " # For debug purposes, print out our prediction accuracy and speed \n", + " # throughout the prediction process. \n", + "\n", + " elapsed_time = float(time.time() - start)\n", + " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", + " \n", + " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", + " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", + " + \" #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) \\\n", + " + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", + " \n", + " def run(self, review):\n", + " \"\"\"\n", + " Returns a POSITIVE or NEGATIVE prediction for the given review.\n", + " \"\"\"\n", + " # TODO: Run a forward pass through the network, like you did in the\n", + " # \"train\" function. That means use the given review to \n", + " # update the input layer, then calculate values for the hidden layer,\n", + " # and finally calculate the output layer.\n", + " # // Implementing Forward Bias // \n", + " ''' Input Layer '''\n", + " self.update_input_layer(review.lower())\n", + " \n", + " ''' Hidden Layer '''\n", + " layer_1 = self.layer_0.dot(self.weights_0_1)\n", + " \n", + " ''' Output Layer '''\n", + " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", + " \n", + " \n", + " #\n", + " # Note: The review passed into this function for prediction \n", + " # might come from anywhere, so you should convert it \n", + " # to lower case prior to using it.\n", + " \n", + " # TODO: The output layer should now contain a prediction. \n", + " # Return `POSITIVE` for predictions greater-than-or-equal-to `0.5`, \n", + " # and `NEGATIVE` otherwise.\n", + " if(layer_2[0] >= 0.5):\n", + " return \"POSITIVE\"\n", + " else:\n", + " return \"NEGATIVE\"\n", + " \n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to recreate the network and train it. Notice we've gone back to the higher learning rate of `0.1`." + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n", + "Progress:10.4% Speed(reviews/sec):109.8 #Correct:1803 #Trained:2501 Training Accuracy:72.0%\n", + "Progress:20.8% Speed(reviews/sec):111.0 #Correct:3777 #Trained:5001 Training Accuracy:75.5%\n", + "Progress:31.2% Speed(reviews/sec):111.4 #Correct:5876 #Trained:7501 Training Accuracy:78.3%\n", + "Progress:41.6% Speed(reviews/sec):110.6 #Correct:8002 #Trained:10001 Training Accuracy:80.0%\n", + "Progress:52.0% Speed(reviews/sec):107.7 #Correct:10137 #Trained:12501 Training Accuracy:81.0%\n", + "Progress:62.5% Speed(reviews/sec):106.0 #Correct:12277 #Trained:15001 Training Accuracy:81.8%\n", + "Progress:72.9% Speed(reviews/sec):104.6 #Correct:14402 #Trained:17501 Training Accuracy:82.2%\n", + "Progress:83.3% Speed(reviews/sec):111.7 #Correct:16581 #Trained:20001 Training Accuracy:82.9%\n", + "Progress:93.7% Speed(reviews/sec):117.8 #Correct:18762 #Trained:22501 Training Accuracy:83.3%\n", + "Progress:99.9% Speed(reviews/sec):121.4 #Correct:20091 #Trained:24000 Training Accuracy:83.7%" + ] + } + ], + "source": [ + "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)\n", + "mlp.train(reviews[:-1000],labels[:-1000])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That should have trained much better than the earlier attempts. It's still not wonderful, but it should have improved dramatically. Run the following cell to test your model with 1000 predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Tested:1 Testing Accuracy:100.%\r", + "Progress:0.1% Speed(reviews/sec):340.5 #Correct:1 #Tested:2 Testing Accuracy:50.0%\r", + "Progress:0.2% Speed(reviews/sec):472.0 #Correct:1 #Tested:3 Testing Accuracy:33.3%\r", + "Progress:0.3% Speed(reviews/sec):483.1 #Correct:2 #Tested:4 Testing Accuracy:50.0%\r", + "Progress:0.4% Speed(reviews/sec):570.9 #Correct:3 #Tested:5 Testing Accuracy:60.0%\r", + "Progress:0.5% Speed(reviews/sec):643.0 #Correct:4 #Tested:6 Testing Accuracy:66.6%\r", + "Progress:0.6% Speed(reviews/sec):691.1 #Correct:5 #Tested:7 Testing Accuracy:71.4%\r", + "Progress:0.7% Speed(reviews/sec):736.8 #Correct:6 #Tested:8 Testing Accuracy:75.0%\r", + "Progress:0.8% Speed(reviews/sec):770.8 #Correct:7 #Tested:9 Testing Accuracy:77.7%\r", + "Progress:0.9% Speed(reviews/sec):743.2 #Correct:8 #Tested:10 Testing Accuracy:80.0%\r", + "Progress:1.0% Speed(reviews/sec):767.4 #Correct:9 #Tested:11 Testing Accuracy:81.8%\r", + "Progress:1.1% Speed(reviews/sec):793.7 #Correct:10 #Tested:12 Testing Accuracy:83.3%\r", + "Progress:1.2% Speed(reviews/sec):816.3 #Correct:11 #Tested:13 Testing Accuracy:84.6%\r", + "Progress:1.3% Speed(reviews/sec):834.8 #Correct:12 #Tested:14 Testing Accuracy:85.7%\r", + "Progress:1.4% Speed(reviews/sec):852.3 #Correct:13 #Tested:15 Testing Accuracy:86.6%\r", + "Progress:1.5% Speed(reviews/sec):867.5 #Correct:14 #Tested:16 Testing Accuracy:87.5%\r", + "Progress:1.6% Speed(reviews/sec):860.1 #Correct:15 #Tested:17 Testing Accuracy:88.2%\r", + "Progress:1.7% Speed(reviews/sec):871.5 #Correct:16 #Tested:18 Testing Accuracy:88.8%\r", + "Progress:1.8% Speed(reviews/sec):887.8 #Correct:17 #Tested:19 Testing Accuracy:89.4%\r", + "Progress:1.9% Speed(reviews/sec):864.6 #Correct:17 #Tested:20 Testing Accuracy:85.0%\r", + "Progress:2.0% Speed(reviews/sec):862.0 #Correct:18 #Tested:21 Testing Accuracy:85.7%\r", + "Progress:2.1% Speed(reviews/sec):868.8 #Correct:19 #Tested:22 Testing Accuracy:86.3%\r", + "Progress:2.2% Speed(reviews/sec):871.5 #Correct:20 #Tested:23 Testing Accuracy:86.9%\r", + "Progress:2.3% Speed(reviews/sec):876.7 #Correct:21 #Tested:24 Testing Accuracy:87.5%\r", + "Progress:2.4% Speed(reviews/sec):872.3 #Correct:22 #Tested:25 Testing Accuracy:88.0%\r", + "Progress:2.5% Speed(reviews/sec):878.1 #Correct:23 #Tested:26 Testing Accuracy:88.4%\r", + "Progress:2.6% Speed(reviews/sec):887.2 #Correct:24 #Tested:27 Testing Accuracy:88.8%\r", + "Progress:2.7% Speed(reviews/sec):873.1 #Correct:25 #Tested:28 Testing Accuracy:89.2%\r", + "Progress:2.8% Speed(reviews/sec):879.2 #Correct:26 #Tested:29 Testing Accuracy:89.6%\r", + "Progress:2.9% Speed(reviews/sec):879.3 #Correct:27 #Tested:30 Testing Accuracy:90.0%\r", + "Progress:3.0% Speed(reviews/sec):876.4 #Correct:28 #Tested:31 Testing Accuracy:90.3%\r", + "Progress:3.1% Speed(reviews/sec):880.1 #Correct:29 #Tested:32 Testing Accuracy:90.6%\r", + "Progress:3.2% Speed(reviews/sec):880.9 #Correct:30 #Tested:33 Testing Accuracy:90.9%\r", + "Progress:3.3% Speed(reviews/sec):883.8 #Correct:31 #Tested:34 Testing Accuracy:91.1%\r", + "Progress:3.4% Speed(reviews/sec):885.7 #Correct:32 #Tested:35 Testing Accuracy:91.4%\r", + "Progress:3.5% Speed(reviews/sec):892.6 #Correct:32 #Tested:36 Testing Accuracy:88.8%\r", + "Progress:3.6% Speed(reviews/sec):894.6 #Correct:33 #Tested:37 Testing Accuracy:89.1%\r", + "Progress:3.7% Speed(reviews/sec):903.0 #Correct:34 #Tested:38 Testing Accuracy:89.4%\r", + "Progress:3.8% Speed(reviews/sec):904.2 #Correct:35 #Tested:39 Testing Accuracy:89.7%\r", + "Progress:3.9% Speed(reviews/sec):904.1 #Correct:35 #Tested:40 Testing Accuracy:87.5%\r", + "Progress:4.0% Speed(reviews/sec):906.7 #Correct:36 #Tested:41 Testing Accuracy:87.8%\r", + "Progress:4.1% Speed(reviews/sec):911.7 #Correct:37 #Tested:42 Testing Accuracy:88.0%\r", + "Progress:4.2% Speed(reviews/sec):906.1 #Correct:38 #Tested:43 Testing Accuracy:88.3%\r", + "Progress:4.3% Speed(reviews/sec):907.2 #Correct:39 #Tested:44 Testing Accuracy:88.6%\r", + "Progress:4.4% Speed(reviews/sec):903.4 #Correct:40 #Tested:45 Testing Accuracy:88.8%\r", + "Progress:4.5% Speed(reviews/sec):892.4 #Correct:40 #Tested:46 Testing Accuracy:86.9%\r", + "Progress:4.6% Speed(reviews/sec):890.5 #Correct:41 #Tested:47 Testing Accuracy:87.2%\r", + "Progress:4.7% Speed(reviews/sec):896.7 #Correct:42 #Tested:48 Testing Accuracy:87.5%\r", + "Progress:4.8% Speed(reviews/sec):895.6 #Correct:43 #Tested:49 Testing Accuracy:87.7%\r", + "Progress:4.9% Speed(reviews/sec):899.9 #Correct:44 #Tested:50 Testing Accuracy:88.0%\r", + "Progress:5.0% Speed(reviews/sec):905.3 #Correct:45 #Tested:51 Testing Accuracy:88.2%\r", + "Progress:5.1% Speed(reviews/sec):910.8 #Correct:46 #Tested:52 Testing Accuracy:88.4%\r", + "Progress:5.2% Speed(reviews/sec):907.3 #Correct:47 #Tested:53 Testing Accuracy:88.6%\r", + "Progress:5.3% Speed(reviews/sec):908.4 #Correct:48 #Tested:54 Testing Accuracy:88.8%\r", + "Progress:5.4% Speed(reviews/sec):912.3 #Correct:49 #Tested:55 Testing Accuracy:89.0%\r", + "Progress:5.5% Speed(reviews/sec):917.7 #Correct:50 #Tested:56 Testing Accuracy:89.2%\r", + "Progress:5.6% Speed(reviews/sec):902.6 #Correct:51 #Tested:57 Testing Accuracy:89.4%\r", + "Progress:5.7% Speed(reviews/sec):904.5 #Correct:52 #Tested:58 Testing Accuracy:89.6%\r", + "Progress:5.8% Speed(reviews/sec):906.6 #Correct:53 #Tested:59 Testing Accuracy:89.8%\r", + "Progress:5.9% Speed(reviews/sec):906.8 #Correct:54 #Tested:60 Testing Accuracy:90.0%\r", + "Progress:6.0% Speed(reviews/sec):899.6 #Correct:55 #Tested:61 Testing Accuracy:90.1%\r", + "Progress:6.1% Speed(reviews/sec):896.5 #Correct:56 #Tested:62 Testing Accuracy:90.3%\r", + "Progress:6.2% Speed(reviews/sec):889.0 #Correct:57 #Tested:63 Testing Accuracy:90.4%\r", + "Progress:6.3% Speed(reviews/sec):891.5 #Correct:58 #Tested:64 Testing Accuracy:90.6%\r", + "Progress:6.4% Speed(reviews/sec):892.2 #Correct:58 #Tested:65 Testing Accuracy:89.2%\r", + "Progress:6.5% Speed(reviews/sec):897.6 #Correct:58 #Tested:66 Testing Accuracy:87.8%\r", + "Progress:6.6% Speed(reviews/sec):903.4 #Correct:59 #Tested:67 Testing Accuracy:88.0%\r", + "Progress:6.7% Speed(reviews/sec):908.1 #Correct:60 #Tested:68 Testing Accuracy:88.2%\r", + "Progress:6.8% Speed(reviews/sec):913.2 #Correct:61 #Tested:69 Testing Accuracy:88.4%\r", + "Progress:6.9% Speed(reviews/sec):916.7 #Correct:62 #Tested:70 Testing Accuracy:88.5%\r", + "Progress:7.0% Speed(reviews/sec):919.5 #Correct:62 #Tested:71 Testing Accuracy:87.3%\r", + "Progress:7.1% Speed(reviews/sec):925.0 #Correct:63 #Tested:72 Testing Accuracy:87.5%\r", + "Progress:7.2% Speed(reviews/sec):927.3 #Correct:64 #Tested:73 Testing Accuracy:87.6%\r", + "Progress:7.3% 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Then continue on to the next lesson.\n", + "# Analyzing Inefficiencies in our Network\n", + "The following cells include the code Andrew shows in the next video. We've included it here so you can run the cells along with the video without having to type in everything." + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 255, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename='sentiment_network_sparse.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [], + "source": [ + "layer_0 = np.zeros(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 257, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "layer_0" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [], + "source": [ + "layer_0[4] = 1\n", + "layer_0[9] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 259, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 1., 0., 0., 0., 0., 1.])" + ] + }, + "execution_count": 259, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "layer_0" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-1.37311732, 0.31515939, 0.84616065, -0.85951594, 0.35054598],\n", + " [-1.31228341, -0.03869551, -1.61577235, 1.12141771, 0.40890054],\n", + " [-0.02461696, -0.77516162, 1.27375593, 1.96710175, -1.85798186],\n", + " [ 1.23616403, 1.62765075, 0.3380117 , -1.19926803, 0.86334532],\n", + " [-0.1809203 , -0.60392063, -1.23005814, 0.5505375 , 0.79280687],\n", + " [-0.62353073, 0.52057634, -1.14434139, 0.80186103, 0.0465673 ],\n", + " [-0.18656977, -0.10174587, 0.86888616, 0.75041164, 0.52946532],\n", + " [ 0.13770121, 0.07782113, 0.61838026, 0.23249456, 0.68255141],\n", + " [-0.31011677, -2.43483776, 1.0388246 , 2.18697965, 0.44136444],\n", + " [-0.10015523, -0.13644474, -0.11905419, 0.01740941, -1.12201873]])" + ] + }, + "execution_count": 270, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weights_0_1 = np.random.randn(10,5)\n", + "weights_0_1" + ] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.28107554, -0.74036537, -1.34911232, 0.5679469 , -0.32921186])" + ] + }, + "execution_count": 271, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "layer_0.dot(weights_0_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": {}, + "outputs": [], + "source": [ + "indices = [4,9]" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 273, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "layer_1 = np.zeros(5)\n", + "layer_1" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.28107554 -0.74036537 -1.34911232 0.5679469 -0.32921186]\n" + ] + } + ], + "source": [ + "for index in indices:\n", + " layer_1 += (1 * weights_0_1[index])\n", + "print(layer_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.28107554, -0.74036537, -1.34911232, 0.5679469 , -0.32921186])" + ] + }, + "execution_count": 275, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "layer_1" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 276, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename='sentiment_network_sparse_2.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [], + "source": [ + "layer_1 = np.zeros(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [], + "source": [ + "for index in indices:\n", + " layer_1 += (weights_0_1[index])" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.28107554, -0.74036537, -1.34911232, 0.5679469 , -0.32921186])" + ] + }, + "execution_count": 279, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "layer_1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project 5: Making our Network More Efficient\n", + "**TODO:** Make the `SentimentNetwork` class more efficient by eliminating unnecessary multiplications and additions that occur during forward and backward propagation. To do that, you can do the following:\n", + "* Copy the `SentimentNetwork` class from the previous project into the following cell.\n", + "* Remove the `update_input_layer` function - you will not need it in this version.\n", + "* Modify `init_network`:\n", + ">* You no longer need a separate input layer, so remove any mention of `self.layer_0`\n", + ">* You will be dealing with the old hidden layer more directly, so create `self.layer_1`, a two-dimensional matrix with shape 1 x hidden_nodes, with all values initialized to zero\n", + "* Modify `train`:\n", + ">* Change the name of the input parameter `training_reviews` to `training_reviews_raw`. This will help with the next step.\n", + ">* At the beginning of the function, you'll want to preprocess your reviews to convert them to a list of indices (from `word2index`) that are actually used in the review. This is equivalent to what you saw in the video when Andrew set specific indices to 1. Your code should create a local `list` variable named `training_reviews` that should contain a `list` for each review in `training_reviews_raw`. Those lists should contain the indices for words found in the review.\n", + ">* Remove call to `update_input_layer`\n", + ">* Use `self`'s `layer_1` instead of a local `layer_1` object.\n", + ">* In the forward pass, replace the code that updates `layer_1` with new logic that only adds the weights for the indices used in the review.\n", + ">* When updating `weights_0_1`, only update the individual weights that were used in the forward pass.\n", + "* Modify `run`:\n", + ">* Remove call to `update_input_layer` \n", + ">* Use `self`'s `layer_1` instead of a local `layer_1` object.\n", + ">* Much like you did in `train`, you will need to pre-process the `review` so you can work with word indices, then update `layer_1` by adding weights for the indices used in the review." + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: -Copy the SentimentNetwork class from Project 4 lesson\n", + "# -Modify it according to the above instructions \n", + "# TODO: -Copy the SentimentNetwork class from Projet 3 lesson\n", + "# -Modify it to reduce noise, like in the video \n", + "import time\n", + "import sys\n", + "import numpy as np\n", + "\n", + "# Encapsulate our neural network in a class\n", + "class SentimentNetwork:\n", + " def __init__(self, reviews, labels, hidden_nodes = 10, learning_rate = 0.1):\n", + " \"\"\"Create a Sentiment Network with the given settings\n", + " Args:\n", + " reviews(list) - List of reviews used for training\n", + " labels(list) - List of POSITIVE/NEGATIVE labels associated with the given reviews\n", + " hidden_nodes(int) - Number of nodes to create in the hidden layer\n", + " learning_rate(float) - Learning rate to use while training\n", + " \n", + " \"\"\"\n", + " # Assign a seed to our random number generator to ensure we get\n", + " # reproducable results during development \n", + " np.random.seed(1)\n", + "\n", + " # process the reviews and their associated labels so that everything\n", + " # is ready for training\n", + " self.pre_process_data(reviews, labels)\n", + " \n", + " # Build the network to have the number of hidden nodes and the learning rate that\n", + " # were passed into this initializer. Make the same number of input nodes as\n", + " # there are vocabulary words and create a single output node.\n", + " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", + "\n", + " def pre_process_data(self, reviews, labels):\n", + " \n", + " review_vocab = set()\n", + " # TODO: populate review_vocab with all of the words in the given reviews\n", + " # Remember to split reviews into individual words \n", + " # using \"split(' ')\" instead of \"split()\".\n", + " \n", + " for text in reviews:\n", + " for word in text.split(\" \"):\n", + " review_vocab.add(word)\n", + " \n", + " \n", + " # Convert the vocabulary set to a list so we can access words via indices\n", + " self.review_vocab = list(review_vocab)\n", + " \n", + " label_vocab = set()\n", + " # TODO: populate label_vocab with all of the words in the given labels.\n", + " # There is no need to split the labels because each one is a single word.\n", + " for label in labels:\n", + " label_vocab.add(label)\n", + " \n", + " # Convert the label vocabulary set to a list so we can access labels via indices\n", + " self.label_vocab = list(label_vocab)\n", + " \n", + " # Store the sizes of the review and label vocabularies.\n", + " self.review_vocab_size = len(self.review_vocab)\n", + " self.label_vocab_size = len(self.label_vocab)\n", + " \n", + " # Create a dictionary of words in the vocabulary mapped to index positions\n", + " self.word2index = {}\n", + " # TODO: populate self.word2index with indices for all the words in self.review_vocab\n", + " # like you saw earlier in the notebook\n", + " for i, word in enumerate(self.review_vocab):\n", + " self.word2index[word] = i\n", + " \n", + " # Create a dictionary of labels mapped to index positions\n", + " self.label2index = {}\n", + " # TODO: do the same thing you did for self.word2index and self.review_vocab, \n", + " # but for self.label2index and self.label_vocab instead\n", + " for i, labl in enumerate(self.label_vocab):\n", + " self.label2index[labl] = i\n", + " \n", + " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", + " # Store the number of nodes in input, hidden, and output layers.\n", + " self.input_nodes = input_nodes\n", + " self.hidden_nodes = hidden_nodes\n", + " self.output_nodes = output_nodes\n", + "\n", + " # Store the learning rate\n", + " self.learning_rate = learning_rate\n", + "\n", + " # Initialize weights\n", + " \n", + " # TODO: initialize self.weights_0_1 as a matrix of zeros. These are the weights between\n", + " # the input layer and the hidden layer.\n", + " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", + " \n", + " # TODO: initialize self.weights_1_2 as a matrix of random values. \n", + " # These are the weights between the hidden layer and the output layer.\n", + " self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n", + " (self.hidden_nodes, self.output_nodes)) # MARKED AS WRONG\n", + " \n", + " # TODO: Create the input layer, a two-dimensional matrix with shape \n", + " # 1 x input_nodes, with all values initialized to zero\n", + " self.layer_1 = np.zeros((1,hidden_nodes))\n", + " \n", + " \n", + "# def update_input_layer(self,review):\n", + "# # TODO: You can copy most of the code you wrote for update_input_layer \n", + "# # earlier in this notebook. \n", + "# #\n", + "# # However, MAKE SURE YOU CHANGE ALL VARIABLES TO REFERENCE\n", + "# # THE VERSIONS STORED IN THIS OBJECT, NOT THE GLOBAL OBJECTS.\n", + "# # For example, replace \"layer_0 *= 0\" with \"self.layer_0 *= 0\"\n", + "# self.layer_0 *= 0\n", + "# for word in review.split(\" \"):\n", + "# if(word in self.word2index.keys()):\n", + "# self.layer_0[0][self.word2index[word]] = 1\n", + " \n", + " def get_target_for_label(self,label):\n", + " # TODO: Copy the code you wrote for get_target_for_label \n", + " # earlier in this notebook. \n", + " \n", + " if(label == \"POSITIVE\"):\n", + " return 1\n", + " else:\n", + " return 0\n", + " \n", + " def sigmoid(self,x):\n", + " # TODO: Return the result of calculating the sigmoid activation function\n", + " # shown in the lectures\n", + " return 1/(1 + np.exp(-x))\n", + " \n", + " def sigmoid_output_2_derivative(self,output):\n", + " # TODO: Return the derivative of the sigmoid activation function, \n", + " # where \"output\" is the original output from the sigmoid fucntion \n", + " return output * (1 - output)\n", + "\n", + " def train(self, training_reviews_raw, training_labels):\n", + " \n", + " \n", + " training_reviews = list()\n", + " for review in training_reviews_raw:\n", + " indices = set()\n", + " for word in review.split(\" \"):\n", + " if(word in self.word2index.keys()):\n", + " indices.add(self.word2index[word])\n", + " training_reviews.append(list(indices))\n", + " \n", + " # make sure out we have a matching number of reviews and labels\n", + " assert(len(training_reviews) == len(training_labels))\n", + " \n", + " # Keep track of correct predictions to display accuracy during training \n", + " correct_so_far = 0\n", + " \n", + " # Remember when we started for printing time statistics\n", + " start = time.time()\n", + "\n", + " # loop through all the given reviews and run a forward and backward pass,\n", + " # updating weights for every item\n", + " for i in range(len(training_reviews)):\n", + " \n", + " # TODO: Get the next review and its correct label\n", + " \n", + " review = training_reviews[i]\n", + " label = training_labels[i]\n", + " \n", + " # TODO: Implement the forward pass through the network. \n", + " # That means use the given review to update the input layer, \n", + " # then calculate values for the hidden layer,\n", + " # and finally calculate the output layer.\n", + " # \n", + " # Do not use an activation function for the hidden layer,\n", + " # but use the sigmoid activation function for the output layer.\n", + " \n", + " # // Implementing Forward Bias // \n", + " self.layer_1 *= 0\n", + " \n", + " \n", + " ''' Hidden Layer '''\n", + " for index in review:\n", + " self.layer_1 += self.weights_0_1[index]\n", + " \n", + " ''' Output Layer '''\n", + " \n", + " layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))\n", + " \n", + " \n", + " \n", + " # TODO: Implement the back propagation pass here. \n", + " # That means calculate the error for the forward pass's prediction\n", + " # and update the weights in the network according to their\n", + " # contributions toward the error, as calculated via the\n", + " # gradient descent and back propagation algorithms you \n", + " # learned in class.\n", + " \n", + " # // Implemnting Back Propagation //\n", + " ''' Output layer is difference between output came and actual output '''\n", + " layer_2_error = layer_2 - self.get_target_for_label(label)\n", + " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2) #MARKED WRONG\n", + " \n", + " ''' Backpropagated errors '''\n", + " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated in to hidden layer\n", + " layer_1_delta = layer_1_error # hidden_layer gradients - no nonliearity so it's the same as the error\n", + " \n", + " ''' Updating the weights '''\n", + " ## New for Project 5: changed to use 'self.layer_1' instead of local 'layer_1'\n", + " self.weights_1_2 -= self.layer_1.T.dot(layer_2_delta)*self.learning_rate\n", + " \n", + " ## New for Project 5: Only update the weights that were used in the forward pass\n", + " for index in review:\n", + " self.weights_0_1[index] -= layer_1_delta[0] * self.learning_rate\n", + " \n", + " \n", + " \n", + " # TODO: Keep track of correct predictions. To determine if the prediction was\n", + " # correct, check that the absolute value of the output error \n", + " # is less than 0.5. If so, add one to the correct_so_far count.\n", + " \n", + " if(layer_2 >= 0.5 and label == 'POSITIVE'):\n", + " correct_so_far += 1\n", + " elif(layer_2 < 0.5 and label == 'NEGATIVE'):\n", + " correct_so_far += 1\n", + " \n", + " \n", + " # For debug purposes, print out our prediction accuracy and speed \n", + " # throughout the training process. \n", + "\n", + " elapsed_time = float(time.time() - start)\n", + " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", + " \n", + " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] \\\n", + " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", + " + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) \\\n", + " + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", + " if(i % 2500 == 0):\n", + " print(\"\")\n", + " \n", + " def test(self, testing_reviews, testing_labels):\n", + " \"\"\"\n", + " Attempts to predict the labels for the given testing_reviews,\n", + " and uses the test_labels to calculate the accuracy of those predictions.\n", + " \"\"\"\n", + " \n", + " # keep track of how many correct predictions we make\n", + " correct = 0\n", + "\n", + " # we'll time how many predictions per second we make\n", + " start = time.time()\n", + "\n", + " # Loop through each of the given reviews and call run to predict\n", + " # its label. \n", + " for i in range(len(testing_reviews)):\n", + " pred = self.run(testing_reviews[i])\n", + " if(pred == testing_labels[i]):\n", + " correct += 1\n", + " \n", + " # For debug purposes, print out our prediction accuracy and speed \n", + " # throughout the prediction process. \n", + "\n", + " elapsed_time = float(time.time() - start)\n", + " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", + " \n", + " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", + " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", + " + \" #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) \\\n", + " + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", + " \n", + " def run(self, review):\n", + " \"\"\"\n", + " Returns a POSITIVE or NEGATIVE prediction for the given review.\n", + " \"\"\"\n", + " # TODO: Run a forward pass through the network, like you did in the\n", + " # \"train\" function. That means use the given review to \n", + " # update the input layer, then calculate values for the hidden layer,\n", + " # and finally calculate the output layer.\n", + " # // Implementing Forward Bias // \n", + " \n", + " ''' Hidden Layer '''\n", + " ## New for Project 5: Identify the indices used in the review and then add\n", + " # just those weights to layer_1 \n", + " self.layer_1 *= 0\n", + " unique_indices = set()\n", + " for word in review.lower().split(\" \"):\n", + " if word in self.word2index.keys():\n", + " unique_indices.add(self.word2index[word])\n", + " for index in unique_indices:\n", + " self.layer_1 += self.weights_0_1[index]\n", + " \n", + " \n", + " ''' Output Layer '''\n", + " layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))\n", + " \n", + " \n", + " #\n", + " # Note: The review passed into this function for prediction \n", + " # might come from anywhere, so you should convert it \n", + " # to lower case prior to using it.\n", + " \n", + " # TODO: The output layer should now contain a prediction. \n", + " # Return `POSITIVE` for predictions greater-than-or-equal-to `0.5`, \n", + " # and `NEGATIVE` otherwise.\n", + " if(layer_2[0] >= 0.5):\n", + " return \"POSITIVE\"\n", + " else:\n", + " return \"NEGATIVE\"\n", + " \n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to recreate the network and train it once again." + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.00% Speed(reviews/sec):698.2 #Correct:1 #Trained:2 Training Accuracy:50.0%\r", + "Progress:0.00% Speed(reviews/sec):761.5 #Correct:2 #Trained:3 Training Accuracy:66.6%\r", + "Progress:0.01% Speed(reviews/sec):622.3 #Correct:2 #Trained:4 Training Accuracy:50.0%\r", + "Progress:0.01% Speed(reviews/sec):732.6 #Correct:3 #Trained:5 Training Accuracy:60.0%\r", + "Progress:0.02% Speed(reviews/sec):832.9 #Correct:3 #Trained:6 Training Accuracy:50.0%\r", + "Progress:0.02% Speed(reviews/sec):936.0 #Correct:3 #Trained:7 Training Accuracy:42.8%\r", + "Progress:0.02% Speed(reviews/sec):1013. #Correct:3 #Trained:8 Training Accuracy:37.5%\r", + "Progress:0.03% Speed(reviews/sec):1098. #Correct:4 #Trained:9 Training Accuracy:44.4%\r", + "Progress:0.03% Speed(reviews/sec):1083. #Correct:4 #Trained:10 Training Accuracy:40.0%\r", + "Progress:0.04% Speed(reviews/sec):1135. #Correct:5 #Trained:11 Training Accuracy:45.4%\r", + "Progress:0.04% Speed(reviews/sec):1198. #Correct:5 #Trained:12 Training Accuracy:41.6%\r", + "Progress:0.05% Speed(reviews/sec):1257. #Correct:5 #Trained:13 Training Accuracy:38.4%\r", + "Progress:0.05% Speed(reviews/sec):1244. #Correct:5 #Trained:14 Training Accuracy:35.7%\r", + "Progress:0.05% Speed(reviews/sec):1195. #Correct:5 #Trained:15 Training Accuracy:33.3%\r", + "Progress:0.06% Speed(reviews/sec):1222. #Correct:6 #Trained:16 Training Accuracy:37.5%\r", + "Progress:0.06% Speed(reviews/sec):1201. #Correct:6 #Trained:17 Training Accuracy:35.2%\r", + "Progress:0.07% Speed(reviews/sec):1252. #Correct:6 #Trained:18 Training Accuracy:33.3%\r", + "Progress:0.07% Speed(reviews/sec):1252. #Correct:6 #Trained:19 Training Accuracy:31.5%\r", + "Progress:0.07% Speed(reviews/sec):1266. #Correct:7 #Trained:20 Training Accuracy:35.0%\r", + "Progress:0.08% Speed(reviews/sec):1275. #Correct:7 #Trained:21 Training Accuracy:33.3%\r", + "Progress:0.08% Speed(reviews/sec):1277. #Correct:7 #Trained:22 Training Accuracy:31.8%\r", + "Progress:0.09% Speed(reviews/sec):1318. #Correct:7 #Trained:23 Training Accuracy:30.4%\r", + "Progress:0.09% Speed(reviews/sec):1341. #Correct:8 #Trained:24 Training Accuracy:33.3%\r", + "Progress:0.1% Speed(reviews/sec):1340. #Correct:8 #Trained:25 Training Accuracy:32.0%\r", + "Progress:0.10% Speed(reviews/sec):1370. #Correct:9 #Trained:26 Training Accuracy:34.6%\r", + "Progress:0.10% Speed(reviews/sec):1383. #Correct:9 #Trained:27 Training Accuracy:33.3%\r", + "Progress:0.11% Speed(reviews/sec):1408. #Correct:10 #Trained:28 Training Accuracy:35.7%\r", + "Progress:0.11% Speed(reviews/sec):1405. #Correct:10 #Trained:29 Training Accuracy:34.4%\r", + "Progress:0.12% Speed(reviews/sec):1422. #Correct:11 #Trained:30 Training Accuracy:36.6%\r", + "Progress:0.12% Speed(reviews/sec):1439. #Correct:11 #Trained:31 Training Accuracy:35.4%\r", + "Progress:0.12% Speed(reviews/sec):1452. #Correct:12 #Trained:32 Training Accuracy:37.5%\r", + "Progress:0.13% Speed(reviews/sec):1461. #Correct:12 #Trained:33 Training Accuracy:36.3%\r", + "Progress:0.13% Speed(reviews/sec):1484. #Correct:13 #Trained:34 Training Accuracy:38.2%\r", + "Progress:0.14% Speed(reviews/sec):1472. #Correct:13 #Trained:35 Training Accuracy:37.1%\r", + "Progress:0.14% Speed(reviews/sec):1497. #Correct:14 #Trained:36 Training Accuracy:38.8%\r", + "Progress:0.15% Speed(reviews/sec):1517. #Correct:14 #Trained:37 Training Accuracy:37.8%\r", + "Progress:0.15% Speed(reviews/sec):1535. #Correct:15 #Trained:38 Training Accuracy:39.4%\r", + "Progress:0.15% Speed(reviews/sec):1552. #Correct:15 #Trained:39 Training Accuracy:38.4%\r", + "Progress:0.16% Speed(reviews/sec):1560. #Correct:16 #Trained:40 Training Accuracy:40.0%\r", + "Progress:0.16% Speed(reviews/sec):1564. #Correct:16 #Trained:41 Training Accuracy:39.0%\r", + "Progress:0.17% Speed(reviews/sec):1597. #Correct:17 #Trained:42 Training Accuracy:40.4%\r", + "Progress:0.17% Speed(reviews/sec):1569. #Correct:17 #Trained:43 Training Accuracy:39.5%\r", + "Progress:0.17% Speed(reviews/sec):1554. #Correct:18 #Trained:44 Training Accuracy:40.9%\r", + "Progress:0.18% Speed(reviews/sec):1561. #Correct:18 #Trained:45 Training Accuracy:40.0%\r", + "Progress:0.18% Speed(reviews/sec):1572. #Correct:19 #Trained:46 Training Accuracy:41.3%\r", + "Progress:0.19% Speed(reviews/sec):1574. #Correct:19 #Trained:47 Training Accuracy:40.4%\r", + "Progress:0.19% Speed(reviews/sec):1582. #Correct:20 #Trained:48 Training Accuracy:41.6%\r", + "Progress:0.2% Speed(reviews/sec):1595. #Correct:20 #Trained:49 Training Accuracy:40.8%\r", + "Progress:0.20% Speed(reviews/sec):1610. #Correct:21 #Trained:50 Training Accuracy:42.0%\r", + "Progress:0.20% Speed(reviews/sec):1625. #Correct:21 #Trained:51 Training Accuracy:41.1%\r", + "Progress:0.21% Speed(reviews/sec):1578. #Correct:22 #Trained:52 Training Accuracy:42.3%\r", + "Progress:0.21% Speed(reviews/sec):1594. #Correct:22 #Trained:53 Training Accuracy:41.5%\r", + "Progress:0.22% Speed(reviews/sec):1596. #Correct:23 #Trained:54 Training Accuracy:42.5%\r", + "Progress:0.22% Speed(reviews/sec):1592. #Correct:23 #Trained:55 Training Accuracy:41.8%\r", + "Progress:0.22% Speed(reviews/sec):1599. #Correct:24 #Trained:56 Training Accuracy:42.8%\r", + "Progress:0.23% Speed(reviews/sec):1612. #Correct:24 #Trained:57 Training Accuracy:42.1%\r", + "Progress:0.23% Speed(reviews/sec):1596. #Correct:25 #Trained:58 Training Accuracy:43.1%\r", + "Progress:0.24% Speed(reviews/sec):1606. #Correct:25 #Trained:59 Training Accuracy:42.3%\r", + "Progress:0.24% Speed(reviews/sec):1613. #Correct:26 #Trained:60 Training Accuracy:43.3%\r", + "Progress:0.25% Speed(reviews/sec):1618. #Correct:26 #Trained:61 Training Accuracy:42.6%\r", + "Progress:0.25% Speed(reviews/sec):1630. #Correct:27 #Trained:62 Training Accuracy:43.5%\r", + "Progress:0.25% Speed(reviews/sec):1644. #Correct:27 #Trained:63 Training Accuracy:42.8%\r", + "Progress:0.26% Speed(reviews/sec):1642. #Correct:28 #Trained:64 Training Accuracy:43.7%\r", + "Progress:0.26% Speed(reviews/sec):1652. #Correct:28 #Trained:65 Training Accuracy:43.0%\r", + "Progress:0.27% Speed(reviews/sec):1650. #Correct:29 #Trained:66 Training Accuracy:43.9%\r", + "Progress:0.27% Speed(reviews/sec):1655. #Correct:29 #Trained:67 Training Accuracy:43.2%\r", + "Progress:0.27% Speed(reviews/sec):1664. #Correct:30 #Trained:68 Training Accuracy:44.1%\r", + "Progress:0.28% Speed(reviews/sec):1658. #Correct:30 #Trained:69 Training Accuracy:43.4%\r", + "Progress:0.28% Speed(reviews/sec):1643. #Correct:31 #Trained:70 Training Accuracy:44.2%\r", + "Progress:0.29% Speed(reviews/sec):1638. #Correct:31 #Trained:71 Training Accuracy:43.6%\r", + "Progress:0.29% Speed(reviews/sec):1636. #Correct:32 #Trained:72 Training Accuracy:44.4%\r", + "Progress:0.3% Speed(reviews/sec):1652. #Correct:32 #Trained:73 Training Accuracy:43.8%\r", + "Progress:0.30% Speed(reviews/sec):1656. #Correct:33 #Trained:74 Training Accuracy:44.5%\r", + "Progress:0.30% Speed(reviews/sec):1655. #Correct:33 #Trained:75 Training Accuracy:44.0%\r", + "Progress:0.31% Speed(reviews/sec):1660. #Correct:34 #Trained:76 Training Accuracy:44.7%\r", + "Progress:0.31% Speed(reviews/sec):1663. #Correct:34 #Trained:77 Training Accuracy:44.1%\r", + "Progress:0.32% Speed(reviews/sec):1663. #Correct:35 #Trained:78 Training Accuracy:44.8%\r", + "Progress:0.32% Speed(reviews/sec):1655. #Correct:35 #Trained:79 Training Accuracy:44.3%\r", + "Progress:0.32% Speed(reviews/sec):1663. #Correct:36 #Trained:80 Training Accuracy:45.0%\r", + "Progress:0.33% Speed(reviews/sec):1660. #Correct:36 #Trained:81 Training Accuracy:44.4%\r", + "Progress:0.33% Speed(reviews/sec):1664. #Correct:37 #Trained:82 Training Accuracy:45.1%\r", + "Progress:0.34% Speed(reviews/sec):1671. #Correct:37 #Trained:83 Training Accuracy:44.5%\r", + "Progress:0.34% Speed(reviews/sec):1680. #Correct:38 #Trained:84 Training Accuracy:45.2%\r", + "Progress:0.35% Speed(reviews/sec):1670. #Correct:38 #Trained:85 Training Accuracy:44.7%\r", + "Progress:0.35% Speed(reviews/sec):1679. #Correct:39 #Trained:86 Training Accuracy:45.3%\r", + "Progress:0.35% Speed(reviews/sec):1681. #Correct:39 #Trained:87 Training Accuracy:44.8%\r", + "Progress:0.36% Speed(reviews/sec):1683. #Correct:40 #Trained:88 Training Accuracy:45.4%\r", + "Progress:0.36% Speed(reviews/sec):1692. #Correct:40 #Trained:89 Training Accuracy:44.9%\r", + "Progress:0.37% Speed(reviews/sec):1690. #Correct:41 #Trained:90 Training Accuracy:45.5%\r", + "Progress:0.37% Speed(reviews/sec):1687. #Correct:41 #Trained:91 Training Accuracy:45.0%\r", + "Progress:0.37% Speed(reviews/sec):1686. #Correct:42 #Trained:92 Training Accuracy:45.6%\r", + "Progress:0.38% Speed(reviews/sec):1661. #Correct:42 #Trained:93 Training Accuracy:45.1%\r", + "Progress:0.38% Speed(reviews/sec):1640. #Correct:43 #Trained:94 Training Accuracy:45.7%\r", + "Progress:0.39% Speed(reviews/sec):1623. #Correct:43 #Trained:95 Training Accuracy:45.2%\r", + "Progress:0.39% Speed(reviews/sec):1627. #Correct:44 #Trained:96 Training Accuracy:45.8%\r", + "Progress:0.4% Speed(reviews/sec):1637. #Correct:44 #Trained:97 Training Accuracy:45.3%\r", + "Progress:0.40% Speed(reviews/sec):1632. #Correct:44 #Trained:98 Training Accuracy:44.8%\r", + "Progress:0.40% Speed(reviews/sec):1587. #Correct:44 #Trained:99 Training Accuracy:44.4%\r", + "Progress:0.41% Speed(reviews/sec):1595. #Correct:45 #Trained:100 Training Accuracy:45.0%\r", + "Progress:0.41% Speed(reviews/sec):1592. #Correct:45 #Trained:101 Training Accuracy:44.5%\r", + "Progress:0.42% Speed(reviews/sec):1600. #Correct:46 #Trained:102 Training Accuracy:45.0%\r", + "Progress:0.42% Speed(reviews/sec):1591. #Correct:46 #Trained:103 Training Accuracy:44.6%\r", + "Progress:0.42% Speed(reviews/sec):1596. #Correct:47 #Trained:104 Training Accuracy:45.1%\r", + "Progress:0.43% Speed(reviews/sec):1601. #Correct:47 #Trained:105 Training Accuracy:44.7%\r", + "Progress:0.43% Speed(reviews/sec):1605. #Correct:48 #Trained:106 Training Accuracy:45.2%\r", + "Progress:0.44% Speed(reviews/sec):1595. #Correct:48 #Trained:107 Training Accuracy:44.8%\r", + "Progress:0.44% Speed(reviews/sec):1582. #Correct:49 #Trained:108 Training Accuracy:45.3%\r", + "Progress:0.45% Speed(reviews/sec):1585. #Correct:49 #Trained:109 Training Accuracy:44.9%\r", + "Progress:0.45% Speed(reviews/sec):1539. #Correct:50 #Trained:110 Training Accuracy:45.4%\r", + "Progress:0.45% Speed(reviews/sec):1522. #Correct:50 #Trained:111 Training Accuracy:45.0%\r", + "Progress:0.46% Speed(reviews/sec):1523. #Correct:51 #Trained:112 Training Accuracy:45.5%\r", + "Progress:0.46% Speed(reviews/sec):1527. #Correct:51 #Trained:113 Training Accuracy:45.1%\r", + "Progress:0.47% Speed(reviews/sec):1531. #Correct:52 #Trained:114 Training Accuracy:45.6%\r", + "Progress:0.47% Speed(reviews/sec):1537. #Correct:52 #Trained:115 Training Accuracy:45.2%\r", + "Progress:0.47% Speed(reviews/sec):1540. #Correct:52 #Trained:116 Training Accuracy:44.8%\r", + "Progress:0.48% Speed(reviews/sec):1549. #Correct:52 #Trained:117 Training Accuracy:44.4%\r", + "Progress:0.48% Speed(reviews/sec):1551. #Correct:53 #Trained:118 Training Accuracy:44.9%\r", + "Progress:0.49% Speed(reviews/sec):1551. #Correct:53 #Trained:119 Training Accuracy:44.5%\r", + "Progress:0.49% Speed(reviews/sec):1555. #Correct:54 #Trained:120 Training Accuracy:45.0%\r", + "Progress:0.5% Speed(reviews/sec):1563. #Correct:54 #Trained:121 Training Accuracy:44.6%\r", + "Progress:0.50% Speed(reviews/sec):1568. #Correct:54 #Trained:122 Training Accuracy:44.2%\r", + "Progress:0.50% Speed(reviews/sec):1571. #Correct:54 #Trained:123 Training Accuracy:43.9%\r", + "Progress:0.51% Speed(reviews/sec):1569. #Correct:55 #Trained:124 Training Accuracy:44.3%\r", + "Progress:0.51% Speed(reviews/sec):1576. #Correct:55 #Trained:125 Training Accuracy:44.0%\r", + "Progress:0.52% Speed(reviews/sec):1578. #Correct:56 #Trained:126 Training Accuracy:44.4%\r", + "Progress:0.52% Speed(reviews/sec):1581. #Correct:56 #Trained:127 Training Accuracy:44.0%\r", + "Progress:0.52% Speed(reviews/sec):1566. #Correct:57 #Trained:128 Training Accuracy:44.5%\r", + "Progress:0.53% Speed(reviews/sec):1564. #Correct:57 #Trained:129 Training Accuracy:44.1%\r", 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Speed(reviews/sec):1595. #Correct:64 #Trained:140 Training Accuracy:45.7%\r", + "Progress:0.58% Speed(reviews/sec):1598. #Correct:65 #Trained:141 Training Accuracy:46.0%\r", + "Progress:0.58% Speed(reviews/sec):1602. #Correct:66 #Trained:142 Training Accuracy:46.4%\r", + "Progress:0.59% Speed(reviews/sec):1603. #Correct:67 #Trained:143 Training Accuracy:46.8%\r", + "Progress:0.59% Speed(reviews/sec):1606. #Correct:68 #Trained:144 Training Accuracy:47.2%\r", + "Progress:0.6% Speed(reviews/sec):1605. #Correct:68 #Trained:145 Training Accuracy:46.8%\r", + "Progress:0.60% Speed(reviews/sec):1612. #Correct:68 #Trained:146 Training Accuracy:46.5%\r", + "Progress:0.60% Speed(reviews/sec):1612. #Correct:69 #Trained:147 Training Accuracy:46.9%\r", + "Progress:0.61% Speed(reviews/sec):1616. #Correct:70 #Trained:148 Training Accuracy:47.2%\r", + "Progress:0.61% Speed(reviews/sec):1624. #Correct:71 #Trained:149 Training Accuracy:47.6%\r", + "Progress:0.62% Speed(reviews/sec):1609. #Correct:72 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#Correct:155 #Trained:263 Training Accuracy:58.9%\r", + "Progress:1.09% Speed(reviews/sec):1724. #Correct:155 #Trained:264 Training Accuracy:58.7%\r", + "Progress:1.1% Speed(reviews/sec):1725. #Correct:156 #Trained:265 Training Accuracy:58.8%\r", + "Progress:1.10% Speed(reviews/sec):1727. #Correct:157 #Trained:266 Training Accuracy:59.0%\r", + "Progress:1.10% Speed(reviews/sec):1730. #Correct:158 #Trained:267 Training Accuracy:59.1%\r", + "Progress:1.11% Speed(reviews/sec):1731. #Correct:159 #Trained:268 Training Accuracy:59.3%\r", + "Progress:1.11% Speed(reviews/sec):1729. #Correct:160 #Trained:269 Training Accuracy:59.4%\r", + "Progress:1.12% Speed(reviews/sec):1731. #Correct:160 #Trained:270 Training Accuracy:59.2%\r", + "Progress:1.12% Speed(reviews/sec):1733. #Correct:161 #Trained:271 Training Accuracy:59.4%\r", + "Progress:1.12% Speed(reviews/sec):1736. #Correct:161 #Trained:272 Training Accuracy:59.1%\r", + "Progress:1.13% Speed(reviews/sec):1738. #Correct:162 #Trained:273 Training Accuracy:59.3%\r", + "Progress:1.13% Speed(reviews/sec):1738. #Correct:162 #Trained:274 Training Accuracy:59.1%\r", + "Progress:1.14% Speed(reviews/sec):1739. #Correct:163 #Trained:275 Training Accuracy:59.2%\r", + "Progress:1.14% Speed(reviews/sec):1732. #Correct:164 #Trained:276 Training Accuracy:59.4%\r", + "Progress:1.15% Speed(reviews/sec):1726. #Correct:164 #Trained:277 Training Accuracy:59.2%\r", + "Progress:1.15% Speed(reviews/sec):1729. #Correct:165 #Trained:278 Training Accuracy:59.3%\r", + "Progress:1.15% Speed(reviews/sec):1729. #Correct:166 #Trained:279 Training Accuracy:59.4%\r", + "Progress:1.16% Speed(reviews/sec):1724. #Correct:167 #Trained:280 Training Accuracy:59.6%\r", + "Progress:1.16% Speed(reviews/sec):1723. #Correct:168 #Trained:281 Training Accuracy:59.7%\r", + "Progress:1.17% Speed(reviews/sec):1716. #Correct:168 #Trained:282 Training Accuracy:59.5%\r", + "Progress:1.17% Speed(reviews/sec):1716. #Correct:168 #Trained:283 Training Accuracy:59.3%\r", + 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Speed(reviews/sec):1615. #Correct:1964 #Trained:2698 Training Accuracy:72.7%\r", + "Progress:11.2% Speed(reviews/sec):1615. #Correct:1964 #Trained:2699 Training Accuracy:72.7%\r", + "Progress:11.2% Speed(reviews/sec):1615. #Correct:1965 #Trained:2700 Training Accuracy:72.7%\r", + "Progress:11.2% Speed(reviews/sec):1615. #Correct:1966 #Trained:2701 Training Accuracy:72.7%\r", + "Progress:11.2% Speed(reviews/sec):1616. #Correct:1967 #Trained:2702 Training Accuracy:72.7%\r", + "Progress:11.2% Speed(reviews/sec):1616. #Correct:1967 #Trained:2703 Training Accuracy:72.7%\r", + "Progress:11.2% Speed(reviews/sec):1616. #Correct:1968 #Trained:2704 Training Accuracy:72.7%\r", + "Progress:11.2% Speed(reviews/sec):1616. #Correct:1969 #Trained:2705 Training Accuracy:72.7%\r", + "Progress:11.2% Speed(reviews/sec):1616. #Correct:1970 #Trained:2706 Training Accuracy:72.8%\r", + "Progress:11.2% Speed(reviews/sec):1616. #Correct:1971 #Trained:2707 Training Accuracy:72.8%\r", + "Progress:11.2% Speed(reviews/sec):1616. #Correct:1972 #Trained:2708 Training Accuracy:72.8%\r", + "Progress:11.2% Speed(reviews/sec):1616. #Correct:1973 #Trained:2709 Training Accuracy:72.8%\r", + "Progress:11.2% Speed(reviews/sec):1616. #Correct:1974 #Trained:2710 Training Accuracy:72.8%\r", + "Progress:11.2% Speed(reviews/sec):1616. #Correct:1975 #Trained:2711 Training Accuracy:72.8%\r", + "Progress:11.2% Speed(reviews/sec):1616. #Correct:1975 #Trained:2712 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1616. #Correct:1976 #Trained:2713 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1617. #Correct:1977 #Trained:2714 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1617. #Correct:1978 #Trained:2715 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1617. #Correct:1978 #Trained:2716 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1618. #Correct:1979 #Trained:2717 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1618. #Correct:1980 #Trained:2718 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1618. #Correct:1981 #Trained:2719 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1618. #Correct:1982 #Trained:2720 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1619. #Correct:1983 #Trained:2721 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1618. #Correct:1984 #Trained:2722 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1619. #Correct:1985 #Trained:2723 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1619. #Correct:1986 #Trained:2724 Training Accuracy:72.9%\r", + "Progress:11.3% Speed(reviews/sec):1619. #Correct:1987 #Trained:2725 Training Accuracy:72.9%\r", + "Progress:11.3% Speed(reviews/sec):1619. #Correct:1988 #Trained:2726 Training Accuracy:72.9%\r", + "Progress:11.3% Speed(reviews/sec):1619. #Correct:1989 #Trained:2727 Training Accuracy:72.9%\r", + "Progress:11.3% Speed(reviews/sec):1619. #Correct:1990 #Trained:2728 Training Accuracy:72.9%\r", + "Progress:11.3% Speed(reviews/sec):1619. #Correct:1990 #Trained:2729 Training Accuracy:72.9%\r", + "Progress:11.3% Speed(reviews/sec):1619. #Correct:1990 #Trained:2730 Training Accuracy:72.8%\r", + "Progress:11.3% Speed(reviews/sec):1619. #Correct:1991 #Trained:2731 Training Accuracy:72.9%\r", + "Progress:11.3% Speed(reviews/sec):1620. #Correct:1992 #Trained:2732 Training Accuracy:72.9%\r", + "Progress:11.3% Speed(reviews/sec):1620. #Correct:1993 #Trained:2733 Training Accuracy:72.9%\r", + "Progress:11.3% Speed(reviews/sec):1620. #Correct:1994 #Trained:2734 Training Accuracy:72.9%\r", + "Progress:11.3% Speed(reviews/sec):1620. #Correct:1995 #Trained:2735 Training Accuracy:72.9%\r", + "Progress:11.3% Speed(reviews/sec):1621. #Correct:1996 #Trained:2736 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1621. #Correct:1997 #Trained:2737 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1621. #Correct:1998 #Trained:2738 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1621. #Correct:1999 #Trained:2739 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1621. #Correct:1999 #Trained:2740 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1621. #Correct:2000 #Trained:2741 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1621. #Correct:2000 #Trained:2742 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1622. #Correct:2000 #Trained:2743 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1622. #Correct:2001 #Trained:2744 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1622. #Correct:2002 #Trained:2745 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1619. #Correct:2003 #Trained:2746 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1618. #Correct:2004 #Trained:2747 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1618. #Correct:2005 #Trained:2748 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1619. #Correct:2006 #Trained:2749 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1618. #Correct:2006 #Trained:2750 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1618. #Correct:2007 #Trained:2751 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1618. #Correct:2008 #Trained:2752 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1618. #Correct:2009 #Trained:2753 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1618. #Correct:2010 #Trained:2754 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1618. #Correct:2011 #Trained:2755 Training Accuracy:72.9%\r", + "Progress:11.4% Speed(reviews/sec):1618. #Correct:2012 #Trained:2756 Training Accuracy:73.0%\r", + "Progress:11.4% Speed(reviews/sec):1619. #Correct:2013 #Trained:2757 Training Accuracy:73.0%\r", + "Progress:11.4% Speed(reviews/sec):1618. #Correct:2014 #Trained:2758 Training Accuracy:73.0%\r", + "Progress:11.4% Speed(reviews/sec):1619. #Correct:2015 #Trained:2759 Training Accuracy:73.0%\r", + "Progress:11.4% Speed(reviews/sec):1619. #Correct:2016 #Trained:2760 Training Accuracy:73.0%\r", + "Progress:11.5% Speed(reviews/sec):1619. #Correct:2017 #Trained:2761 Training Accuracy:73.0%\r", + "Progress:11.5% Speed(reviews/sec):1619. #Correct:2018 #Trained:2762 Training Accuracy:73.0%\r", + "Progress:11.5% Speed(reviews/sec):1619. #Correct:2019 #Trained:2763 Training Accuracy:73.0%\r", + "Progress:11.5% Speed(reviews/sec):1619. #Correct:2020 #Trained:2764 Training Accuracy:73.0%\r", + "Progress:11.5% Speed(reviews/sec):1619. #Correct:2021 #Trained:2765 Training Accuracy:73.0%\r", + "Progress:11.5% Speed(reviews/sec):1619. #Correct:2022 #Trained:2766 Training Accuracy:73.1%\r", + "Progress:11.5% Speed(reviews/sec):1619. #Correct:2023 #Trained:2767 Training Accuracy:73.1%\r", + "Progress:11.5% 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"source": [ + "That should have trained much better than the earlier attempts. Run the following cell to test your model with 1000 predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Tested:1 Testing Accuracy:100.%\r", + "Progress:0.1% Speed(reviews/sec):692.3 #Correct:2 #Tested:2 Testing Accuracy:100.%\r", + "Progress:0.2% Speed(reviews/sec):1113. #Correct:3 #Tested:3 Testing Accuracy:100.%\r", + "Progress:0.3% Speed(reviews/sec):1226. #Correct:4 #Tested:4 Testing Accuracy:100.%\r", + "Progress:0.4% Speed(reviews/sec):1457. #Correct:5 #Tested:5 Testing Accuracy:100.%\r", + "Progress:0.5% Speed(reviews/sec):1441. #Correct:6 #Tested:6 Testing Accuracy:100.%\r", + "Progress:0.6% Speed(reviews/sec):1313. #Correct:7 #Tested:7 Testing Accuracy:100.%\r", + "Progress:0.7% Speed(reviews/sec):1297. #Correct:8 #Tested:8 Testing Accuracy:100.%\r", + "Progress:0.8% Speed(reviews/sec):1320. #Correct:9 #Tested:9 Testing Accuracy:100.%\r", + "Progress:0.9% Speed(reviews/sec):1042. #Correct:10 #Tested:10 Testing Accuracy:100.%\r", + "Progress:1.0% Speed(reviews/sec):1053. #Correct:11 #Tested:11 Testing Accuracy:100.%\r", + "Progress:1.1% Speed(reviews/sec):1101. #Correct:12 #Tested:12 Testing Accuracy:100.%\r", + "Progress:1.2% Speed(reviews/sec):1128. #Correct:13 #Tested:13 Testing Accuracy:100.%\r", + "Progress:1.3% Speed(reviews/sec):1161. #Correct:14 #Tested:14 Testing Accuracy:100.%\r", + "Progress:1.4% Speed(reviews/sec):1187. #Correct:15 #Tested:15 Testing Accuracy:100.%\r", + "Progress:1.5% Speed(reviews/sec):1216. #Correct:16 #Tested:16 Testing Accuracy:100.%\r", + "Progress:1.6% Speed(reviews/sec):1134. #Correct:17 #Tested:17 Testing Accuracy:100.%\r", + "Progress:1.7% Speed(reviews/sec):1133. #Correct:18 #Tested:18 Testing Accuracy:100.%\r", + "Progress:1.8% Speed(reviews/sec):1177. #Correct:19 #Tested:19 Testing Accuracy:100.%\r", + "Progress:1.9% 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Testing Accuracy:87.5%\r", + "Progress:57.8% Speed(reviews/sec):2528. #Correct:506 #Tested:579 Testing Accuracy:87.3%\r", + "Progress:57.9% Speed(reviews/sec):2530. #Correct:507 #Tested:580 Testing Accuracy:87.4%\r", + "Progress:58.0% Speed(reviews/sec):2530. #Correct:508 #Tested:581 Testing Accuracy:87.4%\r", + "Progress:58.1% Speed(reviews/sec):2532. #Correct:509 #Tested:582 Testing Accuracy:87.4%\r", + "Progress:58.2% Speed(reviews/sec):2534. #Correct:510 #Tested:583 Testing Accuracy:87.4%\r", + "Progress:58.3% Speed(reviews/sec):2536. #Correct:511 #Tested:584 Testing Accuracy:87.5%\r", + "Progress:58.4% Speed(reviews/sec):2538. #Correct:512 #Tested:585 Testing Accuracy:87.5%\r", + "Progress:58.5% Speed(reviews/sec):2540. #Correct:513 #Tested:586 Testing Accuracy:87.5%\r", + "Progress:58.6% Speed(reviews/sec):2542. #Correct:514 #Tested:587 Testing Accuracy:87.5%\r", + "Progress:58.7% Speed(reviews/sec):2543. #Correct:515 #Tested:588 Testing Accuracy:87.5%\r", + "Progress:58.8% 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Testing Accuracy:87.1%\r", + "Progress:59.9% Speed(reviews/sec):2562. #Correct:523 #Tested:600 Testing Accuracy:87.1%\r", + "Progress:60.0% Speed(reviews/sec):2562. #Correct:524 #Tested:601 Testing Accuracy:87.1%\r", + "Progress:60.1% Speed(reviews/sec):2564. #Correct:525 #Tested:602 Testing Accuracy:87.2%\r", + "Progress:60.2% Speed(reviews/sec):2566. #Correct:526 #Tested:603 Testing Accuracy:87.2%\r", + "Progress:60.3% Speed(reviews/sec):2569. #Correct:527 #Tested:604 Testing Accuracy:87.2%\r", + "Progress:60.4% Speed(reviews/sec):2570. #Correct:528 #Tested:605 Testing Accuracy:87.2%\r", + "Progress:60.5% Speed(reviews/sec):2566. #Correct:528 #Tested:606 Testing Accuracy:87.1%\r", + "Progress:60.6% Speed(reviews/sec):2568. #Correct:528 #Tested:607 Testing Accuracy:86.9%\r", + "Progress:60.7% Speed(reviews/sec):2569. #Correct:529 #Tested:608 Testing Accuracy:87.0%\r", + "Progress:60.8% Speed(reviews/sec):2572. #Correct:530 #Tested:609 Testing Accuracy:87.0%\r", + "Progress:60.9% 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Testing Accuracy:85.5%\r", + "Progress:68.3% Speed(reviews/sec):2659. #Correct:585 #Tested:684 Testing Accuracy:85.5%\r", + "Progress:68.4% Speed(reviews/sec):2660. #Correct:586 #Tested:685 Testing Accuracy:85.5%\r", + "Progress:68.5% Speed(reviews/sec):2662. #Correct:587 #Tested:686 Testing Accuracy:85.5%\r", + "Progress:68.6% Speed(reviews/sec):2664. #Correct:587 #Tested:687 Testing Accuracy:85.4%\r", + "Progress:68.7% Speed(reviews/sec):2665. #Correct:588 #Tested:688 Testing Accuracy:85.4%\r", + "Progress:68.8% Speed(reviews/sec):2663. #Correct:589 #Tested:689 Testing Accuracy:85.4%\r", + "Progress:68.9% Speed(reviews/sec):2664. #Correct:590 #Tested:690 Testing Accuracy:85.5%\r", + "Progress:69.0% Speed(reviews/sec):2665. #Correct:591 #Tested:691 Testing Accuracy:85.5%\r", + "Progress:69.1% Speed(reviews/sec):2667. #Correct:592 #Tested:692 Testing Accuracy:85.5%\r", + "Progress:69.2% Speed(reviews/sec):2668. #Correct:593 #Tested:693 Testing Accuracy:85.5%\r", + "Progress:69.3% 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Testing Accuracy:85.7%\r", + "Progress:70.4% Speed(reviews/sec):2679. #Correct:605 #Tested:705 Testing Accuracy:85.8%\r", + "Progress:70.5% Speed(reviews/sec):2681. #Correct:606 #Tested:706 Testing Accuracy:85.8%\r", + "Progress:70.6% Speed(reviews/sec):2682. #Correct:607 #Tested:707 Testing Accuracy:85.8%\r", + "Progress:70.7% Speed(reviews/sec):2684. #Correct:608 #Tested:708 Testing Accuracy:85.8%\r", + "Progress:70.8% Speed(reviews/sec):2684. #Correct:609 #Tested:709 Testing Accuracy:85.8%\r", + "Progress:70.9% Speed(reviews/sec):2685. #Correct:610 #Tested:710 Testing Accuracy:85.9%\r", + "Progress:71.0% Speed(reviews/sec):2687. #Correct:610 #Tested:711 Testing Accuracy:85.7%\r", + "Progress:71.1% Speed(reviews/sec):2688. #Correct:611 #Tested:712 Testing Accuracy:85.8%\r", + "Progress:71.2% Speed(reviews/sec):2691. #Correct:612 #Tested:713 Testing Accuracy:85.8%\r", + "Progress:71.3% Speed(reviews/sec):2691. #Correct:613 #Tested:714 Testing Accuracy:85.8%\r", + "Progress:71.4% 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Testing Accuracy:85.5%\r", + "Progress:72.5% Speed(reviews/sec):2708. #Correct:621 #Tested:726 Testing Accuracy:85.5%\r", + "Progress:72.6% Speed(reviews/sec):2706. #Correct:622 #Tested:727 Testing Accuracy:85.5%\r", + "Progress:72.7% Speed(reviews/sec):2704. #Correct:623 #Tested:728 Testing Accuracy:85.5%\r", + "Progress:72.8% Speed(reviews/sec):2707. #Correct:624 #Tested:729 Testing Accuracy:85.5%\r", + "Progress:72.9% Speed(reviews/sec):2704. #Correct:625 #Tested:730 Testing Accuracy:85.6%\r", + "Progress:73.0% Speed(reviews/sec):2706. #Correct:626 #Tested:731 Testing Accuracy:85.6%\r", + "Progress:73.1% Speed(reviews/sec):2707. #Correct:627 #Tested:732 Testing Accuracy:85.6%\r", + "Progress:73.2% Speed(reviews/sec):2707. #Correct:628 #Tested:733 Testing Accuracy:85.6%\r", + "Progress:73.3% Speed(reviews/sec):2706. #Correct:629 #Tested:734 Testing Accuracy:85.6%\r", + "Progress:73.4% Speed(reviews/sec):2705. #Correct:630 #Tested:735 Testing Accuracy:85.7%\r", + "Progress:73.5% 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Testing Accuracy:85.5%\r", + "Progress:74.6% Speed(reviews/sec):2716. #Correct:639 #Tested:747 Testing Accuracy:85.5%\r", + "Progress:74.7% Speed(reviews/sec):2713. #Correct:639 #Tested:748 Testing Accuracy:85.4%\r", + "Progress:74.8% Speed(reviews/sec):2713. #Correct:640 #Tested:749 Testing Accuracy:85.4%\r", + "Progress:74.9% Speed(reviews/sec):2711. #Correct:641 #Tested:750 Testing Accuracy:85.4%\r", + "Progress:75.0% Speed(reviews/sec):2713. #Correct:642 #Tested:751 Testing Accuracy:85.4%\r", + "Progress:75.1% Speed(reviews/sec):2714. #Correct:643 #Tested:752 Testing Accuracy:85.5%\r", + "Progress:75.2% Speed(reviews/sec):2715. #Correct:644 #Tested:753 Testing Accuracy:85.5%\r", + "Progress:75.3% Speed(reviews/sec):2717. #Correct:645 #Tested:754 Testing Accuracy:85.5%\r", + "Progress:75.4% Speed(reviews/sec):2715. #Correct:646 #Tested:755 Testing Accuracy:85.5%\r", + "Progress:75.5% Speed(reviews/sec):2717. #Correct:646 #Tested:756 Testing Accuracy:85.4%\r", + "Progress:75.6% 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Testing Accuracy:84.8%\r", + "Progress:76.7% Speed(reviews/sec):2726. #Correct:652 #Tested:768 Testing Accuracy:84.8%\r", + "Progress:76.8% Speed(reviews/sec):2728. #Correct:652 #Tested:769 Testing Accuracy:84.7%\r", + "Progress:76.9% Speed(reviews/sec):2729. #Correct:652 #Tested:770 Testing Accuracy:84.6%\r", + "Progress:77.0% Speed(reviews/sec):2727. #Correct:653 #Tested:771 Testing Accuracy:84.6%\r", + "Progress:77.1% Speed(reviews/sec):2728. #Correct:653 #Tested:772 Testing Accuracy:84.5%\r", + "Progress:77.2% Speed(reviews/sec):2730. #Correct:654 #Tested:773 Testing Accuracy:84.6%\r", + "Progress:77.3% Speed(reviews/sec):2730. #Correct:655 #Tested:774 Testing Accuracy:84.6%\r", + "Progress:77.4% Speed(reviews/sec):2732. #Correct:656 #Tested:775 Testing Accuracy:84.6%\r", + "Progress:77.5% Speed(reviews/sec):2731. #Correct:657 #Tested:776 Testing Accuracy:84.6%\r", + "Progress:77.6% Speed(reviews/sec):2731. #Correct:657 #Tested:777 Testing Accuracy:84.5%\r", + "Progress:77.7% 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Speed(reviews/sec):2797. #Correct:796 #Tested:945 Testing Accuracy:84.2%\r", + "Progress:94.5% Speed(reviews/sec):2797. #Correct:796 #Tested:946 Testing Accuracy:84.1%\r", + "Progress:94.6% Speed(reviews/sec):2795. #Correct:797 #Tested:947 Testing Accuracy:84.1%\r", + "Progress:94.7% Speed(reviews/sec):2793. #Correct:798 #Tested:948 Testing Accuracy:84.1%\r", + "Progress:94.8% Speed(reviews/sec):2792. #Correct:799 #Tested:949 Testing Accuracy:84.1%\r", + "Progress:94.9% Speed(reviews/sec):2793. #Correct:800 #Tested:950 Testing Accuracy:84.2%\r", + "Progress:95.0% Speed(reviews/sec):2793. #Correct:801 #Tested:951 Testing Accuracy:84.2%\r", + "Progress:95.1% Speed(reviews/sec):2704. #Correct:802 #Tested:952 Testing Accuracy:84.2%\r", + "Progress:95.2% Speed(reviews/sec):2688. #Correct:803 #Tested:953 Testing Accuracy:84.2%\r", + "Progress:95.3% Speed(reviews/sec):2688. #Correct:804 #Tested:954 Testing Accuracy:84.2%\r", + "Progress:95.4% Speed(reviews/sec):2684. #Correct:805 #Tested:955 Testing Accuracy:84.2%\r", + "Progress:95.5% Speed(reviews/sec):2684. #Correct:806 #Tested:956 Testing Accuracy:84.3%\r", + "Progress:95.6% Speed(reviews/sec):2685. #Correct:807 #Tested:957 Testing Accuracy:84.3%\r", + "Progress:95.7% Speed(reviews/sec):2686. #Correct:808 #Tested:958 Testing Accuracy:84.3%\r", + "Progress:95.8% Speed(reviews/sec):2687. #Correct:809 #Tested:959 Testing Accuracy:84.3%\r", + "Progress:95.9% Speed(reviews/sec):2689. #Correct:810 #Tested:960 Testing Accuracy:84.3%\r", + "Progress:96.0% Speed(reviews/sec):2686. #Correct:811 #Tested:961 Testing Accuracy:84.3%\r", + "Progress:96.1% Speed(reviews/sec):2688. #Correct:812 #Tested:962 Testing Accuracy:84.4%\r", + "Progress:96.2% Speed(reviews/sec):2689. #Correct:813 #Tested:963 Testing Accuracy:84.4%\r", + "Progress:96.3% Speed(reviews/sec):2690. #Correct:814 #Tested:964 Testing Accuracy:84.4%\r", + "Progress:96.4% Speed(reviews/sec):2689. #Correct:815 #Tested:965 Testing Accuracy:84.4%\r", + "Progress:96.5% Speed(reviews/sec):2690. #Correct:816 #Tested:966 Testing Accuracy:84.4%\r", + "Progress:96.6% Speed(reviews/sec):2690. #Correct:817 #Tested:967 Testing Accuracy:84.4%\r", + "Progress:96.7% Speed(reviews/sec):2692. #Correct:818 #Tested:968 Testing Accuracy:84.5%\r", + "Progress:96.8% Speed(reviews/sec):2691. #Correct:819 #Tested:969 Testing Accuracy:84.5%\r", + "Progress:96.9% Speed(reviews/sec):2693. #Correct:820 #Tested:970 Testing Accuracy:84.5%\r", + "Progress:97.0% Speed(reviews/sec):2690. #Correct:821 #Tested:971 Testing Accuracy:84.5%\r", + "Progress:97.1% Speed(reviews/sec):2689. #Correct:822 #Tested:972 Testing Accuracy:84.5%\r", + "Progress:97.2% Speed(reviews/sec):2690. #Correct:823 #Tested:973 Testing Accuracy:84.5%\r", + "Progress:97.3% Speed(reviews/sec):2689. #Correct:824 #Tested:974 Testing Accuracy:84.5%\r", + "Progress:97.4% Speed(reviews/sec):2688. #Correct:825 #Tested:975 Testing Accuracy:84.6%\r", + "Progress:97.5% Speed(reviews/sec):2688. #Correct:826 #Tested:976 Testing Accuracy:84.6%\r", + "Progress:97.6% Speed(reviews/sec):2689. #Correct:827 #Tested:977 Testing Accuracy:84.6%\r", + "Progress:97.7% Speed(reviews/sec):2685. #Correct:827 #Tested:978 Testing Accuracy:84.5%\r", + "Progress:97.8% Speed(reviews/sec):2685. #Correct:828 #Tested:979 Testing Accuracy:84.5%\r", + "Progress:97.9% Speed(reviews/sec):2683. #Correct:829 #Tested:980 Testing Accuracy:84.5%\r", + "Progress:98.0% Speed(reviews/sec):2685. #Correct:830 #Tested:981 Testing Accuracy:84.6%\r", + "Progress:98.1% Speed(reviews/sec):2680. #Correct:831 #Tested:982 Testing Accuracy:84.6%\r", + "Progress:98.2% Speed(reviews/sec):2680. #Correct:832 #Tested:983 Testing Accuracy:84.6%\r", + "Progress:98.3% Speed(reviews/sec):2677. #Correct:833 #Tested:984 Testing Accuracy:84.6%\r", + "Progress:98.4% Speed(reviews/sec):2676. #Correct:834 #Tested:985 Testing Accuracy:84.6%\r", + "Progress:98.5% Speed(reviews/sec):2678. #Correct:835 #Tested:986 Testing Accuracy:84.6%\r", + "Progress:98.6% Speed(reviews/sec):2679. #Correct:836 #Tested:987 Testing Accuracy:84.7%\r", + "Progress:98.7% Speed(reviews/sec):2679. #Correct:837 #Tested:988 Testing Accuracy:84.7%\r", + "Progress:98.8% Speed(reviews/sec):2677. #Correct:838 #Tested:989 Testing Accuracy:84.7%\r", + "Progress:98.9% Speed(reviews/sec):2677. #Correct:839 #Tested:990 Testing Accuracy:84.7%\r", + "Progress:99.0% Speed(reviews/sec):2678. #Correct:840 #Tested:991 Testing Accuracy:84.7%\r", + "Progress:99.1% Speed(reviews/sec):2676. #Correct:840 #Tested:992 Testing Accuracy:84.6%\r", + "Progress:99.2% Speed(reviews/sec):2678. #Correct:841 #Tested:993 Testing Accuracy:84.6%\r", + "Progress:99.3% Speed(reviews/sec):2678. #Correct:842 #Tested:994 Testing Accuracy:84.7%\r", + "Progress:99.4% Speed(reviews/sec):2677. #Correct:843 #Tested:995 Testing Accuracy:84.7%\r", + "Progress:99.5% Speed(reviews/sec):2674. #Correct:844 #Tested:996 Testing Accuracy:84.7%\r", + "Progress:99.6% Speed(reviews/sec):2675. #Correct:845 #Tested:997 Testing Accuracy:84.7%\r", + "Progress:99.7% Speed(reviews/sec):2675. #Correct:846 #Tested:998 Testing Accuracy:84.7%\r", + "Progress:99.8% Speed(reviews/sec):2674. #Correct:847 #Tested:999 Testing Accuracy:84.7%\r", + "Progress:99.9% Speed(reviews/sec):2674. #Correct:848 #Tested:1000 Testing Accuracy:84.8%" + ] + } + ], + "source": [ + "mlp.test(reviews[-1000:],labels[-1000:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# End of Project 5. \n", + "## Watch the next video to see Andrew's solution, then continue on to the next lesson.\n", + "# Further Noise Reduction" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0.44802472252696035),\n", + " ('closely', 0.4470141110210369),\n", + " ('segments', 0.4470141110210369),\n", + " ('lose', 0.446583355037637),\n", + " ('caine', 0.44628710262841953),\n", + " ('caught', 0.4461027538399907),\n", + " ('hamlet', 0.44558510189758965),\n", + " ('chinese', 0.4450742462032102),\n", + " ('welcome', 0.4443805243578379),\n", + " ('birth', 0.4436863209283622),\n", + " ('represents', 0.44320543609101143),\n", + " ('puts', 0.4427910657208508),\n", + " ('visuals', 0.44183275227903923),\n", + " ('fame', 0.44183275227903923),\n", + " ('closer', 0.44183275227903923),\n", + " ('web', 0.44183275227903923),\n", + " ('criminal', 0.4412745608048752),\n", + " ('minor', 0.4409224199448939),\n", + " ('jon', 0.44086703515908027),\n", + " ('liked', 0.4407499151402072),\n", + " ('restaurant', 0.44031183943833246),\n", + " ('de', 0.4398327516123722),\n", + " ('flaws', 0.4398327516123722),\n", + " ('searching', 0.4393666597838457),\n", + " ('rap', 0.4389130421757044),\n", + " ('light', 0.4388443301819989),\n", + " ('elizabeth', 0.4387223298646468),\n", + " ('marry', 0.4386173154250649),\n", + " ('learned', 0.4382549309311553),\n", + " ('controversial', 0.4382549309311553),\n", + " ('oz', 0.4382549309311553),\n", + " ('slowly', 0.4378566038993998),\n", + " ('comedic', 0.43721380642274466),\n", + " ('wayne', 0.43721380642274466),\n", + " ('thrilling', 0.43721380642274466),\n", + " ('bridge', 0.43721380642274466),\n", + " ('married', 0.4365850168219689),\n", + " ('nazi', 0.4361020775700542),\n", + " ('murder', 0.4353180712578455),\n", + " ('physical', 0.4353180712578455),\n", + " ('johnny', 0.43483971678806865),\n", + " ('michelle', 0.4344526449814167),\n", + " ('wallace', 0.4340384805522204),\n", + " ('comedies', 0.43395706390247063),\n", + " ('silent', 0.43395706390247063),\n", + " ('played', 0.43387244114515305),\n", + " ('international', 0.43363598507486073),\n", + " ('vision', 0.4328640822962789),\n", + " ('intelligent', 0.431967048853671),\n", + " ('shop', 0.43078291609245434),\n", + " ('also', 0.4303672020976917),\n", + " ('levels', 0.4302451371066513),\n", + " ('miss', 0.4300642671215322),\n", + " ('movement', 0.4295626596872249),\n", + " ...]" + ] + }, + "execution_count": 290, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# words most frequently seen in a review with a \"POSITIVE\" label\n", + "pos_neg_ratios.most_common()" + ] + }, + { + "cell_type": "code", + "execution_count": 291, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('boll', -4.969813299576001),\n", + " ('uwe', -4.624972813284271),\n", + " ('seagal', -3.644143560272545),\n", + " ('unwatchable', -3.258096538021482),\n", + " ('stinker', -3.2088254890146994),\n", + " ('mst', -2.9502698994772336),\n", + " ('incoherent', -2.9368917735310576),\n", + " ('unfunny', -2.6922395950755678),\n", + " ('waste', -2.6193845640165536),\n", + " ('blah', -2.5704288232261625),\n", + " ('horrid', -2.4849066497880004),\n", + " ('pointless', -2.4553061800117097),\n", + " ('atrocious', -2.4259083090260445),\n", + " ('redeeming', -2.3682390632154826),\n", + " ('prom', -2.3608540011180215),\n", + " ('drivel', -2.3470368555648795),\n", + " ('lousy', -2.307572634505085),\n", + " ('worst', -2.286987896180378),\n", + " ('laughable', -2.264363880173848),\n", + " ('awful', -2.227194247027435),\n", + " ('poorly', -2.2207550747464135),\n", + " ('wasting', -2.204604684633842),\n", + " ('remotely', -2.1972245773362196),\n", + " ('existent', -2.0794415416798357),\n", + " ('boredom', -1.995100393246085),\n", + " ('miserably', -1.9924301646902063),\n", + " ('sucks', -1.987068221548821),\n", + " ('uninspired', -1.9832976811269336),\n", + " ('lame', -1.981767458946166),\n", + " ('insult', -1.978345424808467)]" + ] + }, + "execution_count": 291, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# words most frequently seen in a review with a \"NEGATIVE\" label\n", + "list(reversed(pos_neg_ratios.most_common()))[0:30]" + ] + }, + { + "cell_type": "code", + "execution_count": 293, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(\"1002\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };var element = document.getElementById(\"1002\");\n", + " if (element == null) {\n", + " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1002' but no matching script tag was found. \")\n", + " return false;\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1002\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"1002\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1002' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1002\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from bokeh.models import ColumnDataSource, LabelSet\n", + "from bokeh.plotting import figure, show, output_file\n", + "from bokeh.io import output_notebook\n", + "output_notebook()" + ] + }, + { + "cell_type": "code", + "execution_count": 297, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ratna/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: The normed argument is ignored when density is provided. In future passing both will result in an error.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " \n", + " var docs_json = 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Positive/Negative Affinity Distribution\"},\"id\":\"1178\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1187\",\"type\":\"LinearScale\"},{\"attributes\":{\"data_source\":{\"id\":\"1208\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1209\",\"type\":\"Quad\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"1210\",\"type\":\"Quad\"},\"selection_glyph\":null,\"view\":{\"id\":\"1212\",\"type\":\"CDSView\"}},\"id\":\"1211\",\"type\":\"GlyphRenderer\"}],\"root_ids\":[\"1179\"]},\"title\":\"Bokeh Application\",\"version\":\"1.0.4\"}};\n", + " var render_items = [{\"docid\":\"f1943e86-5663-4210-ba2f-ef9a3239f08a\",\"roots\":{\"1179\":\"43ccf54c-9500-4cfc-b02d-fe5f5fc0ea51\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + "\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " clearInterval(timer);\n", + " }\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " clearInterval(timer);\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1179" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "hist, edges = np.histogram(list(map(lambda x:x[1],pos_neg_ratios.most_common())), density=True, bins=100, normed=True)\n", + "\n", + "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", + " toolbar_location=\"above\",\n", + " title=\"Word Positive/Negative Affinity Distribution\")\n", + "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color=\"#555555\")\n", + "show(p)" + ] + }, + { + "cell_type": "code", + "execution_count": 298, + "metadata": {}, + "outputs": [], + "source": [ + "frequency_frequency = Counter()\n", + "\n", + "for word, cnt in total_counts.most_common():\n", + " frequency_frequency[cnt] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 299, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ratna/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: DeprecationWarning: The normed argument is ignored when density is provided. In future passing both will result in an error.\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " \n", + " var docs_json = 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np.histogram(list(map(lambda x:x[1],frequency_frequency.most_common())), density=True, bins=100, normed=True)\n", + "\n", + "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", + " toolbar_location=\"above\",\n", + " title=\"The frequency distribution of the words in our corpus\")\n", + "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color=\"#555555\")\n", + "show(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project 6: Reducing Noise by Strategically Reducing the Vocabulary\n", + "\n", + "**TODO:** Improve `SentimentNetwork`'s performance by reducing more noise in the vocabulary. Specifically, do the following:\n", + "* Copy the `SentimentNetwork` class from the previous project into the following cell.\n", + "* Modify `pre_process_data`:\n", + ">* Add two additional parameters: `min_count` and `polarity_cutoff`\n", + ">* Calculate the positive-to-negative ratios of words used in the reviews. (You can use code you've written elsewhere in the notebook, but we are moving it into the class like we did with other helper code earlier.)\n", + ">* Andrew's solution only calculates a postive-to-negative ratio for words that occur at least 50 times. This keeps the network from attributing too much sentiment to rarer words. You can choose to add this to your solution if you would like. \n", + ">* Change so words are only added to the vocabulary if they occur in the vocabulary more than `min_count` times.\n", + ">* Change so words are only added to the vocabulary if the absolute value of their postive-to-negative ratio is at least `polarity_cutoff`\n", + "* Modify `__init__`:\n", + ">* Add the same two parameters (`min_count` and `polarity_cutoff`) and use them when you call `pre_process_data`" + ] + }, + { + "cell_type": "code", + "execution_count": 300, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: -Copy the SentimentNetwork class from Project 5 lesson\n", + "# -Modify it according to the above instructions \n", + "# TODO: -Copy the SentimentNetwork class from Project 4 lesson\n", + "# -Modify it according to the above instructions \n", + "# TODO: -Copy the SentimentNetwork class from Projet 3 lesson\n", + "# -Modify it to reduce noise, like in the video \n", + "import time\n", + "import sys\n", + "import numpy as np\n", + "\n", + "# Encapsulate our neural network in a class\n", + "class SentimentNetwork:\n", + " ## New for Project 6: added min_count and polarity_cutoff parameters\n", + " def __init__(self, reviews,labels,min_count = 10,polarity_cutoff = 0.1,hidden_nodes = 10, learning_rate = 0.1):\n", + " \"\"\"Create a SentimenNetwork with the given settings\n", + " Args:\n", + " reviews(list) - List of reviews used for training\n", + " labels(list) - List of POSITIVE/NEGATIVE labels associated with the given reviews\n", + " min_count(int) - Words should only be added to the vocabulary \n", + " if they occur more than this many times\n", + " polarity_cutoff(float) - The absolute value of a word's positive-to-negative\n", + " ratio must be at least this big to be considered.\n", + " hidden_nodes(int) - Number of nodes to create in the hidden layer\n", + " learning_rate(float) - Learning rate to use while training\n", + " \n", + " \"\"\"\n", + " # Assign a seed to our random number generator to ensure we get\n", + " # reproducable results during development \n", + " np.random.seed(1)\n", + "\n", + " # process the reviews and their associated labels so that everything\n", + " # is ready for training\n", + " ## New for Project 6: added min_count and polarity_cutoff arguments to pre_process_data call\n", + " self.pre_process_data(reviews, labels, polarity_cutoff, min_count)\n", + " \n", + " # Build the network to have the number of hidden nodes and the learning rate that\n", + " # were passed into this initializer. Make the same number of input nodes as\n", + " # there are vocabulary words and create a single output node.\n", + " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", + "\n", + " ## New for Project 6: added min_count and polarity_cutoff parameters\n", + " def pre_process_data(self, reviews, labels, polarity_cutoff, min_count):\n", + " \n", + " ## ----------------------------------------\n", + " ## New for Project 6: Calculate positive-to-negative ratios for words before\n", + " # building vocabulary\n", + " #\n", + " positive_counts = Counter()\n", + " negative_counts = Counter()\n", + " total_counts = Counter()\n", + "\n", + " for i in range(len(reviews)):\n", + " if(labels[i] == 'POSITIVE'):\n", + " for word in reviews[i].split(\" \"):\n", + " positive_counts[word] += 1\n", + " total_counts[word] += 1\n", + " else:\n", + " for word in reviews[i].split(\" \"):\n", + " negative_counts[word] += 1\n", + " total_counts[word] += 1\n", + "\n", + " pos_neg_ratios = Counter()\n", + "\n", + " for term,cnt in list(total_counts.most_common()):\n", + " if(cnt >= 50):\n", + " pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n", + " pos_neg_ratios[term] = pos_neg_ratio\n", + "\n", + " for word,ratio in pos_neg_ratios.most_common():\n", + " if(ratio > 1):\n", + " pos_neg_ratios[word] = np.log(ratio)\n", + " else:\n", + " pos_neg_ratios[word] = -np.log((1 / (ratio + 0.01)))\n", + " #\n", + " ## end New for Project 6\n", + " ## ----------------------------------------\n", + "\n", + " # populate review_vocab with all of the words in the given reviews\n", + " review_vocab = set()\n", + " for review in reviews:\n", + " for word in review.split(\" \"):\n", + " ## New for Project 6: only add words that occur at least min_count times\n", + " # and for words with pos/neg ratios, only add words\n", + " # that meet the polarity_cutoff\n", + " if(total_counts[word] > min_count):\n", + " if(word in pos_neg_ratios.keys()):\n", + " if((pos_neg_ratios[word] >= polarity_cutoff) or (pos_neg_ratios[word] <= -polarity_cutoff)):\n", + " review_vocab.add(word)\n", + " else:\n", + " review_vocab.add(word)\n", + "\n", + " # Convert the vocabulary set to a list so we can access words via indices\n", + " self.review_vocab = list(review_vocab)\n", + " \n", + " # populate label_vocab with all of the words in the given labels.\n", + " label_vocab = set()\n", + " for label in labels:\n", + " label_vocab.add(label)\n", + " \n", + " # Convert the label vocabulary set to a list so we can access labels via indices\n", + " self.label_vocab = list(label_vocab)\n", + " \n", + " # Store the sizes of the review and label vocabularies.\n", + " self.review_vocab_size = len(self.review_vocab)\n", + " self.label_vocab_size = len(self.label_vocab)\n", + " \n", + " # Create a dictionary of words in the vocabulary mapped to index positions\n", + " self.word2index = {}\n", + " for i, word in enumerate(self.review_vocab):\n", + " self.word2index[word] = i\n", + " \n", + " # Create a dictionary of labels mapped to index positions\n", + " self.label2index = {}\n", + " for i, label in enumerate(self.label_vocab):\n", + " self.label2index[label] = i\n", + "\n", + " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", + " # Store the number of nodes in input, hidden, and output layers.\n", + " self.input_nodes = input_nodes\n", + " self.hidden_nodes = hidden_nodes\n", + " self.output_nodes = output_nodes\n", + "\n", + " # Store the learning rate\n", + " self.learning_rate = learning_rate\n", + "\n", + " # Initialize weights\n", + " \n", + " # TODO: initialize self.weights_0_1 as a matrix of zeros. These are the weights between\n", + " # the input layer and the hidden layer.\n", + " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", + " \n", + " # TODO: initialize self.weights_1_2 as a matrix of random values. \n", + " # These are the weights between the hidden layer and the output layer.\n", + " self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n", + " (self.hidden_nodes, self.output_nodes)) # MARKED AS WRONG\n", + " \n", + " # TODO: Create the input layer, a two-dimensional matrix with shape \n", + " # 1 x input_nodes, with all values initialized to zero\n", + " self.layer_1 = np.zeros((1,hidden_nodes))\n", + " \n", + " \n", + "# def update_input_layer(self,review):\n", + "# # TODO: You can copy most of the code you wrote for update_input_layer \n", + "# # earlier in this notebook. \n", + "# #\n", + "# # However, MAKE SURE YOU CHANGE ALL VARIABLES TO REFERENCE\n", + "# # THE VERSIONS STORED IN THIS OBJECT, NOT THE GLOBAL OBJECTS.\n", + "# # For example, replace \"layer_0 *= 0\" with \"self.layer_0 *= 0\"\n", + "# self.layer_0 *= 0\n", + "# for word in review.split(\" \"):\n", + "# if(word in self.word2index.keys()):\n", + "# self.layer_0[0][self.word2index[word]] = 1\n", + " \n", + " def get_target_for_label(self,label):\n", + " # TODO: Copy the code you wrote for get_target_for_label \n", + " # earlier in this notebook. \n", + " \n", + " if(label == \"POSITIVE\"):\n", + " return 1\n", + " else:\n", + " return 0\n", + " \n", + " def sigmoid(self,x):\n", + " # TODO: Return the result of calculating the sigmoid activation function\n", + " # shown in the lectures\n", + " return 1/(1 + np.exp(-x))\n", + " \n", + " def sigmoid_output_2_derivative(self,output):\n", + " # TODO: Return the derivative of the sigmoid activation function, \n", + " # where \"output\" is the original output from the sigmoid fucntion \n", + " return output * (1 - output)\n", + "\n", + " def train(self, training_reviews_raw, training_labels):\n", + " \n", + " \n", + " training_reviews = list()\n", + " for review in training_reviews_raw:\n", + " indices = set()\n", + " for word in review.split(\" \"):\n", + " if(word in self.word2index.keys()):\n", + " indices.add(self.word2index[word])\n", + " training_reviews.append(list(indices))\n", + " \n", + " # make sure out we have a matching number of reviews and labels\n", + " assert(len(training_reviews) == len(training_labels))\n", + " \n", + " # Keep track of correct predictions to display accuracy during training \n", + " correct_so_far = 0\n", + " \n", + " # Remember when we started for printing time statistics\n", + " start = time.time()\n", + "\n", + " # loop through all the given reviews and run a forward and backward pass,\n", + " # updating weights for every item\n", + " for i in range(len(training_reviews)):\n", + " \n", + " # TODO: Get the next review and its correct label\n", + " \n", + " review = training_reviews[i]\n", + " label = training_labels[i]\n", + " \n", + " # TODO: Implement the forward pass through the network. \n", + " # That means use the given review to update the input layer, \n", + " # then calculate values for the hidden layer,\n", + " # and finally calculate the output layer.\n", + " # \n", + " # Do not use an activation function for the hidden layer,\n", + " # but use the sigmoid activation function for the output layer.\n", + " \n", + " # // Implementing Forward Bias // \n", + " self.layer_1 *= 0\n", + " \n", + " \n", + " ''' Hidden Layer '''\n", + " for index in review:\n", + " self.layer_1 += self.weights_0_1[index]\n", + " \n", + " ''' Output Layer '''\n", + " \n", + " layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))\n", + " \n", + " \n", + " \n", + " # TODO: Implement the back propagation pass here. \n", + " # That means calculate the error for the forward pass's prediction\n", + " # and update the weights in the network according to their\n", + " # contributions toward the error, as calculated via the\n", + " # gradient descent and back propagation algorithms you \n", + " # learned in class.\n", + " \n", + " # // Implemnting Back Propagation //\n", + " ''' Output layer is difference between output came and actual output '''\n", + " layer_2_error = layer_2 - self.get_target_for_label(label)\n", + " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2) #MARKED WRONG\n", + " \n", + " ''' Backpropagated errors '''\n", + " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated in to hidden layer\n", + " layer_1_delta = layer_1_error # hidden_layer gradients - no nonliearity so it's the same as the error\n", + " \n", + " ''' Updating the weights '''\n", + " ## New for Project 5: changed to use 'self.layer_1' instead of local 'layer_1'\n", + " self.weights_1_2 -= self.layer_1.T.dot(layer_2_delta)*self.learning_rate\n", + " \n", + " ## New for Project 5: Only update the weights that were used in the forward pass\n", + " for index in review:\n", + " self.weights_0_1[index] -= layer_1_delta[0] * self.learning_rate\n", + " \n", + " \n", + " \n", + " # TODO: Keep track of correct predictions. To determine if the prediction was\n", + " # correct, check that the absolute value of the output error \n", + " # is less than 0.5. If so, add one to the correct_so_far count.\n", + " \n", + " if(layer_2 >= 0.5 and label == 'POSITIVE'):\n", + " correct_so_far += 1\n", + " elif(layer_2 < 0.5 and label == 'NEGATIVE'):\n", + " correct_so_far += 1\n", + " \n", + " \n", + " # For debug purposes, print out our prediction accuracy and speed \n", + " # throughout the training process. \n", + "\n", + " elapsed_time = float(time.time() - start)\n", + " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", + " \n", + " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] \\\n", + " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", + " + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) \\\n", + " + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", + " if(i % 2500 == 0):\n", + " print(\"\")\n", + " \n", + " def test(self, testing_reviews, testing_labels):\n", + " \"\"\"\n", + " Attempts to predict the labels for the given testing_reviews,\n", + " and uses the test_labels to calculate the accuracy of those predictions.\n", + " \"\"\"\n", + " \n", + " # keep track of how many correct predictions we make\n", + " correct = 0\n", + "\n", + " # we'll time how many predictions per second we make\n", + " start = time.time()\n", + "\n", + " # Loop through each of the given reviews and call run to predict\n", + " # its label. \n", + " for i in range(len(testing_reviews)):\n", + " pred = self.run(testing_reviews[i])\n", + " if(pred == testing_labels[i]):\n", + " correct += 1\n", + " \n", + " # For debug purposes, print out our prediction accuracy and speed \n", + " # throughout the prediction process. \n", + "\n", + " elapsed_time = float(time.time() - start)\n", + " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", + " \n", + " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", + " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", + " + \" #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) \\\n", + " + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", + " \n", + " def run(self, review):\n", + " \"\"\"\n", + " Returns a POSITIVE or NEGATIVE prediction for the given review.\n", + " \"\"\"\n", + " # TODO: Run a forward pass through the network, like you did in the\n", + " # \"train\" function. That means use the given review to \n", + " # update the input layer, then calculate values for the hidden layer,\n", + " # and finally calculate the output layer.\n", + " # // Implementing Forward Bias // \n", + " \n", + " ''' Hidden Layer '''\n", + " ## New for Project 5: Identify the indices used in the review and then add\n", + " # just those weights to layer_1 \n", + " self.layer_1 *= 0\n", + " unique_indices = set()\n", + " for word in review.lower().split(\" \"):\n", + " if word in self.word2index.keys():\n", + " unique_indices.add(self.word2index[word])\n", + " for index in unique_indices:\n", + " self.layer_1 += self.weights_0_1[index]\n", + " \n", + " \n", + " ''' Output Layer '''\n", + " layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))\n", + " \n", + " \n", + " #\n", + " # Note: The review passed into this function for prediction \n", + " # might come from anywhere, so you should convert it \n", + " # to lower case prior to using it.\n", + " \n", + " # TODO: The output layer should now contain a prediction. \n", + " # Return `POSITIVE` for predictions greater-than-or-equal-to `0.5`, \n", + " # and `NEGATIVE` otherwise.\n", + " if(layer_2[0] >= 0.5):\n", + " return \"POSITIVE\"\n", + " else:\n", + " return \"NEGATIVE\"\n", + " \n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to train your network with a small polarity cutoff." + ] + }, + { + "cell_type": "code", + "execution_count": 301, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.00% Speed(reviews/sec):1221. #Correct:1 #Trained:2 Training Accuracy:50.0%\r", + "Progress:0.00% Speed(reviews/sec):1294. #Correct:1 #Trained:3 Training Accuracy:33.3%\r", + "Progress:0.01% Speed(reviews/sec):1069. #Correct:1 #Trained:4 Training Accuracy:25.0%\r", + "Progress:0.01% Speed(reviews/sec):1262. #Correct:1 #Trained:5 Training Accuracy:20.0%\r", + "Progress:0.02% Speed(reviews/sec):1421. #Correct:1 #Trained:6 Training Accuracy:16.6%\r", + "Progress:0.02% Speed(reviews/sec):1568. #Correct:1 #Trained:7 Training Accuracy:14.2%\r", + "Progress:0.02% Speed(reviews/sec):1643. #Correct:2 #Trained:8 Training Accuracy:25.0%\r", + "Progress:0.03% Speed(reviews/sec):1590. #Correct:2 #Trained:9 Training Accuracy:22.2%\r", + "Progress:0.03% Speed(reviews/sec):1550. #Correct:3 #Trained:10 Training Accuracy:30.0%\r", + "Progress:0.04% Speed(reviews/sec):1608. #Correct:3 #Trained:11 Training Accuracy:27.2%\r", + "Progress:0.04% Speed(reviews/sec):1693. #Correct:3 #Trained:12 Training Accuracy:25.0%\r", + "Progress:0.05% Speed(reviews/sec):1735. #Correct:3 #Trained:13 Training Accuracy:23.0%\r", + "Progress:0.05% Speed(reviews/sec):1711. #Correct:4 #Trained:14 Training Accuracy:28.5%\r", + "Progress:0.05% Speed(reviews/sec):1672. #Correct:4 #Trained:15 Training Accuracy:26.6%\r", + "Progress:0.06% Speed(reviews/sec):1701. #Correct:5 #Trained:16 Training Accuracy:31.2%\r", + "Progress:0.06% Speed(reviews/sec):1677. #Correct:6 #Trained:17 Training Accuracy:35.2%\r", + "Progress:0.07% Speed(reviews/sec):1746. #Correct:6 #Trained:18 Training Accuracy:33.3%\r", + "Progress:0.07% Speed(reviews/sec):1729. #Correct:6 #Trained:19 Training Accuracy:31.5%\r", + "Progress:0.07% Speed(reviews/sec):1740. #Correct:6 #Trained:20 Training Accuracy:30.0%\r", + "Progress:0.08% Speed(reviews/sec):1743. #Correct:6 #Trained:21 Training Accuracy:28.5%\r", + "Progress:0.08% Speed(reviews/sec):1742. #Correct:6 #Trained:22 Training Accuracy:27.2%\r", + "Progress:0.09% Speed(reviews/sec):1792. #Correct:7 #Trained:23 Training Accuracy:30.4%\r", + "Progress:0.09% Speed(reviews/sec):1820. #Correct:8 #Trained:24 Training Accuracy:33.3%\r", + "Progress:0.1% Speed(reviews/sec):1815. #Correct:9 #Trained:25 Training Accuracy:36.0%\r", + "Progress:0.10% Speed(reviews/sec):1854. #Correct:10 #Trained:26 Training Accuracy:38.4%\r", + "Progress:0.10% Speed(reviews/sec):1866. #Correct:10 #Trained:27 Training Accuracy:37.0%\r", + "Progress:0.11% Speed(reviews/sec):1894. #Correct:11 #Trained:28 Training Accuracy:39.2%\r", + "Progress:0.11% Speed(reviews/sec):1927. #Correct:11 #Trained:29 Training Accuracy:37.9%\r", + "Progress:0.12% Speed(reviews/sec):1951. #Correct:12 #Trained:30 Training Accuracy:40.0%\r", + "Progress:0.12% Speed(reviews/sec):1971. #Correct:12 #Trained:31 Training Accuracy:38.7%\r", + "Progress:0.12% Speed(reviews/sec):1995. #Correct:12 #Trained:32 Training Accuracy:37.5%\r", + "Progress:0.13% Speed(reviews/sec):2004. #Correct:13 #Trained:33 Training Accuracy:39.3%\r", + "Progress:0.13% Speed(reviews/sec):2042. #Correct:13 #Trained:34 Training Accuracy:38.2%\r", + "Progress:0.14% Speed(reviews/sec):2016. #Correct:14 #Trained:35 Training Accuracy:40.0%\r", + "Progress:0.14% Speed(reviews/sec):2034. #Correct:15 #Trained:36 Training Accuracy:41.6%\r", + "Progress:0.15% Speed(reviews/sec):1998. #Correct:16 #Trained:37 Training Accuracy:43.2%\r", + "Progress:0.15% Speed(reviews/sec):2020. #Correct:17 #Trained:38 Training Accuracy:44.7%\r", + "Progress:0.15% Speed(reviews/sec):2040. #Correct:17 #Trained:39 Training Accuracy:43.5%\r", + "Progress:0.16% Speed(reviews/sec):2048. #Correct:18 #Trained:40 Training Accuracy:45.0%\r", + "Progress:0.16% Speed(reviews/sec):2048. #Correct:19 #Trained:41 Training Accuracy:46.3%\r", + "Progress:0.17% Speed(reviews/sec):2091. #Correct:20 #Trained:42 Training Accuracy:47.6%\r", + "Progress:0.17% Speed(reviews/sec):2043. #Correct:21 #Trained:43 Training Accuracy:48.8%\r", + "Progress:0.17% Speed(reviews/sec):2021. #Correct:21 #Trained:44 Training Accuracy:47.7%\r", + "Progress:0.18% Speed(reviews/sec):2027. #Correct:21 #Trained:45 Training Accuracy:46.6%\r", + "Progress:0.18% Speed(reviews/sec):2039. #Correct:21 #Trained:46 Training Accuracy:45.6%\r", + "Progress:0.19% Speed(reviews/sec):2040. #Correct:21 #Trained:47 Training Accuracy:44.6%\r", + "Progress:0.19% Speed(reviews/sec):2048. #Correct:22 #Trained:48 Training Accuracy:45.8%\r", + "Progress:0.2% Speed(reviews/sec):2063. #Correct:23 #Trained:49 Training Accuracy:46.9%\r", + "Progress:0.20% Speed(reviews/sec):2080. #Correct:24 #Trained:50 Training Accuracy:48.0%\r", + "Progress:0.20% Speed(reviews/sec):2100. #Correct:24 #Trained:51 Training Accuracy:47.0%\r", + "Progress:0.21% Speed(reviews/sec):2033. #Correct:25 #Trained:52 Training Accuracy:48.0%\r", + "Progress:0.21% Speed(reviews/sec):2051. #Correct:26 #Trained:53 Training Accuracy:49.0%\r", + "Progress:0.22% Speed(reviews/sec):2050. #Correct:27 #Trained:54 Training Accuracy:50.0%\r", + "Progress:0.22% Speed(reviews/sec):2043. #Correct:27 #Trained:55 Training Accuracy:49.0%\r", + "Progress:0.22% Speed(reviews/sec):2051. #Correct:28 #Trained:56 Training Accuracy:50.0%\r", 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#Correct:36 #Trained:67 Training Accuracy:53.7%\r", + "Progress:0.27% Speed(reviews/sec):2106. #Correct:36 #Trained:68 Training Accuracy:52.9%\r", + "Progress:0.28% Speed(reviews/sec):2093. #Correct:36 #Trained:69 Training Accuracy:52.1%\r", + "Progress:0.28% Speed(reviews/sec):2068. #Correct:36 #Trained:70 Training Accuracy:51.4%\r", + "Progress:0.29% Speed(reviews/sec):2011. #Correct:37 #Trained:71 Training Accuracy:52.1%\r", + "Progress:0.29% Speed(reviews/sec):2008. #Correct:38 #Trained:72 Training Accuracy:52.7%\r", + "Progress:0.3% Speed(reviews/sec):2026. #Correct:39 #Trained:73 Training Accuracy:53.4%\r", + "Progress:0.30% Speed(reviews/sec):2030. #Correct:40 #Trained:74 Training Accuracy:54.0%\r", + "Progress:0.30% Speed(reviews/sec):2021. #Correct:41 #Trained:75 Training Accuracy:54.6%\r", + "Progress:0.31% Speed(reviews/sec):2028. #Correct:42 #Trained:76 Training Accuracy:55.2%\r", + "Progress:0.31% Speed(reviews/sec):2029. #Correct:43 #Trained:77 Training Accuracy:55.8%\r", 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#Correct:105 #Trained:150 Training Accuracy:70.0%\r", + "Progress:0.62% Speed(reviews/sec):1959. #Correct:105 #Trained:151 Training Accuracy:69.5%\r", + "Progress:0.62% Speed(reviews/sec):1963. #Correct:106 #Trained:152 Training Accuracy:69.7%\r", + "Progress:0.63% Speed(reviews/sec):1968. #Correct:107 #Trained:153 Training Accuracy:69.9%\r", + "Progress:0.63% Speed(reviews/sec):1964. #Correct:108 #Trained:154 Training Accuracy:70.1%\r", + "Progress:0.64% Speed(reviews/sec):1962. #Correct:108 #Trained:155 Training Accuracy:69.6%\r", + "Progress:0.64% Speed(reviews/sec):1956. #Correct:109 #Trained:156 Training Accuracy:69.8%\r", + "Progress:0.65% Speed(reviews/sec):1964. #Correct:110 #Trained:157 Training Accuracy:70.0%\r", + "Progress:0.65% Speed(reviews/sec):1968. #Correct:111 #Trained:158 Training Accuracy:70.2%\r", + "Progress:0.65% Speed(reviews/sec):1970. #Correct:112 #Trained:159 Training Accuracy:70.4%\r", + "Progress:0.66% Speed(reviews/sec):1967. #Correct:113 #Trained:160 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"Progress:0.70% Speed(reviews/sec):1994. #Correct:121 #Trained:171 Training Accuracy:70.7%\r", + "Progress:0.71% Speed(reviews/sec):1990. #Correct:122 #Trained:172 Training Accuracy:70.9%\r", + "Progress:0.71% Speed(reviews/sec):1988. #Correct:123 #Trained:173 Training Accuracy:71.0%\r", + "Progress:0.72% Speed(reviews/sec):1992. #Correct:124 #Trained:174 Training Accuracy:71.2%\r", + "Progress:0.72% Speed(reviews/sec):1996. #Correct:125 #Trained:175 Training Accuracy:71.4%\r", + "Progress:0.72% Speed(reviews/sec):1988. #Correct:126 #Trained:176 Training Accuracy:71.5%\r", + "Progress:0.73% Speed(reviews/sec):1990. #Correct:126 #Trained:177 Training Accuracy:71.1%\r", + "Progress:0.73% Speed(reviews/sec):1991. #Correct:127 #Trained:178 Training Accuracy:71.3%\r", + "Progress:0.74% Speed(reviews/sec):1996. #Correct:127 #Trained:179 Training Accuracy:70.9%\r", + "Progress:0.74% Speed(reviews/sec):2000. #Correct:128 #Trained:180 Training Accuracy:71.1%\r", + "Progress:0.75% 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Training Accuracy:84.6%\r", + "Progress:78.1% Speed(reviews/sec):1650. #Correct:15877 #Trained:18766 Training Accuracy:84.6%\r", + "Progress:78.1% Speed(reviews/sec):1650. #Correct:15878 #Trained:18767 Training Accuracy:84.6%\r", + "Progress:78.1% Speed(reviews/sec):1650. #Correct:15879 #Trained:18768 Training Accuracy:84.6%\r", + "Progress:78.2% Speed(reviews/sec):1650. #Correct:15880 #Trained:18769 Training Accuracy:84.6%\r", + "Progress:78.2% Speed(reviews/sec):1650. #Correct:15881 #Trained:18770 Training Accuracy:84.6%\r", + "Progress:78.2% Speed(reviews/sec):1650. #Correct:15881 #Trained:18771 Training Accuracy:84.6%\r", + "Progress:78.2% Speed(reviews/sec):1650. #Correct:15882 #Trained:18772 Training Accuracy:84.6%\r", + "Progress:78.2% Speed(reviews/sec):1650. #Correct:15883 #Trained:18773 Training Accuracy:84.6%\r", + "Progress:78.2% Speed(reviews/sec):1650. #Correct:15883 #Trained:18774 Training Accuracy:84.6%\r", + "Progress:78.2% Speed(reviews/sec):1650. #Correct:15884 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"Progress:99.8% Speed(reviews/sec):1610. #Correct:20440 #Trained:23975 Training Accuracy:85.2%\r", + "Progress:99.8% Speed(reviews/sec):1610. #Correct:20441 #Trained:23976 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20442 #Trained:23977 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20443 #Trained:23978 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20444 #Trained:23979 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20445 #Trained:23980 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20445 #Trained:23981 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20446 #Trained:23982 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20447 #Trained:23983 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20448 #Trained:23984 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20449 #Trained:23985 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20450 #Trained:23986 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20451 #Trained:23987 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20452 #Trained:23988 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20453 #Trained:23989 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20454 #Trained:23990 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20455 #Trained:23991 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20455 #Trained:23992 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20456 #Trained:23993 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20457 #Trained:23994 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20458 #Trained:23995 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20459 #Trained:23996 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20460 #Trained:23997 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20460 #Trained:23998 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20460 #Trained:23999 Training Accuracy:85.2%\r", + "Progress:99.9% Speed(reviews/sec):1610. #Correct:20461 #Trained:24000 Training Accuracy:85.2%" + ] + } + ], + "source": [ + "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.05,learning_rate=0.01)\n", + "mlp.train(reviews[:-1000],labels[:-1000])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And run the following cell to test it's performance. It should be " + ] + }, + { + "cell_type": "code", + "execution_count": 303, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Tested:1 Testing Accuracy:100.%\r", + "Progress:0.00% Speed(reviews/sec):789.2 #Correct:2 #Tested:2 Testing Accuracy:100.%\r", + "Progress:0.00% Speed(reviews/sec):775.6 #Correct:3 #Tested:3 Testing Accuracy:100.%\r", + "Progress:0.01% Speed(reviews/sec):605.2 #Correct:4 #Tested:4 Testing Accuracy:100.%\r", + "Progress:0.01% Speed(reviews/sec):530.7 #Correct:5 #Tested:5 Testing Accuracy:100.%\r", + "Progress:0.02% Speed(reviews/sec):614.5 #Correct:6 #Tested:6 Testing Accuracy:100.%\r", + "Progress:0.02% Speed(reviews/sec):696.6 #Correct:7 #Tested:7 Testing Accuracy:100.%\r", + "Progress:0.02% Speed(reviews/sec):761.7 #Correct:8 #Tested:8 Testing Accuracy:100.%\r", + "Progress:0.03% Speed(reviews/sec):828.6 #Correct:9 #Tested:9 Testing Accuracy:100.%\r", + 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#Correct:29 #Tested:31 Testing Accuracy:93.5%\r", + "Progress:0.12% Speed(reviews/sec):1208. #Correct:29 #Tested:32 Testing Accuracy:90.6%\r", + "Progress:0.12% Speed(reviews/sec):1218. #Correct:30 #Tested:33 Testing Accuracy:90.9%\r", + "Progress:0.13% Speed(reviews/sec):1238. #Correct:31 #Tested:34 Testing Accuracy:91.1%\r", + "Progress:0.13% Speed(reviews/sec):1234. #Correct:32 #Tested:35 Testing Accuracy:91.4%\r", + "Progress:0.14% Speed(reviews/sec):1252. #Correct:33 #Tested:36 Testing Accuracy:91.6%\r", + "Progress:0.14% Speed(reviews/sec):1271. #Correct:33 #Tested:37 Testing Accuracy:89.1%\r", + "Progress:0.14% Speed(reviews/sec):1287. #Correct:34 #Tested:38 Testing Accuracy:89.4%\r", + "Progress:0.15% Speed(reviews/sec):1296. #Correct:35 #Tested:39 Testing Accuracy:89.7%\r", + "Progress:0.15% Speed(reviews/sec):1305. #Correct:35 #Tested:40 Testing Accuracy:87.5%\r", + "Progress:0.16% Speed(reviews/sec):1307. #Correct:36 #Tested:41 Testing Accuracy:87.8%\r", + "Progress:0.16% 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Accuracy:88.4%\r", + "Progress:0.20% Speed(reviews/sec):1320. #Correct:47 #Tested:53 Testing Accuracy:88.6%\r", + "Progress:0.21% Speed(reviews/sec):1334. #Correct:48 #Tested:54 Testing Accuracy:88.8%\r", + "Progress:0.21% Speed(reviews/sec):1346. #Correct:49 #Tested:55 Testing Accuracy:89.0%\r", + "Progress:0.22% Speed(reviews/sec):1363. #Correct:50 #Tested:56 Testing Accuracy:89.2%\r", + "Progress:0.22% Speed(reviews/sec):1375. #Correct:51 #Tested:57 Testing Accuracy:89.4%\r", + "Progress:0.22% Speed(reviews/sec):1382. #Correct:52 #Tested:58 Testing Accuracy:89.6%\r", + "Progress:0.23% Speed(reviews/sec):1399. #Correct:52 #Tested:59 Testing Accuracy:88.1%\r", + "Progress:0.23% Speed(reviews/sec):1414. #Correct:53 #Tested:60 Testing Accuracy:88.3%\r", + "Progress:0.24% Speed(reviews/sec):1422. #Correct:54 #Tested:61 Testing Accuracy:88.5%\r", + "Progress:0.24% Speed(reviews/sec):1440. #Correct:55 #Tested:62 Testing Accuracy:88.7%\r", + "Progress:0.24% Speed(reviews/sec):1457. #Correct:56 #Tested:63 Testing Accuracy:88.8%\r", + "Progress:0.25% Speed(reviews/sec):1469. #Correct:57 #Tested:64 Testing Accuracy:89.0%\r", + "Progress:0.25% Speed(reviews/sec):1479. #Correct:57 #Tested:65 Testing Accuracy:87.6%\r", + "Progress:0.26% Speed(reviews/sec):1491. #Correct:58 #Tested:66 Testing Accuracy:87.8%\r", + "Progress:0.26% Speed(reviews/sec):1505. #Correct:58 #Tested:67 Testing Accuracy:86.5%\r", + "Progress:0.26% Speed(reviews/sec):1520. #Correct:59 #Tested:68 Testing Accuracy:86.7%\r", + "Progress:0.27% Speed(reviews/sec):1522. #Correct:60 #Tested:69 Testing Accuracy:86.9%\r", + "Progress:0.27% Speed(reviews/sec):1526. #Correct:61 #Tested:70 Testing Accuracy:87.1%\r", + "Progress:0.28% Speed(reviews/sec):1527. #Correct:62 #Tested:71 Testing Accuracy:87.3%\r", + "Progress:0.28% Speed(reviews/sec):1526. #Correct:63 #Tested:72 Testing Accuracy:87.5%\r", + "Progress:0.28% Speed(reviews/sec):1535. #Correct:64 #Tested:73 Testing Accuracy:87.6%\r", + "Progress:0.29% 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Accuracy:89.2%\r", + "Progress:0.33% Speed(reviews/sec):1626. #Correct:76 #Tested:85 Testing Accuracy:89.4%\r", + "Progress:0.34% Speed(reviews/sec):1635. #Correct:77 #Tested:86 Testing Accuracy:89.5%\r", + "Progress:0.34% Speed(reviews/sec):1645. #Correct:78 #Tested:87 Testing Accuracy:89.6%\r", + "Progress:0.34% Speed(reviews/sec):1654. #Correct:79 #Tested:88 Testing Accuracy:89.7%\r", + "Progress:0.35% Speed(reviews/sec):1668. #Correct:80 #Tested:89 Testing Accuracy:89.8%\r", + "Progress:0.35% Speed(reviews/sec):1676. #Correct:81 #Tested:90 Testing Accuracy:90.0%\r", + "Progress:0.36% Speed(reviews/sec):1660. #Correct:82 #Tested:91 Testing Accuracy:90.1%\r", + "Progress:0.36% Speed(reviews/sec):1655. #Correct:83 #Tested:92 Testing Accuracy:90.2%\r", + "Progress:0.36% Speed(reviews/sec):1615. #Correct:84 #Tested:93 Testing Accuracy:90.3%\r", + "Progress:0.37% Speed(reviews/sec):1584. #Correct:85 #Tested:94 Testing Accuracy:90.4%\r", + "Progress:0.37% Speed(reviews/sec):1562. 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Testing Accuracy:91.0%\r", + "Progress:0.67% Speed(reviews/sec):1636. #Correct:154 #Tested:169 Testing Accuracy:91.1%\r", + "Progress:0.67% Speed(reviews/sec):1641. #Correct:154 #Tested:170 Testing Accuracy:90.5%\r", + "Progress:0.68% Speed(reviews/sec):1649. #Correct:155 #Tested:171 Testing Accuracy:90.6%\r", + "Progress:0.68% Speed(reviews/sec):1652. #Correct:156 #Tested:172 Testing Accuracy:90.6%\r", + "Progress:0.68% Speed(reviews/sec):1655. #Correct:157 #Tested:173 Testing Accuracy:90.7%\r", + "Progress:0.69% Speed(reviews/sec):1661. #Correct:158 #Tested:174 Testing Accuracy:90.8%\r", + "Progress:0.69% Speed(reviews/sec):1665. #Correct:159 #Tested:175 Testing Accuracy:90.8%\r", + "Progress:0.7% Speed(reviews/sec):1664. #Correct:160 #Tested:176 Testing Accuracy:90.9%\r", + "Progress:0.70% Speed(reviews/sec):1662. #Correct:161 #Tested:177 Testing Accuracy:90.9%\r", + "Progress:0.70% Speed(reviews/sec):1666. #Correct:162 #Tested:178 Testing Accuracy:91.0%\r", + "Progress:0.71% 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Testing Accuracy:91.0%\r", + "Progress:0.75% Speed(reviews/sec):1702. #Correct:173 #Tested:190 Testing Accuracy:91.0%\r", + "Progress:0.76% Speed(reviews/sec):1709. #Correct:174 #Tested:191 Testing Accuracy:91.0%\r", + "Progress:0.76% Speed(reviews/sec):1717. #Correct:175 #Tested:192 Testing Accuracy:91.1%\r", + "Progress:0.76% Speed(reviews/sec):1724. #Correct:176 #Tested:193 Testing Accuracy:91.1%\r", + "Progress:0.77% Speed(reviews/sec):1731. #Correct:176 #Tested:194 Testing Accuracy:90.7%\r", + "Progress:0.77% Speed(reviews/sec):1737. #Correct:177 #Tested:195 Testing Accuracy:90.7%\r", + "Progress:0.78% Speed(reviews/sec):1743. #Correct:178 #Tested:196 Testing Accuracy:90.8%\r", + "Progress:0.78% Speed(reviews/sec):1746. #Correct:179 #Tested:197 Testing Accuracy:90.8%\r", + "Progress:0.78% Speed(reviews/sec):1743. #Correct:180 #Tested:198 Testing Accuracy:90.9%\r", + "Progress:0.79% Speed(reviews/sec):1749. #Correct:181 #Tested:199 Testing Accuracy:90.9%\r", + "Progress:0.79% 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Testing Accuracy:91.4%\r", + "Progress:0.84% Speed(reviews/sec):1800. #Correct:193 #Tested:211 Testing Accuracy:91.4%\r", + "Progress:0.84% Speed(reviews/sec):1804. #Correct:194 #Tested:212 Testing Accuracy:91.5%\r", + "Progress:0.84% Speed(reviews/sec):1808. #Correct:195 #Tested:213 Testing Accuracy:91.5%\r", + "Progress:0.85% Speed(reviews/sec):1814. #Correct:196 #Tested:214 Testing Accuracy:91.5%\r", + "Progress:0.85% Speed(reviews/sec):1818. #Correct:197 #Tested:215 Testing Accuracy:91.6%\r", + "Progress:0.86% Speed(reviews/sec):1822. #Correct:198 #Tested:216 Testing Accuracy:91.6%\r", + "Progress:0.86% Speed(reviews/sec):1827. #Correct:199 #Tested:217 Testing Accuracy:91.7%\r", + "Progress:0.86% Speed(reviews/sec):1833. #Correct:200 #Tested:218 Testing Accuracy:91.7%\r", + "Progress:0.87% Speed(reviews/sec):1833. #Correct:201 #Tested:219 Testing Accuracy:91.7%\r", + "Progress:0.87% Speed(reviews/sec):1838. #Correct:202 #Tested:220 Testing Accuracy:91.8%\r", + "Progress:0.88% Speed(reviews/sec):1838. #Correct:203 #Tested:221 Testing Accuracy:91.8%\r", + "Progress:0.88% Speed(reviews/sec):1840. #Correct:204 #Tested:222 Testing Accuracy:91.8%\r", + "Progress:0.88% Speed(reviews/sec):1841. #Correct:205 #Tested:223 Testing Accuracy:91.9%\r", + "Progress:0.89% Speed(reviews/sec):1847. #Correct:206 #Tested:224 Testing Accuracy:91.9%\r", + "Progress:0.89% Speed(reviews/sec):1851. #Correct:207 #Tested:225 Testing Accuracy:92.0%\r", + "Progress:0.9% Speed(reviews/sec):1857. #Correct:208 #Tested:226 Testing Accuracy:92.0%\r", + "Progress:0.90% Speed(reviews/sec):1864. #Correct:209 #Tested:227 Testing Accuracy:92.0%\r", + "Progress:0.90% Speed(reviews/sec):1868. #Correct:210 #Tested:228 Testing Accuracy:92.1%\r", + "Progress:0.91% Speed(reviews/sec):1873. #Correct:211 #Tested:229 Testing Accuracy:92.1%\r", + "Progress:0.91% Speed(reviews/sec):1877. #Correct:212 #Tested:230 Testing Accuracy:92.1%\r", + "Progress:0.92% Speed(reviews/sec):1883. #Correct:213 #Tested:231 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Testing Accuracy:90.4%\r", + "Progress:3.18% Speed(reviews/sec):2812. #Correct:721 #Tested:798 Testing Accuracy:90.3%\r", + "Progress:3.19% Speed(reviews/sec):2814. #Correct:722 #Tested:799 Testing Accuracy:90.3%\r", + "Progress:3.19% Speed(reviews/sec):2816. #Correct:723 #Tested:800 Testing Accuracy:90.3%\r", + "Progress:3.2% Speed(reviews/sec):2816. #Correct:724 #Tested:801 Testing Accuracy:90.3%\r", + "Progress:3.20% Speed(reviews/sec):2817. #Correct:725 #Tested:802 Testing Accuracy:90.3%\r", + "Progress:3.20% Speed(reviews/sec):2818. #Correct:726 #Tested:803 Testing Accuracy:90.4%\r", + "Progress:3.21% Speed(reviews/sec):2819. #Correct:727 #Tested:804 Testing Accuracy:90.4%\r", + "Progress:3.21% Speed(reviews/sec):2819. #Correct:728 #Tested:805 Testing Accuracy:90.4%\r", + "Progress:3.22% Speed(reviews/sec):2821. #Correct:729 #Tested:806 Testing Accuracy:90.4%\r", + "Progress:3.22% Speed(reviews/sec):2821. #Correct:730 #Tested:807 Testing Accuracy:90.4%\r", + "Progress:3.22% 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Testing Accuracy:90.5%\r", + "Progress:3.27% Speed(reviews/sec):2836. #Correct:742 #Tested:819 Testing Accuracy:90.5%\r", + "Progress:3.27% Speed(reviews/sec):2837. #Correct:743 #Tested:820 Testing Accuracy:90.6%\r", + "Progress:3.28% Speed(reviews/sec):2839. #Correct:744 #Tested:821 Testing Accuracy:90.6%\r", + "Progress:3.28% Speed(reviews/sec):2841. #Correct:745 #Tested:822 Testing Accuracy:90.6%\r", + "Progress:3.28% Speed(reviews/sec):2842. #Correct:746 #Tested:823 Testing Accuracy:90.6%\r", + "Progress:3.29% Speed(reviews/sec):2843. #Correct:746 #Tested:824 Testing Accuracy:90.5%\r", + "Progress:3.29% Speed(reviews/sec):2845. #Correct:747 #Tested:825 Testing Accuracy:90.5%\r", + "Progress:3.3% Speed(reviews/sec):2845. #Correct:748 #Tested:826 Testing Accuracy:90.5%\r", + "Progress:3.30% Speed(reviews/sec):2846. #Correct:749 #Tested:827 Testing Accuracy:90.5%\r", + "Progress:3.30% Speed(reviews/sec):2848. #Correct:749 #Tested:828 Testing Accuracy:90.4%\r", + "Progress:3.31% 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Testing Accuracy:90.5%\r", + "Progress:3.35% Speed(reviews/sec):2865. #Correct:761 #Tested:840 Testing Accuracy:90.5%\r", + "Progress:3.36% Speed(reviews/sec):2866. #Correct:762 #Tested:841 Testing Accuracy:90.6%\r", + "Progress:3.36% Speed(reviews/sec):2867. #Correct:763 #Tested:842 Testing Accuracy:90.6%\r", + "Progress:3.36% Speed(reviews/sec):2862. #Correct:764 #Tested:843 Testing Accuracy:90.6%\r", + "Progress:3.37% Speed(reviews/sec):2863. #Correct:765 #Tested:844 Testing Accuracy:90.6%\r", + "Progress:3.37% Speed(reviews/sec):2865. #Correct:766 #Tested:845 Testing Accuracy:90.6%\r", + "Progress:3.38% Speed(reviews/sec):2864. #Correct:766 #Tested:846 Testing Accuracy:90.5%\r", + "Progress:3.38% Speed(reviews/sec):2866. #Correct:767 #Tested:847 Testing Accuracy:90.5%\r", + "Progress:3.38% Speed(reviews/sec):2866. #Correct:768 #Tested:848 Testing Accuracy:90.5%\r", + "Progress:3.39% Speed(reviews/sec):2867. #Correct:769 #Tested:849 Testing Accuracy:90.5%\r", + "Progress:3.39% 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Testing Accuracy:90.4%\r", + "Progress:3.77% Speed(reviews/sec):2768. #Correct:855 #Tested:945 Testing Accuracy:90.4%\r", + "Progress:3.78% Speed(reviews/sec):2769. #Correct:856 #Tested:946 Testing Accuracy:90.4%\r", + "Progress:3.78% Speed(reviews/sec):2769. #Correct:857 #Tested:947 Testing Accuracy:90.4%\r", + "Progress:3.78% Speed(reviews/sec):2770. #Correct:858 #Tested:948 Testing Accuracy:90.5%\r", + "Progress:3.79% Speed(reviews/sec):2771. #Correct:859 #Tested:949 Testing Accuracy:90.5%\r", + "Progress:3.79% Speed(reviews/sec):2773. #Correct:860 #Tested:950 Testing Accuracy:90.5%\r", + "Progress:3.8% Speed(reviews/sec):2774. #Correct:861 #Tested:951 Testing Accuracy:90.5%\r", + "Progress:3.80% Speed(reviews/sec):2775. #Correct:862 #Tested:952 Testing Accuracy:90.5%\r", + "Progress:3.80% Speed(reviews/sec):2777. #Correct:863 #Tested:953 Testing Accuracy:90.5%\r", + "Progress:3.81% Speed(reviews/sec):2773. #Correct:864 #Tested:954 Testing Accuracy:90.5%\r", + "Progress:3.81% 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Testing Accuracy:90.5%\r", + "Progress:3.86% Speed(reviews/sec):2772. #Correct:875 #Tested:966 Testing Accuracy:90.5%\r", + "Progress:3.86% Speed(reviews/sec):2772. #Correct:876 #Tested:967 Testing Accuracy:90.5%\r", + "Progress:3.86% Speed(reviews/sec):2773. #Correct:877 #Tested:968 Testing Accuracy:90.5%\r", + "Progress:3.87% Speed(reviews/sec):2772. #Correct:878 #Tested:969 Testing Accuracy:90.6%\r", + "Progress:3.87% Speed(reviews/sec):2770. #Correct:878 #Tested:970 Testing Accuracy:90.5%\r", + "Progress:3.88% Speed(reviews/sec):2766. #Correct:879 #Tested:971 Testing Accuracy:90.5%\r", + "Progress:3.88% Speed(reviews/sec):2767. #Correct:880 #Tested:972 Testing Accuracy:90.5%\r", + "Progress:3.88% Speed(reviews/sec):2765. #Correct:881 #Tested:973 Testing Accuracy:90.5%\r", + "Progress:3.89% Speed(reviews/sec):2764. #Correct:882 #Tested:974 Testing Accuracy:90.5%\r", + "Progress:3.89% Speed(reviews/sec):2762. #Correct:883 #Tested:975 Testing Accuracy:90.5%\r", + "Progress:3.9% 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Testing Accuracy:90.6%\r", + "Progress:3.94% Speed(reviews/sec):2755. #Correct:895 #Tested:987 Testing Accuracy:90.6%\r", + "Progress:3.94% Speed(reviews/sec):2754. #Correct:896 #Tested:988 Testing Accuracy:90.6%\r", + "Progress:3.95% Speed(reviews/sec):2751. #Correct:897 #Tested:989 Testing Accuracy:90.6%\r", + "Progress:3.95% Speed(reviews/sec):2750. #Correct:898 #Tested:990 Testing Accuracy:90.7%\r", + "Progress:3.96% Speed(reviews/sec):2748. #Correct:899 #Tested:991 Testing Accuracy:90.7%\r", + "Progress:3.96% Speed(reviews/sec):2748. #Correct:900 #Tested:992 Testing Accuracy:90.7%\r", + "Progress:3.96% Speed(reviews/sec):2745. #Correct:900 #Tested:993 Testing Accuracy:90.6%\r", + "Progress:3.97% Speed(reviews/sec):2743. #Correct:901 #Tested:994 Testing Accuracy:90.6%\r", + "Progress:3.97% Speed(reviews/sec):2744. #Correct:902 #Tested:995 Testing Accuracy:90.6%\r", + "Progress:3.98% Speed(reviews/sec):2730. #Correct:903 #Tested:996 Testing Accuracy:90.6%\r", + "Progress:3.98% 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Testing Accuracy:90.2%\r", + "Progress:8.33% Speed(reviews/sec):3069. #Correct:1881 #Tested:2084 Testing Accuracy:90.2%\r", + "Progress:8.33% Speed(reviews/sec):3070. #Correct:1882 #Tested:2085 Testing Accuracy:90.2%\r", + "Progress:8.34% Speed(reviews/sec):3070. #Correct:1883 #Tested:2086 Testing Accuracy:90.2%\r", + "Progress:8.34% Speed(reviews/sec):3070. #Correct:1884 #Tested:2087 Testing Accuracy:90.2%\r", + "Progress:8.34% Speed(reviews/sec):3070. #Correct:1885 #Tested:2088 Testing Accuracy:90.2%\r", + "Progress:8.35% Speed(reviews/sec):3069. #Correct:1886 #Tested:2089 Testing Accuracy:90.2%\r", + "Progress:8.35% Speed(reviews/sec):3067. #Correct:1887 #Tested:2090 Testing Accuracy:90.2%\r", + "Progress:8.36% Speed(reviews/sec):3068. #Correct:1888 #Tested:2091 Testing Accuracy:90.2%\r", + "Progress:8.36% Speed(reviews/sec):3068. #Correct:1889 #Tested:2092 Testing Accuracy:90.2%\r", + "Progress:8.36% Speed(reviews/sec):3069. #Correct:1890 #Tested:2093 Testing Accuracy:90.3%\r", + 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Testing Accuracy:90.2%\r", + "Progress:8.49% Speed(reviews/sec):3045. #Correct:1918 #Tested:2125 Testing Accuracy:90.2%\r", + "Progress:8.5% Speed(reviews/sec):3041. #Correct:1918 #Tested:2126 Testing Accuracy:90.2%\r", + "Progress:8.50% Speed(reviews/sec):3039. #Correct:1919 #Tested:2127 Testing Accuracy:90.2%\r", + "Progress:8.50% Speed(reviews/sec):3036. #Correct:1919 #Tested:2128 Testing Accuracy:90.1%\r", + "Progress:8.51% Speed(reviews/sec):3036. #Correct:1920 #Tested:2129 Testing Accuracy:90.1%\r", + "Progress:8.51% Speed(reviews/sec):3036. #Correct:1921 #Tested:2130 Testing Accuracy:90.1%\r", + "Progress:8.52% Speed(reviews/sec):3037. #Correct:1922 #Tested:2131 Testing Accuracy:90.1%\r", + "Progress:8.52% Speed(reviews/sec):3036. #Correct:1923 #Tested:2132 Testing Accuracy:90.1%\r", + "Progress:8.52% Speed(reviews/sec):3036. #Correct:1924 #Tested:2133 Testing Accuracy:90.2%\r", + "Progress:8.53% Speed(reviews/sec):3036. #Correct:1925 #Tested:2134 Testing Accuracy:90.2%\r", + 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Speed(reviews/sec):3026. #Correct:1936 #Tested:2145 Testing Accuracy:90.2%\r", + "Progress:8.58% Speed(reviews/sec):3023. #Correct:1936 #Tested:2146 Testing Accuracy:90.2%\r", + "Progress:8.58% Speed(reviews/sec):3023. #Correct:1937 #Tested:2147 Testing Accuracy:90.2%\r", + "Progress:8.58% Speed(reviews/sec):3022. #Correct:1938 #Tested:2148 Testing Accuracy:90.2%\r", + "Progress:8.59% Speed(reviews/sec):3022. #Correct:1939 #Tested:2149 Testing Accuracy:90.2%\r", + "Progress:8.59% Speed(reviews/sec):3020. #Correct:1940 #Tested:2150 Testing Accuracy:90.2%\r", + "Progress:8.6% Speed(reviews/sec):3020. #Correct:1941 #Tested:2151 Testing Accuracy:90.2%\r", + "Progress:8.60% Speed(reviews/sec):3020. #Correct:1942 #Tested:2152 Testing Accuracy:90.2%\r", + "Progress:8.60% Speed(reviews/sec):3021. #Correct:1943 #Tested:2153 Testing Accuracy:90.2%\r", + "Progress:8.61% Speed(reviews/sec):3021. #Correct:1944 #Tested:2154 Testing Accuracy:90.2%\r", + "Progress:8.61% Speed(reviews/sec):3021. 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Testing Accuracy:90.2%\r", + "Progress:8.66% Speed(reviews/sec):3021. #Correct:1955 #Tested:2166 Testing Accuracy:90.2%\r", + "Progress:8.66% Speed(reviews/sec):3020. #Correct:1956 #Tested:2167 Testing Accuracy:90.2%\r", + "Progress:8.66% Speed(reviews/sec):3021. #Correct:1957 #Tested:2168 Testing Accuracy:90.2%\r", + "Progress:8.67% Speed(reviews/sec):3021. #Correct:1958 #Tested:2169 Testing Accuracy:90.2%\r", + "Progress:8.67% Speed(reviews/sec):3020. #Correct:1959 #Tested:2170 Testing Accuracy:90.2%\r", + "Progress:8.68% Speed(reviews/sec):3017. #Correct:1960 #Tested:2171 Testing Accuracy:90.2%\r", + "Progress:8.68% Speed(reviews/sec):3009. #Correct:1961 #Tested:2172 Testing Accuracy:90.2%\r", + "Progress:8.68% Speed(reviews/sec):3006. #Correct:1962 #Tested:2173 Testing Accuracy:90.2%\r", + "Progress:8.69% Speed(reviews/sec):3004. #Correct:1963 #Tested:2174 Testing Accuracy:90.2%\r", + "Progress:8.69% Speed(reviews/sec):3002. #Correct:1964 #Tested:2175 Testing Accuracy:90.2%\r", + "Progress:8.7% Speed(reviews/sec):3001. #Correct:1965 #Tested:2176 Testing Accuracy:90.3%\r", + "Progress:8.70% Speed(reviews/sec):2998. #Correct:1965 #Tested:2177 Testing Accuracy:90.2%\r", + "Progress:8.70% Speed(reviews/sec):2998. #Correct:1966 #Tested:2178 Testing Accuracy:90.2%\r", + "Progress:8.71% Speed(reviews/sec):2995. #Correct:1967 #Tested:2179 Testing Accuracy:90.2%\r", + "Progress:8.71% Speed(reviews/sec):2995. #Correct:1968 #Tested:2180 Testing Accuracy:90.2%\r", + "Progress:8.72% Speed(reviews/sec):2994. #Correct:1969 #Tested:2181 Testing Accuracy:90.2%\r", + "Progress:8.72% Speed(reviews/sec):2994. #Correct:1970 #Tested:2182 Testing Accuracy:90.2%\r", + "Progress:8.72% Speed(reviews/sec):2992. #Correct:1971 #Tested:2183 Testing Accuracy:90.2%\r", + "Progress:8.73% Speed(reviews/sec):2992. #Correct:1971 #Tested:2184 Testing Accuracy:90.2%\r", + "Progress:8.73% Speed(reviews/sec):2990. #Correct:1972 #Tested:2185 Testing Accuracy:90.2%\r", + "Progress:8.74% Speed(reviews/sec):2991. #Correct:1973 #Tested:2186 Testing Accuracy:90.2%\r", + "Progress:8.74% Speed(reviews/sec):2991. #Correct:1974 #Tested:2187 Testing Accuracy:90.2%\r", + "Progress:8.74% Speed(reviews/sec):2988. #Correct:1975 #Tested:2188 Testing Accuracy:90.2%\r", + "Progress:8.75% Speed(reviews/sec):2988. #Correct:1976 #Tested:2189 Testing Accuracy:90.2%\r", + "Progress:8.75% Speed(reviews/sec):2982. #Correct:1977 #Tested:2190 Testing Accuracy:90.2%\r", + "Progress:8.76% Speed(reviews/sec):2980. #Correct:1978 #Tested:2191 Testing Accuracy:90.2%\r", + "Progress:8.76% Speed(reviews/sec):2978. #Correct:1979 #Tested:2192 Testing Accuracy:90.2%\r", + "Progress:8.76% Speed(reviews/sec):2977. #Correct:1980 #Tested:2193 Testing Accuracy:90.2%\r", + "Progress:8.77% Speed(reviews/sec):2975. #Correct:1981 #Tested:2194 Testing Accuracy:90.2%\r", + "Progress:8.77% Speed(reviews/sec):2974. #Correct:1982 #Tested:2195 Testing Accuracy:90.2%\r", + "Progress:8.78% Speed(reviews/sec):2973. #Correct:1983 #Tested:2196 Testing Accuracy:90.3%\r", + "Progress:8.78% Speed(reviews/sec):2973. #Correct:1984 #Tested:2197 Testing Accuracy:90.3%\r", + "Progress:8.78% Speed(reviews/sec):2974. #Correct:1985 #Tested:2198 Testing Accuracy:90.3%\r", + "Progress:8.79% Speed(reviews/sec):2973. #Correct:1986 #Tested:2199 Testing Accuracy:90.3%\r", + "Progress:8.79% Speed(reviews/sec):2972. #Correct:1987 #Tested:2200 Testing Accuracy:90.3%\r", + "Progress:8.8% Speed(reviews/sec):2973. #Correct:1988 #Tested:2201 Testing Accuracy:90.3%\r", + "Progress:8.80% Speed(reviews/sec):2973. #Correct:1988 #Tested:2202 Testing Accuracy:90.2%\r", + "Progress:8.80% Speed(reviews/sec):2973. #Correct:1989 #Tested:2203 Testing Accuracy:90.2%\r", + "Progress:8.81% Speed(reviews/sec):2973. #Correct:1990 #Tested:2204 Testing Accuracy:90.2%\r", + "Progress:8.81% Speed(reviews/sec):2974. #Correct:1991 #Tested:2205 Testing Accuracy:90.2%\r", + "Progress:8.82% Speed(reviews/sec):2974. #Correct:1992 #Tested:2206 Testing Accuracy:90.2%\r", + "Progress:8.82% Speed(reviews/sec):2975. #Correct:1993 #Tested:2207 Testing Accuracy:90.3%\r", + "Progress:8.82% Speed(reviews/sec):2976. #Correct:1994 #Tested:2208 Testing Accuracy:90.3%\r", + "Progress:8.83% Speed(reviews/sec):2977. #Correct:1995 #Tested:2209 Testing Accuracy:90.3%\r", + "Progress:8.83% Speed(reviews/sec):2977. #Correct:1996 #Tested:2210 Testing Accuracy:90.3%\r", + "Progress:8.84% Speed(reviews/sec):2977. #Correct:1997 #Tested:2211 Testing Accuracy:90.3%\r", + "Progress:8.84% Speed(reviews/sec):2978. #Correct:1998 #Tested:2212 Testing Accuracy:90.3%\r", + "Progress:8.84% Speed(reviews/sec):2979. #Correct:1999 #Tested:2213 Testing Accuracy:90.3%\r", + "Progress:8.85% Speed(reviews/sec):2979. #Correct:2000 #Tested:2214 Testing Accuracy:90.3%\r", + "Progress:8.85% Speed(reviews/sec):2978. #Correct:2001 #Tested:2215 Testing Accuracy:90.3%\r", + "Progress:8.86% Speed(reviews/sec):2979. #Correct:2002 #Tested:2216 Testing Accuracy:90.3%\r", + "Progress:8.86% Speed(reviews/sec):2980. #Correct:2003 #Tested:2217 Testing Accuracy:90.3%\r", + "Progress:8.86% Speed(reviews/sec):2979. #Correct:2004 #Tested:2218 Testing Accuracy:90.3%\r", + "Progress:8.87% Speed(reviews/sec):2977. #Correct:2005 #Tested:2219 Testing Accuracy:90.3%\r", + "Progress:8.87% Speed(reviews/sec):2978. #Correct:2006 #Tested:2220 Testing Accuracy:90.3%\r", + "Progress:8.88% Speed(reviews/sec):2976. #Correct:2007 #Tested:2221 Testing Accuracy:90.3%\r", + "Progress:8.88% Speed(reviews/sec):2977. #Correct:2008 #Tested:2222 Testing Accuracy:90.3%\r", + "Progress:8.88% Speed(reviews/sec):2977. #Correct:2009 #Tested:2223 Testing Accuracy:90.3%\r", + "Progress:8.89% Speed(reviews/sec):2978. #Correct:2010 #Tested:2224 Testing Accuracy:90.3%\r", + "Progress:8.89% Speed(reviews/sec):2978. #Correct:2011 #Tested:2225 Testing Accuracy:90.3%\r", + "Progress:8.9% Speed(reviews/sec):2979. #Correct:2012 #Tested:2226 Testing Accuracy:90.3%\r", + "Progress:8.90% Speed(reviews/sec):2977. #Correct:2013 #Tested:2227 Testing Accuracy:90.3%\r", + "Progress:8.90% Speed(reviews/sec):2977. #Correct:2014 #Tested:2228 Testing Accuracy:90.3%\r", + "Progress:8.91% Speed(reviews/sec):2976. #Correct:2015 #Tested:2229 Testing Accuracy:90.3%\r", + "Progress:8.91% Speed(reviews/sec):2977. #Correct:2016 #Tested:2230 Testing Accuracy:90.4%\r", + "Progress:8.92% Speed(reviews/sec):2976. #Correct:2017 #Tested:2231 Testing Accuracy:90.4%\r", + "Progress:8.92% Speed(reviews/sec):2977. #Correct:2018 #Tested:2232 Testing Accuracy:90.4%\r", + "Progress:8.92% Speed(reviews/sec):2977. #Correct:2019 #Tested:2233 Testing Accuracy:90.4%\r", + "Progress:8.93% Speed(reviews/sec):2977. #Correct:2020 #Tested:2234 Testing Accuracy:90.4%\r", + "Progress:8.93% Speed(reviews/sec):2974. #Correct:2021 #Tested:2235 Testing Accuracy:90.4%\r", + "Progress:8.94% Speed(reviews/sec):2975. #Correct:2022 #Tested:2236 Testing Accuracy:90.4%\r", + "Progress:8.94% Speed(reviews/sec):2973. 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Testing Accuracy:90.4%\r", + "Progress:8.98% Speed(reviews/sec):2974. #Correct:2033 #Tested:2248 Testing Accuracy:90.4%\r", + "Progress:8.99% Speed(reviews/sec):2975. #Correct:2034 #Tested:2249 Testing Accuracy:90.4%\r", + "Progress:8.99% Speed(reviews/sec):2975. #Correct:2035 #Tested:2250 Testing Accuracy:90.4%\r", + "Progress:9.0% Speed(reviews/sec):2975. #Correct:2036 #Tested:2251 Testing Accuracy:90.4%\r", + "Progress:9.00% Speed(reviews/sec):2974. #Correct:2037 #Tested:2252 Testing Accuracy:90.4%\r", + "Progress:9.00% Speed(reviews/sec):2974. #Correct:2038 #Tested:2253 Testing Accuracy:90.4%\r", + "Progress:9.01% Speed(reviews/sec):2974. #Correct:2039 #Tested:2254 Testing Accuracy:90.4%\r", + "Progress:9.01% Speed(reviews/sec):2974. #Correct:2040 #Tested:2255 Testing Accuracy:90.4%\r", + "Progress:9.02% Speed(reviews/sec):2974. #Correct:2041 #Tested:2256 Testing Accuracy:90.4%\r", + "Progress:9.02% Speed(reviews/sec):2975. #Correct:2042 #Tested:2257 Testing Accuracy:90.4%\r", + 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Accuracy:89.6%\r", + "Progress:72.3% Speed(reviews/sec):2890. #Correct:16229 #Tested:18100 Testing Accuracy:89.6%\r", + "Progress:72.4% Speed(reviews/sec):2890. #Correct:16230 #Tested:18101 Testing Accuracy:89.6%\r", + "Progress:72.4% Speed(reviews/sec):2890. #Correct:16231 #Tested:18102 Testing Accuracy:89.6%\r", + "Progress:72.4% Speed(reviews/sec):2890. #Correct:16232 #Tested:18103 Testing Accuracy:89.6%\r", + "Progress:72.4% Speed(reviews/sec):2890. #Correct:16233 #Tested:18104 Testing Accuracy:89.6%\r", + "Progress:72.4% Speed(reviews/sec):2890. #Correct:16234 #Tested:18105 Testing Accuracy:89.6%\r", + "Progress:72.4% Speed(reviews/sec):2890. #Correct:16235 #Tested:18106 Testing Accuracy:89.6%\r", + "Progress:72.4% Speed(reviews/sec):2890. #Correct:16236 #Tested:18107 Testing Accuracy:89.6%\r", + "Progress:72.4% Speed(reviews/sec):2890. #Correct:16237 #Tested:18108 Testing Accuracy:89.6%\r", + "Progress:72.4% Speed(reviews/sec):2890. #Correct:16238 #Tested:18109 Testing 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Speed(reviews/sec):2893. #Correct:19861 #Tested:22015 Testing Accuracy:90.2%\r", + "Progress:88.0% Speed(reviews/sec):2893. #Correct:19862 #Tested:22016 Testing Accuracy:90.2%\r", + "Progress:88.0% Speed(reviews/sec):2893. #Correct:19863 #Tested:22017 Testing Accuracy:90.2%\r", + "Progress:88.0% Speed(reviews/sec):2893. #Correct:19864 #Tested:22018 Testing Accuracy:90.2%\r", + "Progress:88.0% Speed(reviews/sec):2893. #Correct:19865 #Tested:22019 Testing Accuracy:90.2%\r", + "Progress:88.0% Speed(reviews/sec):2893. #Correct:19866 #Tested:22020 Testing Accuracy:90.2%\r", + "Progress:88.0% Speed(reviews/sec):2893. #Correct:19867 #Tested:22021 Testing Accuracy:90.2%\r", + "Progress:88.0% Speed(reviews/sec):2893. #Correct:19868 #Tested:22022 Testing Accuracy:90.2%\r", + "Progress:88.0% Speed(reviews/sec):2893. #Correct:19869 #Tested:22023 Testing Accuracy:90.2%\r", + "Progress:88.0% Speed(reviews/sec):2893. #Correct:19870 #Tested:22024 Testing Accuracy:90.2%\r", + "Progress:88.0% 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Speed(reviews/sec):2890. #Correct:19888 #Tested:22045 Testing Accuracy:90.2%\r", + "Progress:88.1% Speed(reviews/sec):2890. #Correct:19889 #Tested:22046 Testing Accuracy:90.2%\r", + "Progress:88.1% Speed(reviews/sec):2890. #Correct:19889 #Tested:22047 Testing Accuracy:90.2%\r", + "Progress:88.1% Speed(reviews/sec):2890. #Correct:19890 #Tested:22048 Testing Accuracy:90.2%\r", + "Progress:88.1% Speed(reviews/sec):2890. #Correct:19891 #Tested:22049 Testing Accuracy:90.2%\r", + "Progress:88.1% Speed(reviews/sec):2890. #Correct:19892 #Tested:22050 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19893 #Tested:22051 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19894 #Tested:22052 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19895 #Tested:22053 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19896 #Tested:22054 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19897 #Tested:22055 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19898 #Tested:22056 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19899 #Tested:22057 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19900 #Tested:22058 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19901 #Tested:22059 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19902 #Tested:22060 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19902 #Tested:22061 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19903 #Tested:22062 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19904 #Tested:22063 Testing Accuracy:90.2%\r", + "Progress:88.2% Speed(reviews/sec):2890. #Correct:19905 #Tested:22064 Testing Accuracy:90.2%\r", + "Progress:88.2% 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"mlp.test(reviews[:],labels[:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the following cell to train your network with a much larger polarity cutoff." + ] + }, + { + "cell_type": "code", + "execution_count": 305, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.00% Speed(reviews/sec):2045. #Correct:1 #Trained:2 Training Accuracy:50.0%\r", + "Progress:0.00% Speed(reviews/sec):3285. #Correct:2 #Trained:3 Training Accuracy:66.6%\r", + "Progress:0.01% Speed(reviews/sec):3419. #Correct:3 #Trained:4 Training Accuracy:75.0%\r", + "Progress:0.01% Speed(reviews/sec):4123. #Correct:4 #Trained:5 Training Accuracy:80.0%\r", + "Progress:0.02% Speed(reviews/sec):4853. #Correct:5 #Trained:6 Training Accuracy:83.3%\r", + 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Training Accuracy:84.5%\r", + "Progress:0.67% Speed(reviews/sec):7144. #Correct:138 #Trained:163 Training Accuracy:84.6%\r", + "Progress:0.67% Speed(reviews/sec):7102. #Correct:139 #Trained:164 Training Accuracy:84.7%\r", + "Progress:0.68% Speed(reviews/sec):7104. #Correct:140 #Trained:165 Training Accuracy:84.8%\r", + "Progress:0.68% Speed(reviews/sec):7108. #Correct:141 #Trained:166 Training Accuracy:84.9%\r", + "Progress:0.69% Speed(reviews/sec):7108. #Correct:142 #Trained:167 Training Accuracy:85.0%\r", + "Progress:0.69% Speed(reviews/sec):7115. #Correct:143 #Trained:168 Training Accuracy:85.1%\r", + "Progress:0.7% Speed(reviews/sec):7111. #Correct:144 #Trained:169 Training Accuracy:85.2%\r", + "Progress:0.70% Speed(reviews/sec):7105. #Correct:145 #Trained:170 Training Accuracy:85.2%\r", + "Progress:0.70% Speed(reviews/sec):7112. #Correct:146 #Trained:171 Training Accuracy:85.3%\r", + "Progress:0.71% Speed(reviews/sec):7101. #Correct:147 #Trained:172 Training Accuracy:85.4%\r", + "Progress:0.71% Speed(reviews/sec):7088. #Correct:148 #Trained:173 Training Accuracy:85.5%\r", + "Progress:0.72% Speed(reviews/sec):7082. #Correct:149 #Trained:174 Training Accuracy:85.6%\r", + "Progress:0.72% Speed(reviews/sec):7077. #Correct:150 #Trained:175 Training Accuracy:85.7%\r", + "Progress:0.72% Speed(reviews/sec):7047. #Correct:151 #Trained:176 Training Accuracy:85.7%\r", + "Progress:0.73% Speed(reviews/sec):7044. #Correct:152 #Trained:177 Training Accuracy:85.8%\r", + "Progress:0.73% Speed(reviews/sec):7035. #Correct:153 #Trained:178 Training Accuracy:85.9%\r", + "Progress:0.74% Speed(reviews/sec):7030. #Correct:153 #Trained:179 Training Accuracy:85.4%\r", + "Progress:0.74% Speed(reviews/sec):7033. #Correct:154 #Trained:180 Training Accuracy:85.5%\r", + "Progress:0.75% Speed(reviews/sec):7026. #Correct:155 #Trained:181 Training Accuracy:85.6%\r", + "Progress:0.75% Speed(reviews/sec):7027. #Correct:156 #Trained:182 Training Accuracy:85.7%\r", + "Progress:0.75% 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#Correct:165 #Trained:193 Training Accuracy:85.4%\r", + "Progress:0.80% Speed(reviews/sec):7055. #Correct:166 #Trained:194 Training Accuracy:85.5%\r", + "Progress:0.80% Speed(reviews/sec):7051. #Correct:166 #Trained:195 Training Accuracy:85.1%\r", + "Progress:0.81% Speed(reviews/sec):7053. #Correct:167 #Trained:196 Training Accuracy:85.2%\r", + "Progress:0.81% Speed(reviews/sec):7052. #Correct:168 #Trained:197 Training Accuracy:85.2%\r", + "Progress:0.82% Speed(reviews/sec):7003. #Correct:169 #Trained:198 Training Accuracy:85.3%\r", + "Progress:0.82% Speed(reviews/sec):7004. #Correct:170 #Trained:199 Training Accuracy:85.4%\r", + "Progress:0.82% Speed(reviews/sec):7002. #Correct:171 #Trained:200 Training Accuracy:85.5%\r", + "Progress:0.83% Speed(reviews/sec):6999. #Correct:172 #Trained:201 Training Accuracy:85.5%\r", + "Progress:0.83% Speed(reviews/sec):6997. #Correct:173 #Trained:202 Training Accuracy:85.6%\r", + "Progress:0.84% Speed(reviews/sec):6984. #Correct:174 #Trained:203 Training Accuracy:85.7%\r", + "Progress:0.84% Speed(reviews/sec):6986. #Correct:175 #Trained:204 Training Accuracy:85.7%\r", + "Progress:0.85% Speed(reviews/sec):6988. #Correct:176 #Trained:205 Training Accuracy:85.8%\r", + "Progress:0.85% Speed(reviews/sec):6981. #Correct:177 #Trained:206 Training Accuracy:85.9%\r", + "Progress:0.85% Speed(reviews/sec):6975. #Correct:178 #Trained:207 Training Accuracy:85.9%\r", + "Progress:0.86% Speed(reviews/sec):6974. #Correct:179 #Trained:208 Training Accuracy:86.0%\r", + "Progress:0.86% Speed(reviews/sec):6956. #Correct:180 #Trained:209 Training Accuracy:86.1%\r", + "Progress:0.87% Speed(reviews/sec):6959. #Correct:181 #Trained:210 Training Accuracy:86.1%\r", + "Progress:0.87% Speed(reviews/sec):6961. #Correct:182 #Trained:211 Training Accuracy:86.2%\r", + "Progress:0.87% Speed(reviews/sec):6958. #Correct:183 #Trained:212 Training Accuracy:86.3%\r", + "Progress:0.88% Speed(reviews/sec):6941. #Correct:184 #Trained:213 Training Accuracy:86.3%\r", + 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Speed(reviews/sec):7178. #Correct:1890 #Trained:2233 Training Accuracy:84.6%\r", + "Progress:9.30% Speed(reviews/sec):7178. #Correct:1891 #Trained:2234 Training Accuracy:84.6%\r", + "Progress:9.30% Speed(reviews/sec):7176. #Correct:1892 #Trained:2235 Training Accuracy:84.6%\r", + "Progress:9.31% Speed(reviews/sec):7174. #Correct:1893 #Trained:2236 Training Accuracy:84.6%\r", + "Progress:9.31% Speed(reviews/sec):7172. #Correct:1894 #Trained:2237 Training Accuracy:84.6%\r", + "Progress:9.32% Speed(reviews/sec):7171. #Correct:1895 #Trained:2238 Training Accuracy:84.6%\r", + "Progress:9.32% Speed(reviews/sec):7170. #Correct:1896 #Trained:2239 Training Accuracy:84.6%\r", + "Progress:9.32% Speed(reviews/sec):7169. #Correct:1897 #Trained:2240 Training Accuracy:84.6%\r", + "Progress:9.33% Speed(reviews/sec):7168. #Correct:1898 #Trained:2241 Training Accuracy:84.6%\r", + "Progress:9.33% Speed(reviews/sec):7168. #Correct:1899 #Trained:2242 Training Accuracy:84.7%\r", + "Progress:9.34% Speed(reviews/sec):7168. #Correct:1900 #Trained:2243 Training Accuracy:84.7%\r", + "Progress:9.34% Speed(reviews/sec):7168. #Correct:1901 #Trained:2244 Training Accuracy:84.7%\r", + "Progress:9.35% Speed(reviews/sec):7167. #Correct:1902 #Trained:2245 Training Accuracy:84.7%\r", + "Progress:9.35% Speed(reviews/sec):7165. #Correct:1902 #Trained:2246 Training Accuracy:84.6%\r", + "Progress:9.35% Speed(reviews/sec):7164. #Correct:1903 #Trained:2247 Training Accuracy:84.6%\r", + "Progress:9.36% Speed(reviews/sec):7164. #Correct:1904 #Trained:2248 Training Accuracy:84.6%\r", + "Progress:9.36% Speed(reviews/sec):7163. #Correct:1905 #Trained:2249 Training Accuracy:84.7%\r", + "Progress:9.37% Speed(reviews/sec):7162. #Correct:1906 #Trained:2250 Training Accuracy:84.7%\r", + "Progress:9.37% Speed(reviews/sec):7163. #Correct:1907 #Trained:2251 Training Accuracy:84.7%\r", + "Progress:9.37% Speed(reviews/sec):7158. #Correct:1908 #Trained:2252 Training Accuracy:84.7%\r", + "Progress:9.38% Speed(reviews/sec):7157. #Correct:1909 #Trained:2253 Training Accuracy:84.7%\r", + "Progress:9.38% Speed(reviews/sec):7157. #Correct:1910 #Trained:2254 Training Accuracy:84.7%\r", + "Progress:9.39% Speed(reviews/sec):7157. #Correct:1911 #Trained:2255 Training Accuracy:84.7%\r", + "Progress:9.39% Speed(reviews/sec):7156. #Correct:1912 #Trained:2256 Training Accuracy:84.7%\r", + "Progress:9.4% Speed(reviews/sec):7155. #Correct:1913 #Trained:2257 Training Accuracy:84.7%\r", + "Progress:9.40% Speed(reviews/sec):7155. #Correct:1914 #Trained:2258 Training Accuracy:84.7%\r", + "Progress:9.40% Speed(reviews/sec):7155. #Correct:1914 #Trained:2259 Training Accuracy:84.7%\r", + "Progress:9.41% Speed(reviews/sec):7155. #Correct:1915 #Trained:2260 Training Accuracy:84.7%\r", + "Progress:9.41% Speed(reviews/sec):7154. #Correct:1916 #Trained:2261 Training Accuracy:84.7%\r", + "Progress:9.42% Speed(reviews/sec):7154. #Correct:1917 #Trained:2262 Training Accuracy:84.7%\r", + "Progress:9.42% Speed(reviews/sec):7154. #Correct:1918 #Trained:2263 Training Accuracy:84.7%\r", + "Progress:9.42% Speed(reviews/sec):7154. #Correct:1919 #Trained:2264 Training Accuracy:84.7%\r", + "Progress:9.43% Speed(reviews/sec):7154. #Correct:1920 #Trained:2265 Training Accuracy:84.7%\r", + "Progress:9.43% Speed(reviews/sec):7154. #Correct:1920 #Trained:2266 Training Accuracy:84.7%\r", + "Progress:9.44% Speed(reviews/sec):7153. #Correct:1921 #Trained:2267 Training Accuracy:84.7%\r", + "Progress:9.44% Speed(reviews/sec):7152. #Correct:1922 #Trained:2268 Training Accuracy:84.7%\r", + "Progress:9.45% Speed(reviews/sec):7151. #Correct:1923 #Trained:2269 Training Accuracy:84.7%\r", + "Progress:9.45% Speed(reviews/sec):7149. #Correct:1924 #Trained:2270 Training Accuracy:84.7%\r", + "Progress:9.45% Speed(reviews/sec):7147. #Correct:1924 #Trained:2271 Training Accuracy:84.7%\r", + "Progress:9.46% Speed(reviews/sec):7146. #Correct:1925 #Trained:2272 Training Accuracy:84.7%\r", + "Progress:9.46% Speed(reviews/sec):7145. #Correct:1926 #Trained:2273 Training Accuracy:84.7%\r", + "Progress:9.47% Speed(reviews/sec):7144. #Correct:1927 #Trained:2274 Training Accuracy:84.7%\r", + "Progress:9.47% Speed(reviews/sec):7143. #Correct:1928 #Trained:2275 Training Accuracy:84.7%\r", + "Progress:9.47% Speed(reviews/sec):7141. #Correct:1928 #Trained:2276 Training Accuracy:84.7%\r", + "Progress:9.48% Speed(reviews/sec):7140. #Correct:1929 #Trained:2277 Training Accuracy:84.7%\r", + "Progress:9.48% Speed(reviews/sec):7139. #Correct:1930 #Trained:2278 Training Accuracy:84.7%\r", + "Progress:9.49% Speed(reviews/sec):7138. #Correct:1930 #Trained:2279 Training Accuracy:84.6%\r", + "Progress:9.49% Speed(reviews/sec):7135. #Correct:1931 #Trained:2280 Training Accuracy:84.6%\r", + "Progress:9.5% Speed(reviews/sec):7132. #Correct:1931 #Trained:2281 Training Accuracy:84.6%\r", + "Progress:9.50% Speed(reviews/sec):7132. #Correct:1931 #Trained:2282 Training Accuracy:84.6%\r", + "Progress:9.50% Speed(reviews/sec):7133. #Correct:1932 #Trained:2283 Training Accuracy:84.6%\r", + "Progress:9.51% Speed(reviews/sec):7130. #Correct:1933 #Trained:2284 Training Accuracy:84.6%\r", + "Progress:9.51% Speed(reviews/sec):7130. #Correct:1934 #Trained:2285 Training Accuracy:84.6%\r", + "Progress:9.52% Speed(reviews/sec):7129. #Correct:1935 #Trained:2286 Training Accuracy:84.6%\r", + "Progress:9.52% Speed(reviews/sec):7128. #Correct:1935 #Trained:2287 Training Accuracy:84.6%\r", + "Progress:9.52% Speed(reviews/sec):7123. #Correct:1936 #Trained:2288 Training Accuracy:84.6%\r", + "Progress:9.53% Speed(reviews/sec):7122. #Correct:1937 #Trained:2289 Training Accuracy:84.6%\r", + "Progress:9.53% Speed(reviews/sec):7122. #Correct:1937 #Trained:2290 Training Accuracy:84.5%\r", + "Progress:9.54% Speed(reviews/sec):7115. #Correct:1938 #Trained:2291 Training Accuracy:84.5%\r", + "Progress:9.54% Speed(reviews/sec):7115. #Correct:1939 #Trained:2292 Training Accuracy:84.5%\r", + "Progress:9.55% Speed(reviews/sec):7115. #Correct:1940 #Trained:2293 Training Accuracy:84.6%\r", + "Progress:9.55% Speed(reviews/sec):7114. #Correct:1941 #Trained:2294 Training Accuracy:84.6%\r", + "Progress:9.55% Speed(reviews/sec):7114. #Correct:1942 #Trained:2295 Training Accuracy:84.6%\r", + "Progress:9.56% Speed(reviews/sec):7112. #Correct:1943 #Trained:2296 Training Accuracy:84.6%\r", + "Progress:9.56% Speed(reviews/sec):7111. #Correct:1944 #Trained:2297 Training Accuracy:84.6%\r", + "Progress:9.57% Speed(reviews/sec):7111. #Correct:1944 #Trained:2298 Training Accuracy:84.5%\r", + "Progress:9.57% Speed(reviews/sec):7109. #Correct:1945 #Trained:2299 Training Accuracy:84.6%\r", + "Progress:9.57% Speed(reviews/sec):7109. #Correct:1946 #Trained:2300 Training Accuracy:84.6%\r", + "Progress:9.58% Speed(reviews/sec):7107. #Correct:1946 #Trained:2301 Training Accuracy:84.5%\r", + "Progress:9.58% Speed(reviews/sec):7108. #Correct:1946 #Trained:2302 Training Accuracy:84.5%\r", + "Progress:9.59% Speed(reviews/sec):7107. #Correct:1946 #Trained:2303 Training Accuracy:84.4%\r", + "Progress:9.59% Speed(reviews/sec):7108. #Correct:1947 #Trained:2304 Training Accuracy:84.5%\r", + "Progress:9.6% Speed(reviews/sec):7107. #Correct:1947 #Trained:2305 Training Accuracy:84.4%\r", + "Progress:9.60% Speed(reviews/sec):7108. #Correct:1947 #Trained:2306 Training Accuracy:84.4%\r", + "Progress:9.60% Speed(reviews/sec):7107. #Correct:1948 #Trained:2307 Training Accuracy:84.4%\r", + "Progress:9.61% Speed(reviews/sec):7107. #Correct:1948 #Trained:2308 Training Accuracy:84.4%\r", + "Progress:9.61% Speed(reviews/sec):7106. #Correct:1949 #Trained:2309 Training Accuracy:84.4%\r", + "Progress:9.62% Speed(reviews/sec):7107. #Correct:1950 #Trained:2310 Training Accuracy:84.4%\r", + "Progress:9.62% Speed(reviews/sec):7106. #Correct:1951 #Trained:2311 Training Accuracy:84.4%\r", + "Progress:9.62% Speed(reviews/sec):7108. #Correct:1951 #Trained:2312 Training Accuracy:84.3%\r", + "Progress:9.63% Speed(reviews/sec):7107. #Correct:1952 #Trained:2313 Training Accuracy:84.3%\r", + "Progress:9.63% Speed(reviews/sec):7107. #Correct:1953 #Trained:2314 Training Accuracy:84.3%\r", + "Progress:9.64% Speed(reviews/sec):7105. #Correct:1954 #Trained:2315 Training Accuracy:84.4%\r", + "Progress:9.64% Speed(reviews/sec):7105. #Correct:1955 #Trained:2316 Training Accuracy:84.4%\r", + "Progress:9.65% Speed(reviews/sec):7105. #Correct:1956 #Trained:2317 Training Accuracy:84.4%\r", + "Progress:9.65% Speed(reviews/sec):7104. #Correct:1957 #Trained:2318 Training Accuracy:84.4%\r", + "Progress:9.65% Speed(reviews/sec):7104. #Correct:1957 #Trained:2319 Training Accuracy:84.3%\r", + "Progress:9.66% Speed(reviews/sec):7103. #Correct:1958 #Trained:2320 Training Accuracy:84.3%\r", + "Progress:9.66% Speed(reviews/sec):7103. #Correct:1959 #Trained:2321 Training Accuracy:84.4%\r", + "Progress:9.67% Speed(reviews/sec):7102. #Correct:1960 #Trained:2322 Training Accuracy:84.4%\r", + "Progress:9.67% Speed(reviews/sec):7101. #Correct:1961 #Trained:2323 Training Accuracy:84.4%\r", + "Progress:9.67% Speed(reviews/sec):7102. #Correct:1962 #Trained:2324 Training Accuracy:84.4%\r", + "Progress:9.68% Speed(reviews/sec):7099. #Correct:1963 #Trained:2325 Training Accuracy:84.4%\r", + "Progress:9.68% Speed(reviews/sec):7099. #Correct:1964 #Trained:2326 Training Accuracy:84.4%\r", + "Progress:9.69% Speed(reviews/sec):7098. #Correct:1965 #Trained:2327 Training Accuracy:84.4%\r", + "Progress:9.69% Speed(reviews/sec):7099. #Correct:1966 #Trained:2328 Training Accuracy:84.4%\r", + "Progress:9.7% Speed(reviews/sec):7098. #Correct:1967 #Trained:2329 Training Accuracy:84.4%\r", + "Progress:9.70% Speed(reviews/sec):7099. #Correct:1968 #Trained:2330 Training Accuracy:84.4%\r", + "Progress:9.70% Speed(reviews/sec):7096. #Correct:1969 #Trained:2331 Training Accuracy:84.4%\r", + "Progress:9.71% Speed(reviews/sec):7096. #Correct:1970 #Trained:2332 Training Accuracy:84.4%\r", + "Progress:9.71% Speed(reviews/sec):7095. #Correct:1971 #Trained:2333 Training Accuracy:84.4%\r", + "Progress:9.72% Speed(reviews/sec):7094. #Correct:1972 #Trained:2334 Training Accuracy:84.4%\r", + "Progress:9.72% Speed(reviews/sec):7095. #Correct:1972 #Trained:2335 Training Accuracy:84.4%\r", + "Progress:9.72% Speed(reviews/sec):7094. #Correct:1973 #Trained:2336 Training Accuracy:84.4%\r", + "Progress:9.73% Speed(reviews/sec):7091. #Correct:1974 #Trained:2337 Training Accuracy:84.4%\r", + "Progress:9.73% Speed(reviews/sec):7090. #Correct:1975 #Trained:2338 Training Accuracy:84.4%\r", + "Progress:9.74% Speed(reviews/sec):7091. #Correct:1975 #Trained:2339 Training Accuracy:84.4%\r", + "Progress:9.74% Speed(reviews/sec):7090. #Correct:1976 #Trained:2340 Training Accuracy:84.4%\r", + "Progress:9.75% Speed(reviews/sec):7088. #Correct:1977 #Trained:2341 Training Accuracy:84.4%\r", + "Progress:9.75% Speed(reviews/sec):7088. #Correct:1978 #Trained:2342 Training Accuracy:84.4%\r", + "Progress:9.75% Speed(reviews/sec):7088. #Correct:1979 #Trained:2343 Training Accuracy:84.4%\r", + "Progress:9.76% Speed(reviews/sec):7085. #Correct:1979 #Trained:2344 Training Accuracy:84.4%\r", + "Progress:9.76% Speed(reviews/sec):7085. #Correct:1980 #Trained:2345 Training Accuracy:84.4%\r", + "Progress:9.77% Speed(reviews/sec):7084. #Correct:1980 #Trained:2346 Training Accuracy:84.3%\r", + "Progress:9.77% Speed(reviews/sec):7084. #Correct:1981 #Trained:2347 Training Accuracy:84.4%\r", + "Progress:9.77% Speed(reviews/sec):7083. #Correct:1981 #Trained:2348 Training Accuracy:84.3%\r", + "Progress:9.78% Speed(reviews/sec):7082. #Correct:1982 #Trained:2349 Training Accuracy:84.3%\r", + "Progress:9.78% Speed(reviews/sec):7080. #Correct:1983 #Trained:2350 Training Accuracy:84.3%\r", + "Progress:9.79% Speed(reviews/sec):7080. #Correct:1984 #Trained:2351 Training Accuracy:84.3%\r", + "Progress:9.79% Speed(reviews/sec):7078. #Correct:1985 #Trained:2352 Training Accuracy:84.3%\r", + "Progress:9.8% Speed(reviews/sec):7077. #Correct:1986 #Trained:2353 Training Accuracy:84.4%\r", + "Progress:9.80% Speed(reviews/sec):7077. #Correct:1987 #Trained:2354 Training Accuracy:84.4%\r", + "Progress:9.80% Speed(reviews/sec):7076. #Correct:1988 #Trained:2355 Training Accuracy:84.4%\r", + "Progress:9.81% Speed(reviews/sec):7076. #Correct:1989 #Trained:2356 Training Accuracy:84.4%\r", + "Progress:9.81% Speed(reviews/sec):7076. #Correct:1990 #Trained:2357 Training Accuracy:84.4%\r", + "Progress:9.82% Speed(reviews/sec):7076. #Correct:1991 #Trained:2358 Training Accuracy:84.4%\r", + "Progress:9.82% Speed(reviews/sec):7076. #Correct:1992 #Trained:2359 Training Accuracy:84.4%\r", + "Progress:9.82% Speed(reviews/sec):7076. #Correct:1993 #Trained:2360 Training Accuracy:84.4%\r", + "Progress:9.83% Speed(reviews/sec):7076. #Correct:1994 #Trained:2361 Training Accuracy:84.4%\r", + "Progress:9.83% Speed(reviews/sec):7076. #Correct:1995 #Trained:2362 Training Accuracy:84.4%\r", + "Progress:9.84% Speed(reviews/sec):7076. #Correct:1996 #Trained:2363 Training Accuracy:84.4%\r", + "Progress:9.84% Speed(reviews/sec):7076. #Correct:1997 #Trained:2364 Training Accuracy:84.4%\r", + "Progress:9.85% Speed(reviews/sec):7077. #Correct:1998 #Trained:2365 Training Accuracy:84.4%\r", + "Progress:9.85% Speed(reviews/sec):7076. #Correct:1999 #Trained:2366 Training Accuracy:84.4%\r", + "Progress:9.85% Speed(reviews/sec):7075. #Correct:2000 #Trained:2367 Training Accuracy:84.4%\r", + "Progress:9.86% Speed(reviews/sec):7075. #Correct:2001 #Trained:2368 Training Accuracy:84.5%\r", + "Progress:9.86% Speed(reviews/sec):7076. #Correct:2001 #Trained:2369 Training Accuracy:84.4%\r", + "Progress:9.87% Speed(reviews/sec):7076. #Correct:2002 #Trained:2370 Training Accuracy:84.4%\r", + "Progress:9.87% Speed(reviews/sec):7076. #Correct:2003 #Trained:2371 Training Accuracy:84.4%\r", + "Progress:9.87% Speed(reviews/sec):7076. #Correct:2004 #Trained:2372 Training Accuracy:84.4%\r", + "Progress:9.88% Speed(reviews/sec):7075. #Correct:2005 #Trained:2373 Training Accuracy:84.4%\r", + "Progress:9.88% Speed(reviews/sec):7073. #Correct:2006 #Trained:2374 Training Accuracy:84.4%\r", + "Progress:9.89% Speed(reviews/sec):7073. #Correct:2007 #Trained:2375 Training Accuracy:84.5%\r", + "Progress:9.89% Speed(reviews/sec):7073. #Correct:2008 #Trained:2376 Training Accuracy:84.5%\r", + "Progress:9.9% Speed(reviews/sec):7071. #Correct:2009 #Trained:2377 Training Accuracy:84.5%\r", + "Progress:9.90% Speed(reviews/sec):7070. #Correct:2010 #Trained:2378 Training Accuracy:84.5%\r", + "Progress:9.90% Speed(reviews/sec):7069. #Correct:2010 #Trained:2379 Training Accuracy:84.4%\r", + "Progress:9.91% Speed(reviews/sec):7070. #Correct:2011 #Trained:2380 Training Accuracy:84.4%\r", + "Progress:9.91% Speed(reviews/sec):7071. #Correct:2012 #Trained:2381 Training Accuracy:84.5%\r", + "Progress:9.92% Speed(reviews/sec):7072. #Correct:2013 #Trained:2382 Training Accuracy:84.5%\r", + "Progress:9.92% Speed(reviews/sec):7074. #Correct:2014 #Trained:2383 Training Accuracy:84.5%\r", + "Progress:9.92% Speed(reviews/sec):7075. #Correct:2015 #Trained:2384 Training Accuracy:84.5%\r", + "Progress:9.93% Speed(reviews/sec):7076. #Correct:2016 #Trained:2385 Training Accuracy:84.5%\r", + "Progress:9.93% Speed(reviews/sec):7077. #Correct:2017 #Trained:2386 Training Accuracy:84.5%\r", + "Progress:9.94% Speed(reviews/sec):7077. #Correct:2018 #Trained:2387 Training Accuracy:84.5%\r", + "Progress:9.94% Speed(reviews/sec):7069. #Correct:2018 #Trained:2388 Training Accuracy:84.5%\r", + "Progress:9.95% Speed(reviews/sec):7070. #Correct:2019 #Trained:2389 Training Accuracy:84.5%\r", + "Progress:9.95% Speed(reviews/sec):7071. #Correct:2019 #Trained:2390 Training Accuracy:84.4%\r", + "Progress:9.95% Speed(reviews/sec):7073. #Correct:2020 #Trained:2391 Training Accuracy:84.4%\r", + "Progress:9.96% Speed(reviews/sec):7071. #Correct:2020 #Trained:2392 Training Accuracy:84.4%\r", + "Progress:9.96% Speed(reviews/sec):7072. #Correct:2021 #Trained:2393 Training Accuracy:84.4%\r", + "Progress:9.97% Speed(reviews/sec):7072. #Correct:2022 #Trained:2394 Training Accuracy:84.4%\r", + "Progress:9.97% Speed(reviews/sec):7065. #Correct:2023 #Trained:2395 Training Accuracy:84.4%\r", + "Progress:9.97% Speed(reviews/sec):7066. #Correct:2024 #Trained:2396 Training Accuracy:84.4%\r", + "Progress:9.98% Speed(reviews/sec):7067. #Correct:2025 #Trained:2397 Training Accuracy:84.4%\r", + "Progress:9.98% Speed(reviews/sec):7069. #Correct:2026 #Trained:2398 Training Accuracy:84.4%\r", + "Progress:9.99% Speed(reviews/sec):7068. #Correct:2027 #Trained:2399 Training Accuracy:84.4%\r", + "Progress:9.99% Speed(reviews/sec):7063. #Correct:2027 #Trained:2400 Training Accuracy:84.4%\r", + "Progress:10.0% Speed(reviews/sec):7062. #Correct:2028 #Trained:2401 Training Accuracy:84.4%\r", + "Progress:10.0% Speed(reviews/sec):7064. #Correct:2029 #Trained:2402 Training Accuracy:84.4%\r", + "Progress:10.0% 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Training Accuracy:85.1%\r", + "Progress:68.2% Speed(reviews/sec):5534. #Correct:13949 #Trained:16385 Training Accuracy:85.1%\r", + "Progress:68.2% Speed(reviews/sec):5534. #Correct:13950 #Trained:16386 Training Accuracy:85.1%\r", + "Progress:68.2% Speed(reviews/sec):5534. #Correct:13950 #Trained:16387 Training Accuracy:85.1%\r", + "Progress:68.2% Speed(reviews/sec):5534. #Correct:13951 #Trained:16388 Training Accuracy:85.1%\r", + "Progress:68.2% Speed(reviews/sec):5534. #Correct:13952 #Trained:16389 Training Accuracy:85.1%\r", + "Progress:68.2% Speed(reviews/sec):5534. #Correct:13953 #Trained:16390 Training Accuracy:85.1%\r", + "Progress:68.2% Speed(reviews/sec):5534. #Correct:13954 #Trained:16391 Training Accuracy:85.1%\r", + "Progress:68.2% Speed(reviews/sec):5534. #Correct:13955 #Trained:16392 Training Accuracy:85.1%\r", + "Progress:68.3% Speed(reviews/sec):5535. #Correct:13955 #Trained:16393 Training Accuracy:85.1%\r", + "Progress:68.3% Speed(reviews/sec):5535. #Correct:13956 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Speed(reviews/sec):5539. #Correct:13970 #Trained:16414 Training Accuracy:85.1%\r", + "Progress:68.3% Speed(reviews/sec):5539. #Correct:13971 #Trained:16415 Training Accuracy:85.1%\r", + "Progress:68.3% Speed(reviews/sec):5539. #Correct:13972 #Trained:16416 Training Accuracy:85.1%\r", + "Progress:68.4% Speed(reviews/sec):5539. #Correct:13973 #Trained:16417 Training Accuracy:85.1%\r", + "Progress:68.4% Speed(reviews/sec):5539. #Correct:13974 #Trained:16418 Training Accuracy:85.1%\r", + "Progress:68.4% Speed(reviews/sec):5539. #Correct:13975 #Trained:16419 Training Accuracy:85.1%\r", + "Progress:68.4% Speed(reviews/sec):5537. #Correct:13976 #Trained:16420 Training Accuracy:85.1%\r", + "Progress:68.4% Speed(reviews/sec):5537. #Correct:13977 #Trained:16421 Training Accuracy:85.1%\r", + "Progress:68.4% Speed(reviews/sec):5537. #Correct:13978 #Trained:16422 Training Accuracy:85.1%\r", + "Progress:68.4% Speed(reviews/sec):5537. #Correct:13979 #Trained:16423 Training Accuracy:85.1%\r", + 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Accuracy:85.3%\r", + "Progress:82.8% Speed(reviews/sec):5479. #Correct:16980 #Trained:19893 Training Accuracy:85.3%\r", + "Progress:82.8% Speed(reviews/sec):5479. #Correct:16981 #Trained:19894 Training Accuracy:85.3%\r", + "Progress:82.8% Speed(reviews/sec):5479. #Correct:16982 #Trained:19895 Training Accuracy:85.3%\r", + "Progress:82.8% Speed(reviews/sec):5479. #Correct:16983 #Trained:19896 Training Accuracy:85.3%\r", + "Progress:82.9% Speed(reviews/sec):5479. #Correct:16984 #Trained:19897 Training Accuracy:85.3%\r", + "Progress:82.9% Speed(reviews/sec):5479. #Correct:16984 #Trained:19898 Training Accuracy:85.3%\r", + "Progress:82.9% Speed(reviews/sec):5479. #Correct:16984 #Trained:19899 Training Accuracy:85.3%\r", + "Progress:82.9% Speed(reviews/sec):5479. #Correct:16985 #Trained:19900 Training Accuracy:85.3%\r", + "Progress:82.9% Speed(reviews/sec):5479. #Correct:16986 #Trained:19901 Training Accuracy:85.3%\r", + "Progress:82.9% Speed(reviews/sec):5479. #Correct:16987 #Trained:19902 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Training Accuracy:85.3%\r", + "Progress:83.1% Speed(reviews/sec):5480. #Correct:17042 #Trained:19962 Training Accuracy:85.3%\r", + "Progress:83.1% Speed(reviews/sec):5480. #Correct:17042 #Trained:19963 Training Accuracy:85.3%\r", + "Progress:83.1% Speed(reviews/sec):5480. #Correct:17043 #Trained:19964 Training Accuracy:85.3%\r", + "Progress:83.1% Speed(reviews/sec):5480. #Correct:17044 #Trained:19965 Training Accuracy:85.3%\r", + "Progress:83.1% Speed(reviews/sec):5480. #Correct:17045 #Trained:19966 Training Accuracy:85.3%\r", + "Progress:83.1% Speed(reviews/sec):5481. #Correct:17046 #Trained:19967 Training Accuracy:85.3%\r", + "Progress:83.1% Speed(reviews/sec):5481. #Correct:17047 #Trained:19968 Training Accuracy:85.3%\r", + "Progress:83.2% Speed(reviews/sec):5481. #Correct:17048 #Trained:19969 Training Accuracy:85.3%\r", + "Progress:83.2% Speed(reviews/sec):5481. #Correct:17049 #Trained:19970 Training Accuracy:85.3%\r", + "Progress:83.2% Speed(reviews/sec):5481. #Correct:17050 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Accuracy:85.4%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17571 #Trained:20551 Training Accuracy:85.4%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17572 #Trained:20552 Training Accuracy:85.5%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17573 #Trained:20553 Training Accuracy:85.5%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17574 #Trained:20554 Training Accuracy:85.5%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17575 #Trained:20555 Training Accuracy:85.5%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17576 #Trained:20556 Training Accuracy:85.5%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17577 #Trained:20557 Training Accuracy:85.5%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17577 #Trained:20558 Training Accuracy:85.4%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17578 #Trained:20559 Training Accuracy:85.5%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17579 #Trained:20560 Training Accuracy:85.5%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17579 #Trained:20561 Training Accuracy:85.4%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17580 #Trained:20562 Training Accuracy:85.4%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17581 #Trained:20563 Training Accuracy:85.4%\r", + "Progress:85.6% Speed(reviews/sec):5453. #Correct:17582 #Trained:20564 Training Accuracy:85.4%\r", + "Progress:85.6% Speed(reviews/sec):5452. #Correct:17583 #Trained:20565 Training Accuracy:85.4%\r", + "Progress:85.6% Speed(reviews/sec):5451. #Correct:17584 #Trained:20566 Training Accuracy:85.5%\r", + "Progress:85.6% Speed(reviews/sec):5451. #Correct:17585 #Trained:20567 Training Accuracy:85.5%\r", + "Progress:85.6% Speed(reviews/sec):5451. #Correct:17586 #Trained:20568 Training Accuracy:85.5%\r", + "Progress:85.7% Speed(reviews/sec):5451. #Correct:17587 #Trained:20569 Training Accuracy:85.5%\r", + "Progress:85.7% Speed(reviews/sec):5450. #Correct:17588 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Training Accuracy:85.5%\r", + "Progress:85.9% Speed(reviews/sec):5447. #Correct:17631 #Trained:20620 Training Accuracy:85.5%\r", + "Progress:85.9% Speed(reviews/sec):5447. #Correct:17632 #Trained:20621 Training Accuracy:85.5%\r", + "Progress:85.9% Speed(reviews/sec):5447. #Correct:17633 #Trained:20622 Training Accuracy:85.5%\r", + "Progress:85.9% Speed(reviews/sec):5446. #Correct:17634 #Trained:20623 Training Accuracy:85.5%\r", + "Progress:85.9% Speed(reviews/sec):5447. #Correct:17635 #Trained:20624 Training Accuracy:85.5%\r", + "Progress:85.9% Speed(reviews/sec):5447. #Correct:17635 #Trained:20625 Training Accuracy:85.5%\r", + "Progress:85.9% Speed(reviews/sec):5447. #Correct:17636 #Trained:20626 Training Accuracy:85.5%\r", + "Progress:85.9% Speed(reviews/sec):5447. #Correct:17637 #Trained:20627 Training Accuracy:85.5%\r", + "Progress:85.9% Speed(reviews/sec):5447. #Correct:17637 #Trained:20628 Training Accuracy:85.5%\r", + "Progress:85.9% Speed(reviews/sec):5447. #Correct:17638 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Training Accuracy:85.5%\r", + "Progress:86.1% Speed(reviews/sec):5450. #Correct:17681 #Trained:20679 Training Accuracy:85.5%\r", + "Progress:86.1% Speed(reviews/sec):5450. #Correct:17682 #Trained:20680 Training Accuracy:85.5%\r", + "Progress:86.1% Speed(reviews/sec):5450. #Correct:17683 #Trained:20681 Training Accuracy:85.5%\r", + "Progress:86.1% Speed(reviews/sec):5450. #Correct:17684 #Trained:20682 Training Accuracy:85.5%\r", + "Progress:86.1% Speed(reviews/sec):5450. #Correct:17684 #Trained:20683 Training Accuracy:85.5%\r", + "Progress:86.1% Speed(reviews/sec):5451. #Correct:17685 #Trained:20684 Training Accuracy:85.5%\r", + "Progress:86.1% Speed(reviews/sec):5451. #Correct:17686 #Trained:20685 Training Accuracy:85.5%\r", + "Progress:86.1% Speed(reviews/sec):5451. #Correct:17686 #Trained:20686 Training Accuracy:85.4%\r", + "Progress:86.1% Speed(reviews/sec):5451. #Correct:17687 #Trained:20687 Training Accuracy:85.4%\r", + "Progress:86.1% Speed(reviews/sec):5451. #Correct:17688 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Training Accuracy:85.5%\r", + "Progress:87.1% Speed(reviews/sec):5444. #Correct:17890 #Trained:20915 Training Accuracy:85.5%\r", + "Progress:87.1% Speed(reviews/sec):5444. #Correct:17890 #Trained:20916 Training Accuracy:85.5%\r", + "Progress:87.1% Speed(reviews/sec):5444. #Correct:17891 #Trained:20917 Training Accuracy:85.5%\r", + "Progress:87.1% Speed(reviews/sec):5444. #Correct:17892 #Trained:20918 Training Accuracy:85.5%\r", + "Progress:87.1% Speed(reviews/sec):5443. #Correct:17892 #Trained:20919 Training Accuracy:85.5%\r", + "Progress:87.1% Speed(reviews/sec):5443. #Correct:17893 #Trained:20920 Training Accuracy:85.5%\r", + "Progress:87.1% Speed(reviews/sec):5443. #Correct:17894 #Trained:20921 Training Accuracy:85.5%\r", + "Progress:87.1% Speed(reviews/sec):5443. #Correct:17895 #Trained:20922 Training Accuracy:85.5%\r", + "Progress:87.1% Speed(reviews/sec):5443. #Correct:17896 #Trained:20923 Training Accuracy:85.5%\r", + "Progress:87.1% Speed(reviews/sec):5443. #Correct:17897 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Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5436. #Correct:17930 #Trained:20964 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5436. #Correct:17931 #Trained:20965 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5436. #Correct:17932 #Trained:20966 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5436. #Correct:17933 #Trained:20967 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5437. #Correct:17934 #Trained:20968 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5437. #Correct:17935 #Trained:20969 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5437. #Correct:17936 #Trained:20970 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5437. #Correct:17937 #Trained:20971 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5437. #Correct:17938 #Trained:20972 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5437. #Correct:17939 #Trained:20973 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5437. #Correct:17940 #Trained:20974 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5437. #Correct:17941 #Trained:20975 Training Accuracy:85.5%\r", + "Progress:87.3% Speed(reviews/sec):5438. #Correct:17942 #Trained:20976 Training Accuracy:85.5%\r", + "Progress:87.4% Speed(reviews/sec):5437. #Correct:17943 #Trained:20977 Training Accuracy:85.5%\r", + "Progress:87.4% Speed(reviews/sec):5437. #Correct:17944 #Trained:20978 Training Accuracy:85.5%\r", + "Progress:87.4% Speed(reviews/sec):5437. #Correct:17945 #Trained:20979 Training Accuracy:85.5%\r", + "Progress:87.4% Speed(reviews/sec):5437. #Correct:17946 #Trained:20980 Training Accuracy:85.5%\r", + "Progress:87.4% Speed(reviews/sec):5437. #Correct:17947 #Trained:20981 Training Accuracy:85.5%\r", + "Progress:87.4% Speed(reviews/sec):5437. #Correct:17948 #Trained:20982 Training Accuracy:85.5%\r", + "Progress:87.4% Speed(reviews/sec):5437. #Correct:17948 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Training Accuracy:85.5%\r", + "Progress:87.6% Speed(reviews/sec):5437. #Correct:17990 #Trained:21033 Training Accuracy:85.5%\r", + "Progress:87.6% Speed(reviews/sec):5437. #Correct:17991 #Trained:21034 Training Accuracy:85.5%\r", + "Progress:87.6% Speed(reviews/sec):5437. #Correct:17992 #Trained:21035 Training Accuracy:85.5%\r", + "Progress:87.6% Speed(reviews/sec):5438. #Correct:17993 #Trained:21036 Training Accuracy:85.5%\r", + "Progress:87.6% Speed(reviews/sec):5438. #Correct:17994 #Trained:21037 Training Accuracy:85.5%\r", + "Progress:87.6% Speed(reviews/sec):5438. #Correct:17995 #Trained:21038 Training Accuracy:85.5%\r", + "Progress:87.6% Speed(reviews/sec):5437. #Correct:17996 #Trained:21039 Training Accuracy:85.5%\r", + "Progress:87.6% Speed(reviews/sec):5437. #Correct:17997 #Trained:21040 Training Accuracy:85.5%\r", + "Progress:87.6% Speed(reviews/sec):5437. #Correct:17998 #Trained:21041 Training Accuracy:85.5%\r", + "Progress:87.6% Speed(reviews/sec):5438. #Correct:17999 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Training Accuracy:85.5%\r", + "Progress:90.3% Speed(reviews/sec):5422. #Correct:18563 #Trained:21691 Training Accuracy:85.5%\r", + "Progress:90.3% Speed(reviews/sec):5422. #Correct:18563 #Trained:21692 Training Accuracy:85.5%\r", + "Progress:90.3% Speed(reviews/sec):5422. #Correct:18563 #Trained:21693 Training Accuracy:85.5%\r", + "Progress:90.3% Speed(reviews/sec):5423. #Correct:18564 #Trained:21694 Training Accuracy:85.5%\r", + "Progress:90.3% Speed(reviews/sec):5423. #Correct:18565 #Trained:21695 Training Accuracy:85.5%\r", + "Progress:90.3% Speed(reviews/sec):5423. #Correct:18566 #Trained:21696 Training Accuracy:85.5%\r", + "Progress:90.4% Speed(reviews/sec):5423. #Correct:18567 #Trained:21697 Training Accuracy:85.5%\r", + "Progress:90.4% Speed(reviews/sec):5423. #Correct:18568 #Trained:21698 Training Accuracy:85.5%\r", + "Progress:90.4% Speed(reviews/sec):5423. #Correct:18569 #Trained:21699 Training Accuracy:85.5%\r", + "Progress:90.4% Speed(reviews/sec):5423. #Correct:18570 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Accuracy:85.5%\r", + "Progress:91.0% Speed(reviews/sec):5419. #Correct:18697 #Trained:21858 Training Accuracy:85.5%\r", + "Progress:91.0% Speed(reviews/sec):5419. #Correct:18698 #Trained:21859 Training Accuracy:85.5%\r", + "Progress:91.0% Speed(reviews/sec):5419. #Correct:18699 #Trained:21860 Training Accuracy:85.5%\r", + "Progress:91.0% Speed(reviews/sec):5419. #Correct:18700 #Trained:21861 Training Accuracy:85.5%\r", + "Progress:91.0% Speed(reviews/sec):5419. #Correct:18701 #Trained:21862 Training Accuracy:85.5%\r", + "Progress:91.0% Speed(reviews/sec):5418. #Correct:18701 #Trained:21863 Training Accuracy:85.5%\r", + "Progress:91.0% Speed(reviews/sec):5418. #Correct:18702 #Trained:21864 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5418. #Correct:18703 #Trained:21865 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5418. #Correct:18704 #Trained:21866 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5418. #Correct:18705 #Trained:21867 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5418. #Correct:18706 #Trained:21868 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5418. #Correct:18706 #Trained:21869 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5418. #Correct:18707 #Trained:21870 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5417. #Correct:18708 #Trained:21871 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5417. #Correct:18709 #Trained:21872 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5417. #Correct:18709 #Trained:21873 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5416. #Correct:18710 #Trained:21874 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5416. #Correct:18711 #Trained:21875 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5416. #Correct:18712 #Trained:21876 Training Accuracy:85.5%\r", + "Progress:91.1% Speed(reviews/sec):5416. #Correct:18713 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Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18753 #Trained:21917 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18754 #Trained:21918 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18755 #Trained:21919 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18756 #Trained:21920 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18757 #Trained:21921 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18758 #Trained:21922 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18759 #Trained:21923 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18760 #Trained:21924 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18761 #Trained:21925 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5412. #Correct:18762 #Trained:21926 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5412. #Correct:18762 #Trained:21927 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5412. #Correct:18763 #Trained:21928 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18764 #Trained:21929 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18765 #Trained:21930 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18766 #Trained:21931 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18767 #Trained:21932 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18768 #Trained:21933 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18768 #Trained:21934 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18769 #Trained:21935 Training Accuracy:85.5%\r", + "Progress:91.3% Speed(reviews/sec):5411. #Correct:18770 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Speed(reviews/sec):5412. #Correct:18786 #Trained:21956 Training Accuracy:85.5%\r", + "Progress:91.4% Speed(reviews/sec):5412. #Correct:18787 #Trained:21957 Training Accuracy:85.5%\r", + "Progress:91.4% Speed(reviews/sec):5412. #Correct:18788 #Trained:21958 Training Accuracy:85.5%\r", + "Progress:91.4% Speed(reviews/sec):5412. #Correct:18789 #Trained:21959 Training Accuracy:85.5%\r", + "Progress:91.4% Speed(reviews/sec):5412. #Correct:18790 #Trained:21960 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18791 #Trained:21961 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18792 #Trained:21962 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18793 #Trained:21963 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18794 #Trained:21964 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18794 #Trained:21965 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18795 #Trained:21966 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18795 #Trained:21967 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18796 #Trained:21968 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18797 #Trained:21969 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18798 #Trained:21970 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18798 #Trained:21971 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5412. #Correct:18799 #Trained:21972 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5413. #Correct:18800 #Trained:21973 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5413. #Correct:18801 #Trained:21974 Training Accuracy:85.5%\r", + "Progress:91.5% Speed(reviews/sec):5413. #Correct:18802 #Trained:21975 Training 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Training Accuracy:85.5%\r", + "Progress:91.6% Speed(reviews/sec):5413. #Correct:18813 #Trained:21986 Training Accuracy:85.5%\r", + "Progress:91.6% Speed(reviews/sec):5413. #Correct:18814 #Trained:21987 Training Accuracy:85.5%\r", + "Progress:91.6% Speed(reviews/sec):5413. #Correct:18815 #Trained:21988 Training Accuracy:85.5%\r", + "Progress:91.6% Speed(reviews/sec):5413. #Correct:18815 #Trained:21989 Training Accuracy:85.5%\r", + "Progress:91.6% Speed(reviews/sec):5413. #Correct:18816 #Trained:21990 Training Accuracy:85.5%\r", + "Progress:91.6% Speed(reviews/sec):5413. #Correct:18816 #Trained:21991 Training Accuracy:85.5%\r", + "Progress:91.6% Speed(reviews/sec):5413. #Correct:18817 #Trained:21992 Training Accuracy:85.5%\r", + "Progress:91.6% Speed(reviews/sec):5413. #Correct:18818 #Trained:21993 Training Accuracy:85.5%\r", + "Progress:91.6% Speed(reviews/sec):5412. #Correct:18819 #Trained:21994 Training Accuracy:85.5%\r", + "Progress:91.6% Speed(reviews/sec):5412. #Correct:18820 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Speed(reviews/sec):5412. #Correct:18838 #Trained:22015 Training Accuracy:85.5%\r", + "Progress:91.7% Speed(reviews/sec):5412. #Correct:18839 #Trained:22016 Training Accuracy:85.5%\r", + "Progress:91.7% Speed(reviews/sec):5413. #Correct:18840 #Trained:22017 Training Accuracy:85.5%\r", + "Progress:91.7% Speed(reviews/sec):5413. #Correct:18841 #Trained:22018 Training Accuracy:85.5%\r", + "Progress:91.7% Speed(reviews/sec):5413. #Correct:18842 #Trained:22019 Training Accuracy:85.5%\r", + "Progress:91.7% Speed(reviews/sec):5412. #Correct:18843 #Trained:22020 Training Accuracy:85.5%\r", + "Progress:91.7% Speed(reviews/sec):5412. #Correct:18844 #Trained:22021 Training Accuracy:85.5%\r", + "Progress:91.7% Speed(reviews/sec):5412. #Correct:18845 #Trained:22022 Training Accuracy:85.5%\r", + "Progress:91.7% Speed(reviews/sec):5412. #Correct:18846 #Trained:22023 Training Accuracy:85.5%\r", + "Progress:91.7% Speed(reviews/sec):5413. #Correct:18847 #Trained:22024 Training Accuracy:85.5%\r", + 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Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18857 #Trained:22035 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18858 #Trained:22036 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18859 #Trained:22037 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18860 #Trained:22038 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18860 #Trained:22039 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18860 #Trained:22040 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18861 #Trained:22041 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18862 #Trained:22042 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18863 #Trained:22043 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18863 #Trained:22044 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18864 #Trained:22045 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18865 #Trained:22046 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18865 #Trained:22047 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18866 #Trained:22048 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18866 #Trained:22049 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18867 #Trained:22050 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18868 #Trained:22051 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18869 #Trained:22052 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18870 #Trained:22053 Training Accuracy:85.5%\r", + "Progress:91.8% Speed(reviews/sec):5414. #Correct:18871 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"Progress:92.0% Speed(reviews/sec):5414. #Correct:18895 #Trained:22084 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18895 #Trained:22085 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18896 #Trained:22086 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18897 #Trained:22087 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5413. #Correct:18898 #Trained:22088 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5413. #Correct:18899 #Trained:22089 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5413. #Correct:18899 #Trained:22090 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18899 #Trained:22091 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18899 #Trained:22092 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18900 #Trained:22093 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5413. #Correct:18901 #Trained:22094 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18902 #Trained:22095 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18903 #Trained:22096 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18904 #Trained:22097 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18905 #Trained:22098 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5413. #Correct:18906 #Trained:22099 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5413. #Correct:18907 #Trained:22100 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18908 #Trained:22101 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18909 #Trained:22102 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18910 #Trained:22103 Training Accuracy:85.5%\r", + "Progress:92.0% Speed(reviews/sec):5414. #Correct:18911 #Trained:22104 Training Accuracy:85.5%\r", + "Progress:92.1% Speed(reviews/sec):5414. #Correct:18912 #Trained:22105 Training Accuracy:85.5%\r", + "Progress:92.1% Speed(reviews/sec):5414. #Correct:18913 #Trained:22106 Training Accuracy:85.5%\r", + "Progress:92.1% Speed(reviews/sec):5414. #Correct:18914 #Trained:22107 Training Accuracy:85.5%\r", + "Progress:92.1% Speed(reviews/sec):5414. #Correct:18915 #Trained:22108 Training Accuracy:85.5%\r", + "Progress:92.1% Speed(reviews/sec):5414. #Correct:18915 #Trained:22109 Training Accuracy:85.5%\r", + "Progress:92.1% Speed(reviews/sec):5414. #Correct:18916 #Trained:22110 Training Accuracy:85.5%\r", + "Progress:92.1% Speed(reviews/sec):5414. #Correct:18917 #Trained:22111 Training Accuracy:85.5%\r", + "Progress:92.1% Speed(reviews/sec):5414. #Correct:18918 #Trained:22112 Training Accuracy:85.5%\r", + "Progress:92.1% Speed(reviews/sec):5414. #Correct:18919 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Training Accuracy:85.5%\r", + "Progress:92.3% Speed(reviews/sec):5416. #Correct:18964 #Trained:22163 Training Accuracy:85.5%\r", + "Progress:92.3% Speed(reviews/sec):5416. #Correct:18965 #Trained:22164 Training Accuracy:85.5%\r", + "Progress:92.3% Speed(reviews/sec):5416. #Correct:18966 #Trained:22165 Training Accuracy:85.5%\r", + "Progress:92.3% Speed(reviews/sec):5416. #Correct:18967 #Trained:22166 Training Accuracy:85.5%\r", + "Progress:92.3% Speed(reviews/sec):5416. #Correct:18968 #Trained:22167 Training Accuracy:85.5%\r", + "Progress:92.3% Speed(reviews/sec):5416. #Correct:18969 #Trained:22168 Training Accuracy:85.5%\r", + "Progress:92.3% Speed(reviews/sec):5416. #Correct:18969 #Trained:22169 Training Accuracy:85.5%\r", + "Progress:92.3% Speed(reviews/sec):5416. #Correct:18970 #Trained:22170 Training Accuracy:85.5%\r", + "Progress:92.3% Speed(reviews/sec):5416. #Correct:18970 #Trained:22171 Training Accuracy:85.5%\r", + "Progress:92.3% Speed(reviews/sec):5416. #Correct:18971 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Training Accuracy:85.5%\r", + "Progress:96.3% Speed(reviews/sec):5412. #Correct:19794 #Trained:23135 Training Accuracy:85.5%\r", + "Progress:96.3% Speed(reviews/sec):5412. #Correct:19795 #Trained:23136 Training Accuracy:85.5%\r", + "Progress:96.4% Speed(reviews/sec):5412. #Correct:19796 #Trained:23137 Training Accuracy:85.5%\r", + "Progress:96.4% Speed(reviews/sec):5412. #Correct:19796 #Trained:23138 Training Accuracy:85.5%\r", + "Progress:96.4% Speed(reviews/sec):5412. #Correct:19797 #Trained:23139 Training Accuracy:85.5%\r", + "Progress:96.4% Speed(reviews/sec):5412. #Correct:19798 #Trained:23140 Training Accuracy:85.5%\r", + "Progress:96.4% Speed(reviews/sec):5412. #Correct:19799 #Trained:23141 Training Accuracy:85.5%\r", + "Progress:96.4% Speed(reviews/sec):5412. #Correct:19800 #Trained:23142 Training Accuracy:85.5%\r", + "Progress:96.4% Speed(reviews/sec):5413. #Correct:19801 #Trained:23143 Training Accuracy:85.5%\r", + "Progress:96.4% Speed(reviews/sec):5413. #Correct:19802 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Training Accuracy:85.5%\r", + "Progress:96.6% Speed(reviews/sec):5411. #Correct:19849 #Trained:23194 Training Accuracy:85.5%\r", + "Progress:96.6% Speed(reviews/sec):5411. #Correct:19850 #Trained:23195 Training Accuracy:85.5%\r", + "Progress:96.6% Speed(reviews/sec):5411. #Correct:19851 #Trained:23196 Training Accuracy:85.5%\r", + "Progress:96.6% Speed(reviews/sec):5411. #Correct:19851 #Trained:23197 Training Accuracy:85.5%\r", + "Progress:96.6% Speed(reviews/sec):5410. #Correct:19851 #Trained:23198 Training Accuracy:85.5%\r", + "Progress:96.6% Speed(reviews/sec):5410. #Correct:19851 #Trained:23199 Training Accuracy:85.5%\r", + "Progress:96.6% Speed(reviews/sec):5410. #Correct:19852 #Trained:23200 Training Accuracy:85.5%\r", + "Progress:96.6% Speed(reviews/sec):5410. #Correct:19853 #Trained:23201 Training Accuracy:85.5%\r", + "Progress:96.6% Speed(reviews/sec):5410. #Correct:19854 #Trained:23202 Training Accuracy:85.5%\r", + "Progress:96.6% Speed(reviews/sec):5410. #Correct:19855 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Training Accuracy:85.5%\r", + "Progress:96.8% Speed(reviews/sec):5412. #Correct:19897 #Trained:23253 Training Accuracy:85.5%\r", + "Progress:96.8% Speed(reviews/sec):5412. #Correct:19898 #Trained:23254 Training Accuracy:85.5%\r", + "Progress:96.8% Speed(reviews/sec):5412. #Correct:19898 #Trained:23255 Training Accuracy:85.5%\r", + "Progress:96.8% Speed(reviews/sec):5412. #Correct:19899 #Trained:23256 Training Accuracy:85.5%\r", + "Progress:96.9% Speed(reviews/sec):5412. #Correct:19899 #Trained:23257 Training Accuracy:85.5%\r", + "Progress:96.9% Speed(reviews/sec):5412. #Correct:19900 #Trained:23258 Training Accuracy:85.5%\r", + "Progress:96.9% Speed(reviews/sec):5412. #Correct:19901 #Trained:23259 Training Accuracy:85.5%\r", + "Progress:96.9% Speed(reviews/sec):5412. #Correct:19902 #Trained:23260 Training Accuracy:85.5%\r", + "Progress:96.9% Speed(reviews/sec):5412. #Correct:19903 #Trained:23261 Training Accuracy:85.5%\r", + "Progress:96.9% Speed(reviews/sec):5411. #Correct:19904 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Training Accuracy:85.5%\r", + "Progress:97.1% Speed(reviews/sec):5409. #Correct:19942 #Trained:23312 Training Accuracy:85.5%\r", + "Progress:97.1% Speed(reviews/sec):5409. #Correct:19943 #Trained:23313 Training Accuracy:85.5%\r", + "Progress:97.1% Speed(reviews/sec):5409. #Correct:19944 #Trained:23314 Training Accuracy:85.5%\r", + "Progress:97.1% Speed(reviews/sec):5409. #Correct:19944 #Trained:23315 Training Accuracy:85.5%\r", + "Progress:97.1% Speed(reviews/sec):5409. #Correct:19945 #Trained:23316 Training Accuracy:85.5%\r", + "Progress:97.1% Speed(reviews/sec):5410. #Correct:19945 #Trained:23317 Training Accuracy:85.5%\r", + "Progress:97.1% Speed(reviews/sec):5410. #Correct:19946 #Trained:23318 Training Accuracy:85.5%\r", + "Progress:97.1% Speed(reviews/sec):5410. #Correct:19947 #Trained:23319 Training Accuracy:85.5%\r", + "Progress:97.1% Speed(reviews/sec):5410. #Correct:19948 #Trained:23320 Training Accuracy:85.5%\r", + "Progress:97.1% Speed(reviews/sec):5410. #Correct:19949 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Training Accuracy:85.5%\r", + "Progress:98.3% Speed(reviews/sec):5401. #Correct:20203 #Trained:23607 Training Accuracy:85.5%\r", + "Progress:98.3% Speed(reviews/sec):5401. #Correct:20204 #Trained:23608 Training Accuracy:85.5%\r", + "Progress:98.3% Speed(reviews/sec):5400. #Correct:20205 #Trained:23609 Training Accuracy:85.5%\r", + "Progress:98.3% Speed(reviews/sec):5400. #Correct:20205 #Trained:23610 Training Accuracy:85.5%\r", + "Progress:98.3% Speed(reviews/sec):5400. #Correct:20206 #Trained:23611 Training Accuracy:85.5%\r", + "Progress:98.3% Speed(reviews/sec):5400. #Correct:20207 #Trained:23612 Training Accuracy:85.5%\r", + "Progress:98.3% Speed(reviews/sec):5400. #Correct:20208 #Trained:23613 Training Accuracy:85.5%\r", + "Progress:98.3% Speed(reviews/sec):5400. #Correct:20209 #Trained:23614 Training Accuracy:85.5%\r", + "Progress:98.3% Speed(reviews/sec):5400. #Correct:20210 #Trained:23615 Training Accuracy:85.5%\r", + "Progress:98.3% Speed(reviews/sec):5400. #Correct:20211 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Training Accuracy:85.6%\r", + "Progress:99.5% Speed(reviews/sec):5396. #Correct:20464 #Trained:23902 Training Accuracy:85.6%\r", + "Progress:99.5% Speed(reviews/sec):5396. #Correct:20465 #Trained:23903 Training Accuracy:85.6%\r", + "Progress:99.5% Speed(reviews/sec):5396. #Correct:20466 #Trained:23904 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20467 #Trained:23905 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20468 #Trained:23906 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20469 #Trained:23907 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20470 #Trained:23908 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20471 #Trained:23909 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20472 #Trained:23910 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20473 #Trained:23911 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20474 #Trained:23912 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20475 #Trained:23913 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20476 #Trained:23914 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20477 #Trained:23915 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20478 #Trained:23916 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20479 #Trained:23917 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20480 #Trained:23918 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5397. #Correct:20481 #Trained:23919 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20482 #Trained:23920 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20482 #Trained:23921 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20483 #Trained:23922 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20484 #Trained:23923 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20484 #Trained:23924 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20485 #Trained:23925 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5396. #Correct:20486 #Trained:23926 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5397. #Correct:20487 #Trained:23927 Training Accuracy:85.6%\r", + "Progress:99.6% Speed(reviews/sec):5397. #Correct:20488 #Trained:23928 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5396. #Correct:20489 #Trained:23929 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5396. #Correct:20490 #Trained:23930 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5396. #Correct:20491 #Trained:23931 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5396. #Correct:20492 #Trained:23932 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5396. #Correct:20493 #Trained:23933 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5396. #Correct:20494 #Trained:23934 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5396. #Correct:20495 #Trained:23935 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5396. #Correct:20496 #Trained:23936 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5396. #Correct:20497 #Trained:23937 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5395. #Correct:20498 #Trained:23938 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5395. #Correct:20499 #Trained:23939 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5395. #Correct:20499 #Trained:23940 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5395. #Correct:20500 #Trained:23941 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5395. #Correct:20501 #Trained:23942 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5395. #Correct:20502 #Trained:23943 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5395. #Correct:20503 #Trained:23944 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5394. #Correct:20504 #Trained:23945 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5394. #Correct:20505 #Trained:23946 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5394. #Correct:20506 #Trained:23947 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5394. #Correct:20507 #Trained:23948 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5394. #Correct:20508 #Trained:23949 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5393. #Correct:20509 #Trained:23950 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5393. #Correct:20510 #Trained:23951 Training Accuracy:85.6%\r", + "Progress:99.7% Speed(reviews/sec):5393. #Correct:20511 #Trained:23952 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20512 #Trained:23953 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20513 #Trained:23954 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20514 #Trained:23955 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20515 #Trained:23956 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20516 #Trained:23957 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20517 #Trained:23958 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20518 #Trained:23959 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5394. #Correct:20518 #Trained:23960 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20519 #Trained:23961 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20520 #Trained:23962 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20521 #Trained:23963 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20522 #Trained:23964 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20523 #Trained:23965 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20524 #Trained:23966 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20525 #Trained:23967 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5394. #Correct:20526 #Trained:23968 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20527 #Trained:23969 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5393. #Correct:20528 #Trained:23970 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5392. #Correct:20528 #Trained:23971 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5391. #Correct:20529 #Trained:23972 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5391. #Correct:20530 #Trained:23973 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5391. #Correct:20531 #Trained:23974 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5391. #Correct:20532 #Trained:23975 Training Accuracy:85.6%\r", + "Progress:99.8% Speed(reviews/sec):5391. #Correct:20533 #Trained:23976 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20534 #Trained:23977 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20535 #Trained:23978 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20536 #Trained:23979 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20537 #Trained:23980 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20538 #Trained:23981 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20539 #Trained:23982 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20540 #Trained:23983 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20541 #Trained:23984 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20542 #Trained:23985 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20543 #Trained:23986 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20543 #Trained:23987 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20544 #Trained:23988 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20544 #Trained:23989 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5393. #Correct:20545 #Trained:23990 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5393. #Correct:20545 #Trained:23991 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5393. #Correct:20546 #Trained:23992 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5393. #Correct:20547 #Trained:23993 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20548 #Trained:23994 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20549 #Trained:23995 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20550 #Trained:23996 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20551 #Trained:23997 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20551 #Trained:23998 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5392. #Correct:20551 #Trained:23999 Training Accuracy:85.6%\r", + "Progress:99.9% Speed(reviews/sec):5393. #Correct:20552 #Trained:24000 Training Accuracy:85.6%" + ] + } + ], + "source": [ + "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.8,learning_rate=0.01)\n", + "mlp.train(reviews[:-1000],labels[:-1000])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And run the following cell to test it's performance." + ] + }, + { + "cell_type": "code", + "execution_count": 307, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Tested:1 Testing Accuracy:0.0%\r", + "Progress:0.1% Speed(reviews/sec):2119. #Correct:1 #Tested:2 Testing Accuracy:50.0%\r", + "Progress:0.2% Speed(reviews/sec):3644. #Correct:2 #Tested:3 Testing Accuracy:66.6%\r", + "Progress:0.3% Speed(reviews/sec):4650. #Correct:3 #Tested:4 Testing Accuracy:75.0%\r", + "Progress:0.4% Speed(reviews/sec):5704. #Correct:3 #Tested:5 Testing Accuracy:60.0%\r", + "Progress:0.5% Speed(reviews/sec):5791. #Correct:4 #Tested:6 Testing Accuracy:66.6%\r", + "Progress:0.6% Speed(reviews/sec):5332. #Correct:5 #Tested:7 Testing Accuracy:71.4%\r", + "Progress:0.7% Speed(reviews/sec):5173. #Correct:6 #Tested:8 Testing Accuracy:75.0%\r", + "Progress:0.8% Speed(reviews/sec):5017. #Correct:7 #Tested:9 Testing Accuracy:77.7%\r", + "Progress:0.9% Speed(reviews/sec):4549. #Correct:8 #Tested:10 Testing Accuracy:80.0%\r", + "Progress:1.0% Speed(reviews/sec):4768. #Correct:9 #Tested:11 Testing Accuracy:81.8%\r", + "Progress:1.1% Speed(reviews/sec):5031. #Correct:10 #Tested:12 Testing Accuracy:83.3%\r", + "Progress:1.2% Speed(reviews/sec):5276. #Correct:10 #Tested:13 Testing Accuracy:76.9%\r", + "Progress:1.3% Speed(reviews/sec):5465. #Correct:11 #Tested:14 Testing Accuracy:78.5%\r", + "Progress:1.4% Speed(reviews/sec):5622. #Correct:11 #Tested:15 Testing Accuracy:73.3%\r", + "Progress:1.5% Speed(reviews/sec):5564. #Correct:12 #Tested:16 Testing Accuracy:75.0%\r", + "Progress:1.6% Speed(reviews/sec):4965. #Correct:13 #Tested:17 Testing Accuracy:76.4%\r", + "Progress:1.7% Speed(reviews/sec):4851. #Correct:14 #Tested:18 Testing Accuracy:77.7%\r", + "Progress:1.8% Speed(reviews/sec):4971. #Correct:15 #Tested:19 Testing Accuracy:78.9%\r", + "Progress:1.9% Speed(reviews/sec):3536. #Correct:16 #Tested:20 Testing Accuracy:80.0%\r", + "Progress:2.0% Speed(reviews/sec):3464. #Correct:17 #Tested:21 Testing Accuracy:80.9%\r", + "Progress:2.1% Speed(reviews/sec):3528. #Correct:18 #Tested:22 Testing Accuracy:81.8%\r", + "Progress:2.2% Speed(reviews/sec):3574. #Correct:19 #Tested:23 Testing Accuracy:82.6%\r", + "Progress:2.3% Speed(reviews/sec):3585. #Correct:20 #Tested:24 Testing Accuracy:83.3%\r", + "Progress:2.4% Speed(reviews/sec):3504. #Correct:21 #Tested:25 Testing Accuracy:84.0%\r", + "Progress:2.5% Speed(reviews/sec):3567. #Correct:22 #Tested:26 Testing Accuracy:84.6%\r", + "Progress:2.6% Speed(reviews/sec):3640. #Correct:22 #Tested:27 Testing Accuracy:81.4%\r", + "Progress:2.7% Speed(reviews/sec):3455. #Correct:23 #Tested:28 Testing Accuracy:82.1%\r", + "Progress:2.8% Speed(reviews/sec):3478. #Correct:24 #Tested:29 Testing Accuracy:82.7%\r", + "Progress:2.9% Speed(reviews/sec):3361. #Correct:25 #Tested:30 Testing Accuracy:83.3%\r", + "Progress:3.0% Speed(reviews/sec):3349. #Correct:25 #Tested:31 Testing Accuracy:80.6%\r", + "Progress:3.1% Speed(reviews/sec):3407. #Correct:26 #Tested:32 Testing Accuracy:81.2%\r", + "Progress:3.2% Speed(reviews/sec):3428. #Correct:27 #Tested:33 Testing Accuracy:81.8%\r", + "Progress:3.3% Speed(reviews/sec):3301. #Correct:28 #Tested:34 Testing Accuracy:82.3%\r", + "Progress:3.4% Speed(reviews/sec):3288. #Correct:29 #Tested:35 Testing Accuracy:82.8%\r", + "Progress:3.5% Speed(reviews/sec):3309. #Correct:30 #Tested:36 Testing Accuracy:83.3%\r", + "Progress:3.6% Speed(reviews/sec):3216. #Correct:31 #Tested:37 Testing Accuracy:83.7%\r", + "Progress:3.7% Speed(reviews/sec):3264. #Correct:32 #Tested:38 Testing Accuracy:84.2%\r", + "Progress:3.8% Speed(reviews/sec):3254. #Correct:33 #Tested:39 Testing Accuracy:84.6%\r", + "Progress:3.9% Speed(reviews/sec):3159. #Correct:33 #Tested:40 Testing Accuracy:82.5%\r", + "Progress:4.0% Speed(reviews/sec):3176. #Correct:34 #Tested:41 Testing Accuracy:82.9%\r", + "Progress:4.1% Speed(reviews/sec):3206. #Correct:35 #Tested:42 Testing Accuracy:83.3%\r", + "Progress:4.2% Speed(reviews/sec):3144. #Correct:36 #Tested:43 Testing Accuracy:83.7%\r", + "Progress:4.3% Speed(reviews/sec):3016. #Correct:37 #Tested:44 Testing Accuracy:84.0%\r", + "Progress:4.4% Speed(reviews/sec):2980. #Correct:38 #Tested:45 Testing Accuracy:84.4%\r", + "Progress:4.5% Speed(reviews/sec):2874. #Correct:39 #Tested:46 Testing Accuracy:84.7%\r", + "Progress:4.6% Speed(reviews/sec):2854. #Correct:40 #Tested:47 Testing Accuracy:85.1%\r", + "Progress:4.7% Speed(reviews/sec):2851. #Correct:41 #Tested:48 Testing Accuracy:85.4%\r", + "Progress:4.8% Speed(reviews/sec):2842. #Correct:42 #Tested:49 Testing Accuracy:85.7%\r", + "Progress:4.9% Speed(reviews/sec):2867. #Correct:43 #Tested:50 Testing Accuracy:86.0%\r", + "Progress:5.0% Speed(reviews/sec):2898. #Correct:44 #Tested:51 Testing Accuracy:86.2%\r", + "Progress:5.1% Speed(reviews/sec):2933. #Correct:45 #Tested:52 Testing Accuracy:86.5%\r", + "Progress:5.2% Speed(reviews/sec):2907. #Correct:46 #Tested:53 Testing Accuracy:86.7%\r", + "Progress:5.3% Speed(reviews/sec):2924. #Correct:47 #Tested:54 Testing Accuracy:87.0%\r", + "Progress:5.4% Speed(reviews/sec):2951. #Correct:48 #Tested:55 Testing Accuracy:87.2%\r", + "Progress:5.5% Speed(reviews/sec):2984. #Correct:49 #Tested:56 Testing Accuracy:87.5%\r", + "Progress:5.6% Speed(reviews/sec):2898. #Correct:50 #Tested:57 Testing Accuracy:87.7%\r", + "Progress:5.7% Speed(reviews/sec):2927. #Correct:51 #Tested:58 Testing Accuracy:87.9%\r", + "Progress:5.8% Speed(reviews/sec):2958. #Correct:52 #Tested:59 Testing Accuracy:88.1%\r", + "Progress:5.9% Speed(reviews/sec):2990. #Correct:53 #Tested:60 Testing Accuracy:88.3%\r", + "Progress:6.0% Speed(reviews/sec):2987. #Correct:53 #Tested:61 Testing Accuracy:86.8%\r", + "Progress:6.1% Speed(reviews/sec):2991. #Correct:54 #Tested:62 Testing Accuracy:87.0%\r", + "Progress:6.2% Speed(reviews/sec):3024. #Correct:55 #Tested:63 Testing Accuracy:87.3%\r", + "Progress:6.3% Speed(reviews/sec):3052. #Correct:55 #Tested:64 Testing Accuracy:85.9%\r", + "Progress:6.4% Speed(reviews/sec):3044. #Correct:55 #Tested:65 Testing Accuracy:84.6%\r", + "Progress:6.5% Speed(reviews/sec):3069. #Correct:55 #Tested:66 Testing Accuracy:83.3%\r", + "Progress:6.6% Speed(reviews/sec):3096. #Correct:56 #Tested:67 Testing Accuracy:83.5%\r", + "Progress:6.7% Speed(reviews/sec):3116. #Correct:57 #Tested:68 Testing Accuracy:83.8%\r", + "Progress:6.8% Speed(reviews/sec):3133. #Correct:58 #Tested:69 Testing Accuracy:84.0%\r", + "Progress:6.9% Speed(reviews/sec):3141. #Correct:59 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#Correct:69 #Tested:81 Testing Accuracy:85.1%\r", + "Progress:8.1% Speed(reviews/sec):3338. #Correct:69 #Tested:82 Testing Accuracy:84.1%\r", + "Progress:8.2% Speed(reviews/sec):3346. #Correct:70 #Tested:83 Testing Accuracy:84.3%\r", + "Progress:8.3% Speed(reviews/sec):3360. #Correct:71 #Tested:84 Testing Accuracy:84.5%\r", + "Progress:8.4% Speed(reviews/sec):3382. #Correct:72 #Tested:85 Testing Accuracy:84.7%\r", + "Progress:8.5% Speed(reviews/sec):3400. #Correct:73 #Tested:86 Testing Accuracy:84.8%\r", + "Progress:8.6% Speed(reviews/sec):3401. #Correct:74 #Tested:87 Testing Accuracy:85.0%\r", + "Progress:8.7% Speed(reviews/sec):3421. #Correct:74 #Tested:88 Testing Accuracy:84.0%\r", + "Progress:8.8% Speed(reviews/sec):3442. #Correct:75 #Tested:89 Testing Accuracy:84.2%\r", + "Progress:8.9% Speed(reviews/sec):3409. #Correct:76 #Tested:90 Testing Accuracy:84.4%\r", + "Progress:9.0% Speed(reviews/sec):3275. #Correct:77 #Tested:91 Testing Accuracy:84.6%\r", + "Progress:9.1% 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Testing Accuracy:87.4%\r", + "Progress:27.0% Speed(reviews/sec):3830. #Correct:236 #Tested:271 Testing Accuracy:87.0%\r", + "Progress:27.1% Speed(reviews/sec):3838. #Correct:237 #Tested:272 Testing Accuracy:87.1%\r", + "Progress:27.2% Speed(reviews/sec):3842. #Correct:238 #Tested:273 Testing Accuracy:87.1%\r", + "Progress:27.3% Speed(reviews/sec):3853. #Correct:239 #Tested:274 Testing Accuracy:87.2%\r", + "Progress:27.4% Speed(reviews/sec):3859. #Correct:240 #Tested:275 Testing Accuracy:87.2%\r", + "Progress:27.5% Speed(reviews/sec):3852. #Correct:241 #Tested:276 Testing Accuracy:87.3%\r", + "Progress:27.6% Speed(reviews/sec):3852. #Correct:242 #Tested:277 Testing Accuracy:87.3%\r", + "Progress:27.7% Speed(reviews/sec):3861. #Correct:243 #Tested:278 Testing Accuracy:87.4%\r", + "Progress:27.8% Speed(reviews/sec):3871. #Correct:244 #Tested:279 Testing Accuracy:87.4%\r", + "Progress:27.9% Speed(reviews/sec):3881. #Correct:245 #Tested:280 Testing Accuracy:87.5%\r", + "Progress:28.0% 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Testing Accuracy:86.2%\r", + "Progress:45.9% Speed(reviews/sec):4078. #Correct:397 #Tested:460 Testing Accuracy:86.3%\r", + "Progress:46.0% Speed(reviews/sec):4071. #Correct:398 #Tested:461 Testing Accuracy:86.3%\r", + "Progress:46.1% Speed(reviews/sec):4073. #Correct:398 #Tested:462 Testing Accuracy:86.1%\r", + "Progress:46.2% Speed(reviews/sec):4070. #Correct:399 #Tested:463 Testing Accuracy:86.1%\r", + "Progress:46.3% Speed(reviews/sec):4076. #Correct:400 #Tested:464 Testing Accuracy:86.2%\r", + "Progress:46.4% Speed(reviews/sec):4082. #Correct:401 #Tested:465 Testing Accuracy:86.2%\r", + "Progress:46.5% Speed(reviews/sec):4070. #Correct:402 #Tested:466 Testing Accuracy:86.2%\r", + "Progress:46.6% Speed(reviews/sec):4074. #Correct:403 #Tested:467 Testing Accuracy:86.2%\r", + "Progress:46.7% Speed(reviews/sec):4074. #Correct:404 #Tested:468 Testing Accuracy:86.3%\r", + "Progress:46.8% Speed(reviews/sec):4076. #Correct:405 #Tested:469 Testing Accuracy:86.3%\r", + "Progress:46.9% 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Testing Accuracy:85.0%\r", + "Progress:62.7% Speed(reviews/sec):4477. #Correct:533 #Tested:628 Testing Accuracy:84.8%\r", + "Progress:62.8% Speed(reviews/sec):4479. #Correct:533 #Tested:629 Testing Accuracy:84.7%\r", + "Progress:62.9% Speed(reviews/sec):4473. #Correct:534 #Tested:630 Testing Accuracy:84.7%\r", + "Progress:63.0% Speed(reviews/sec):4470. #Correct:534 #Tested:631 Testing Accuracy:84.6%\r", + "Progress:63.1% Speed(reviews/sec):4467. #Correct:535 #Tested:632 Testing Accuracy:84.6%\r", + "Progress:63.2% Speed(reviews/sec):4471. #Correct:535 #Tested:633 Testing Accuracy:84.5%\r", + "Progress:63.3% Speed(reviews/sec):4473. #Correct:536 #Tested:634 Testing Accuracy:84.5%\r", + "Progress:63.4% Speed(reviews/sec):4473. #Correct:536 #Tested:635 Testing Accuracy:84.4%\r", + "Progress:63.5% Speed(reviews/sec):4472. #Correct:537 #Tested:636 Testing Accuracy:84.4%\r", + "Progress:63.6% Speed(reviews/sec):4475. #Correct:537 #Tested:637 Testing Accuracy:84.3%\r", + "Progress:63.7% 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Testing Accuracy:81.9%\r", + "Progress:96.3% Speed(reviews/sec):5243. #Correct:790 #Tested:964 Testing Accuracy:81.9%\r", + "Progress:96.4% Speed(reviews/sec):5247. #Correct:791 #Tested:965 Testing Accuracy:81.9%\r", + "Progress:96.5% Speed(reviews/sec):5250. #Correct:792 #Tested:966 Testing Accuracy:81.9%\r", + "Progress:96.6% Speed(reviews/sec):5254. #Correct:793 #Tested:967 Testing Accuracy:82.0%\r", + "Progress:96.7% Speed(reviews/sec):5258. #Correct:794 #Tested:968 Testing Accuracy:82.0%\r", + "Progress:96.8% Speed(reviews/sec):5261. #Correct:795 #Tested:969 Testing Accuracy:82.0%\r", + "Progress:96.9% Speed(reviews/sec):5265. #Correct:796 #Tested:970 Testing Accuracy:82.0%\r", + "Progress:97.0% Speed(reviews/sec):5250. #Correct:797 #Tested:971 Testing Accuracy:82.0%\r", + "Progress:97.1% Speed(reviews/sec):5253. #Correct:798 #Tested:972 Testing Accuracy:82.0%\r", + "Progress:97.2% Speed(reviews/sec):5257. #Correct:798 #Tested:973 Testing Accuracy:82.0%\r", + "Progress:97.3% 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Testing Accuracy:82.2%\r", + "Progress:98.4% Speed(reviews/sec):5268. #Correct:809 #Tested:985 Testing Accuracy:82.1%\r", + "Progress:98.5% Speed(reviews/sec):5271. #Correct:810 #Tested:986 Testing Accuracy:82.1%\r", + "Progress:98.6% Speed(reviews/sec):5275. #Correct:811 #Tested:987 Testing Accuracy:82.1%\r", + "Progress:98.7% Speed(reviews/sec):5277. #Correct:812 #Tested:988 Testing Accuracy:82.1%\r", + "Progress:98.8% Speed(reviews/sec):5280. #Correct:813 #Tested:989 Testing Accuracy:82.2%\r", + "Progress:98.9% Speed(reviews/sec):5282. #Correct:814 #Tested:990 Testing Accuracy:82.2%\r", + "Progress:99.0% Speed(reviews/sec):5286. #Correct:815 #Tested:991 Testing Accuracy:82.2%\r", + "Progress:99.1% Speed(reviews/sec):5288. #Correct:816 #Tested:992 Testing Accuracy:82.2%\r", + "Progress:99.2% Speed(reviews/sec):5291. #Correct:817 #Tested:993 Testing Accuracy:82.2%\r", + "Progress:99.3% Speed(reviews/sec):5294. #Correct:818 #Tested:994 Testing Accuracy:82.2%\r", + "Progress:99.4% Speed(reviews/sec):5295. #Correct:818 #Tested:995 Testing Accuracy:82.2%\r", + "Progress:99.5% Speed(reviews/sec):5297. #Correct:819 #Tested:996 Testing Accuracy:82.2%\r", + "Progress:99.6% Speed(reviews/sec):5299. #Correct:820 #Tested:997 Testing Accuracy:82.2%\r", + "Progress:99.7% Speed(reviews/sec):5302. #Correct:821 #Tested:998 Testing Accuracy:82.2%\r", + "Progress:99.8% Speed(reviews/sec):5304. #Correct:821 #Tested:999 Testing Accuracy:82.1%\r", + "Progress:99.9% Speed(reviews/sec):5307. #Correct:822 #Tested:1000 Testing Accuracy:82.2%" + ] + } + ], + "source": [ + "mlp.test(reviews[-1000:],labels[-1000:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# End of Project 6. \n", + "## Watch the next video to see Andrew's solution, then continue on to the next lesson." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Analysis: What's Going on in the Weights?" + ] + }, + { + "cell_type": "code", + "execution_count": 308, + "metadata": {}, + "outputs": [], + "source": [ + "mlp_full = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=0,polarity_cutoff=0,learning_rate=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.0% Speed(reviews/sec):0.0 #Correct:1 #Trained:1 Training Accuracy:100.%\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "Progress:0.00% Speed(reviews/sec):1086. #Correct:2 #Trained:2 Training Accuracy:100.%\r", + "Progress:0.00% Speed(reviews/sec):1090. #Correct:3 #Trained:3 Training Accuracy:100.%\r", + "Progress:0.01% Speed(reviews/sec):886.1 #Correct:4 #Trained:4 Training Accuracy:100.%\r", + "Progress:0.01% Speed(reviews/sec):1039. #Correct:5 #Trained:5 Training Accuracy:100.%\r", + "Progress:0.02% Speed(reviews/sec):1176. #Correct:6 #Trained:6 Training Accuracy:100.%\r", + 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Speed(reviews/sec):1614. #Correct:206 #Trained:224 Training Accuracy:91.9%\r", + "Progress:0.93% Speed(reviews/sec):1615. #Correct:207 #Trained:225 Training Accuracy:92.0%\r", + "Progress:0.93% Speed(reviews/sec):1619. #Correct:208 #Trained:226 Training Accuracy:92.0%\r", + "Progress:0.94% Speed(reviews/sec):1623. #Correct:209 #Trained:227 Training Accuracy:92.0%\r", + "Progress:0.94% Speed(reviews/sec):1625. #Correct:210 #Trained:228 Training Accuracy:92.1%\r", + "Progress:0.95% Speed(reviews/sec):1627. #Correct:211 #Trained:229 Training Accuracy:92.1%\r", + "Progress:0.95% Speed(reviews/sec):1627. #Correct:212 #Trained:230 Training Accuracy:92.1%\r", + "Progress:0.95% Speed(reviews/sec):1630. #Correct:213 #Trained:231 Training Accuracy:92.2%\r", + "Progress:0.96% Speed(reviews/sec):1630. #Correct:214 #Trained:232 Training Accuracy:92.2%\r", + "Progress:0.96% Speed(reviews/sec):1630. #Correct:215 #Trained:233 Training Accuracy:92.2%\r", + "Progress:0.97% Speed(reviews/sec):1630. #Correct:216 #Trained:234 Training Accuracy:92.3%\r", + "Progress:0.97% Speed(reviews/sec):1628. #Correct:217 #Trained:235 Training Accuracy:92.3%\r", + "Progress:0.97% Speed(reviews/sec):1624. #Correct:218 #Trained:236 Training Accuracy:92.3%\r", + "Progress:0.98% Speed(reviews/sec):1624. #Correct:219 #Trained:237 Training Accuracy:92.4%\r", + "Progress:0.98% Speed(reviews/sec):1624. #Correct:220 #Trained:238 Training Accuracy:92.4%\r", + "Progress:0.99% Speed(reviews/sec):1622. #Correct:221 #Trained:239 Training Accuracy:92.4%\r", + "Progress:0.99% Speed(reviews/sec):1623. #Correct:222 #Trained:240 Training Accuracy:92.5%\r", + "Progress:1.0% Speed(reviews/sec):1625. #Correct:223 #Trained:241 Training Accuracy:92.5%\r", + "Progress:1.00% Speed(reviews/sec):1615. #Correct:224 #Trained:242 Training Accuracy:92.5%\r", + "Progress:1.00% Speed(reviews/sec):1618. #Correct:225 #Trained:243 Training Accuracy:92.5%\r", + "Progress:1.01% Speed(reviews/sec):1620. #Correct:226 #Trained:244 Training Accuracy:92.6%\r", + "Progress:1.01% Speed(reviews/sec):1621. #Correct:227 #Trained:245 Training Accuracy:92.6%\r", + "Progress:1.02% Speed(reviews/sec):1622. #Correct:228 #Trained:246 Training Accuracy:92.6%\r", + "Progress:1.02% Speed(reviews/sec):1624. #Correct:229 #Trained:247 Training Accuracy:92.7%\r", + "Progress:1.02% Speed(reviews/sec):1625. #Correct:230 #Trained:248 Training Accuracy:92.7%\r", + "Progress:1.03% Speed(reviews/sec):1627. #Correct:231 #Trained:249 Training Accuracy:92.7%\r", + "Progress:1.03% Speed(reviews/sec):1629. #Correct:232 #Trained:250 Training Accuracy:92.8%\r", + "Progress:1.04% Speed(reviews/sec):1629. #Correct:233 #Trained:251 Training Accuracy:92.8%\r", + "Progress:1.04% Speed(reviews/sec):1630. #Correct:234 #Trained:252 Training Accuracy:92.8%\r", + "Progress:1.05% Speed(reviews/sec):1626. #Correct:235 #Trained:253 Training Accuracy:92.8%\r", + "Progress:1.05% Speed(reviews/sec):1628. #Correct:236 #Trained:254 Training Accuracy:92.9%\r", + "Progress:1.05% Speed(reviews/sec):1630. #Correct:237 #Trained:255 Training Accuracy:92.9%\r", + "Progress:1.06% Speed(reviews/sec):1633. #Correct:238 #Trained:256 Training Accuracy:92.9%\r", + "Progress:1.06% Speed(reviews/sec):1636. #Correct:239 #Trained:257 Training Accuracy:92.9%\r", + "Progress:1.07% Speed(reviews/sec):1638. #Correct:240 #Trained:258 Training Accuracy:93.0%\r", + "Progress:1.07% Speed(reviews/sec):1641. #Correct:241 #Trained:259 Training Accuracy:93.0%\r", + "Progress:1.07% Speed(reviews/sec):1644. #Correct:242 #Trained:260 Training Accuracy:93.0%\r", + "Progress:1.08% Speed(reviews/sec):1595. #Correct:243 #Trained:261 Training Accuracy:93.1%\r", + "Progress:1.08% Speed(reviews/sec):1596. #Correct:244 #Trained:262 Training Accuracy:93.1%\r", + "Progress:1.09% Speed(reviews/sec):1598. #Correct:245 #Trained:263 Training Accuracy:93.1%\r", + "Progress:1.09% Speed(reviews/sec):1602. #Correct:245 #Trained:264 Training Accuracy:92.8%\r", + "Progress:1.1% Speed(reviews/sec):1602. #Correct:246 #Trained:265 Training Accuracy:92.8%\r", + "Progress:1.10% Speed(reviews/sec):1604. #Correct:247 #Trained:266 Training Accuracy:92.8%\r", + "Progress:1.10% Speed(reviews/sec):1606. #Correct:248 #Trained:267 Training Accuracy:92.8%\r", + "Progress:1.11% Speed(reviews/sec):1607. #Correct:249 #Trained:268 Training Accuracy:92.9%\r", + "Progress:1.11% Speed(reviews/sec):1606. #Correct:250 #Trained:269 Training Accuracy:92.9%\r", + "Progress:1.12% Speed(reviews/sec):1608. #Correct:250 #Trained:270 Training Accuracy:92.5%\r", + "Progress:1.12% Speed(reviews/sec):1609. #Correct:251 #Trained:271 Training Accuracy:92.6%\r", + "Progress:1.12% Speed(reviews/sec):1611. #Correct:252 #Trained:272 Training Accuracy:92.6%\r", + "Progress:1.13% Speed(reviews/sec):1613. #Correct:252 #Trained:273 Training Accuracy:92.3%\r", + "Progress:1.13% Speed(reviews/sec):1614. #Correct:253 #Trained:274 Training Accuracy:92.3%\r", + "Progress:1.14% Speed(reviews/sec):1614. 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"Progress:99.9% Speed(reviews/sec):1429. #Correct:21821 #Trained:23996 Training Accuracy:90.9%\r", + "Progress:99.9% Speed(reviews/sec):1429. #Correct:21822 #Trained:23997 Training Accuracy:90.9%\r", + "Progress:99.9% Speed(reviews/sec):1429. #Correct:21822 #Trained:23998 Training Accuracy:90.9%\r", + "Progress:99.9% Speed(reviews/sec):1429. #Correct:21822 #Trained:23999 Training Accuracy:90.9%\r", + "Progress:99.9% Speed(reviews/sec):1429. #Correct:21823 #Trained:24000 Training Accuracy:90.9%" + ] + } + ], + "source": [ + "mlp_full.train(reviews[:-1000],labels[:-1000])" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 312, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(filename='sentiment_network_sparse.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 313, + "metadata": {}, + "outputs": [], + "source": [ + "def get_most_similar_words(focus = \"horrible\"):\n", + " most_similar = Counter()\n", + "\n", + " for word in mlp_full.word2index.keys():\n", + " most_similar[word] = np.dot(mlp_full.weights_0_1[mlp_full.word2index[word]],mlp_full.weights_0_1[mlp_full.word2index[focus]])\n", + " \n", + " return most_similar.most_common()" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('excellent', 0.1443759156657105),\n", + " ('perfect', 0.13445234797540037),\n", + " ('amazing', 0.10084499844844746),\n", + " ('today', 0.09508576537160299),\n", + " ('definitely', 0.09323327707346463),\n", + " ('wonderful', 0.09238904497121271),\n", + " ('refreshing', 0.08915783670427067),\n", + " ('liked', 0.0882411967079638),\n", + " ('enjoyable', 0.08681069084921053),\n", + " ('incredible', 0.08666092442919852),\n", + " ('rare', 0.0866158053961189),\n", + " ('wonderfully', 0.0865073583222816),\n", + " ('great', 0.08409337055253208),\n", + " ('superb', 0.08312811252373334),\n", + " ('fantastic', 0.08251213196725246),\n", + " ('best', 0.08224078484828994),\n", + " ('surprised', 0.08019238358673877),\n", + " ('gem', 0.08019017655378305),\n", + " ('favorite', 0.07937866587215331),\n", + " ('outstanding', 0.07667421136001533),\n", + " ('highly', 0.07630599782062364),\n", + " ('loved', 0.07562940157491867),\n", + " ('perfectly', 0.0756106641741694),\n", + " ('fascinating', 0.07466727191278122),\n", + " ('masterpiece', 0.0746021459872847),\n", + " ('noir', 0.07436414623014992),\n", + " ('fun', 0.07358532585452454),\n", + " ('enjoyed', 0.07159158422372096),\n", + " ('brilliant', 0.07075058161049724),\n", + " ('funniest', 0.06995120830916574),\n", + " ('solid', 0.06700489892387843),\n", + " ('atmosphere', 0.0657826657441384),\n", + " ('subtle', 0.06570106357647104),\n", + " ('hilarious', 0.06405442219260121),\n", + " ('job', 0.06395459921136776),\n", + " ('sweet', 0.06351565655960122),\n", + " ('bit', 0.06327330630779716),\n", + " ('beautiful', 0.062405698607707276),\n", + " ('cry', 0.061787556369963054),\n", + " ('expecting', 0.061024964727495676),\n", + " ('awesome', 0.05981887693571051),\n", + " ('powerful', 0.058273044728585605),\n", + " ('appreciate', 0.05791641654493708),\n", + " ('recommended', 0.057111304249357425),\n", + " ('remember', 0.05707519707916278),\n", + " ('appreciated', 0.056993631874853706),\n", + " ('believable', 0.056729380697415545),\n", + " ('surprisingly', 0.05636417750381337),\n", + " ('nice', 0.055825025837142425),\n", + " ('strong', 0.05572792666003323),\n", + " ('jack', 0.05544108609117576),\n", + " ('tight', 0.05530604860812347),\n", + " ('somewhat', 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" ('sexually', 0.015324603380911527),\n", + " ('rises', 0.015324501639980191),\n", + " ('jagger', 0.015322028440793561),\n", + " ('rodney', 0.015318815755030166),\n", + " ('whats', 0.015317890257021996),\n", + " ('dorothy', 0.015310286967387097),\n", + " ('early', 0.01527652230217317),\n", + " ('did', 0.015275447970403058),\n", + " ('protest', 0.015265020385325278),\n", + " ('plummer', 0.015254765396519905),\n", + " ('manner', 0.015219286004313642),\n", + " ('pub', 0.015213882051545462),\n", + " ('gags', 0.015206946711964587),\n", + " ('moon', 0.015200919140169245),\n", + " ('jamie', 0.01519920530780747),\n", + " ('dan', 0.015196726452634323),\n", + " ('physically', 0.015194006868357798),\n", + " ('too', 0.015173538507991774),\n", + " ...]" + ] + }, + "execution_count": 315, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_most_similar_words(\"terrible\")" + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.colors as colors\n", + "\n", + "words_to_visualize = list()\n", + "for word, ratio in pos_neg_ratios.most_common(500):\n", + " if(word in mlp_full.word2index.keys()):\n", + " words_to_visualize.append(word)\n", + " \n", + "for word, ratio in list(reversed(pos_neg_ratios.most_common()))[0:500]:\n", + " if(word in mlp_full.word2index.keys()):\n", + " words_to_visualize.append(word)" + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "metadata": {}, + "outputs": [], + "source": [ + "pos = 0\n", + "neg = 0\n", + "\n", + "colors_list = list()\n", + "vectors_list = list()\n", + "for word in words_to_visualize:\n", + " if word in pos_neg_ratios.keys():\n", + " vectors_list.append(mlp_full.weights_0_1[mlp_full.word2index[word]])\n", + " if(pos_neg_ratios[word] > 0):\n", + " pos+=1\n", + " colors_list.append(\"#00ff00\")\n", + " else:\n", + " neg+=1\n", + " colors_list.append(\"#000000\")" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.manifold import TSNE\n", + "tsne = TSNE(n_components=2, random_state=0)\n", + "words_top_ted_tsne = tsne.fit_transform(vectors_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 324, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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\",\"dtype\":\"float32\",\"shape\":[1000]}},\"selected\":{\"id\":\"1840\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1839\",\"type\":\"UnionRenderers\"}},\"id\":\"1777\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1840\",\"type\":\"Selection\"}],\"root_ids\":[\"1748\"]},\"title\":\"Bokeh Application\",\"version\":\"1.0.4\"}};\n", + " var render_items = [{\"docid\":\"34af8a4e-beda-4f0c-8458-5454a5ab27bf\",\"roots\":{\"1748\":\"05c4b49d-5fdb-4aa7-9769-b4c06312a537\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + "\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " clearInterval(timer);\n", + " }\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " clearInterval(timer);\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1748" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", + " toolbar_location=\"above\",\n", + " title=\"vector T-SNE for most polarized words\")\n", + "\n", + "source = ColumnDataSource(data=dict(x1=words_top_ted_tsne[:,0],\n", + " x2=words_top_ted_tsne[:,1],\n", + " names=words_to_visualize,\n", + " color=colors_list))\n", + "\n", + "p.scatter(x=\"x1\", y=\"x2\", size=8, source=source, fill_color=\"color\")\n", + "\n", + "word_labels = LabelSet(x=\"x1\", y=\"x2\", text=\"names\", y_offset=6,\n", + " text_font_size=\"8pt\", text_color=\"#555555\",\n", + " source=source, text_align='center')\n", + "p.add_layout(word_labels)\n", + "\n", + "show(p)\n", + "\n", + "# green indicates positive words, black indicates negative words" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}