AV-Intel-Scene-Classification-Challenge/scene-classification-cnn-data-aug-resnet101.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"scene-classification-cnn-data-aug-resnet101.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"metadata":{"id":"ZgiJmF-3pe3s","colab_type":"text"},"cell_type":"markdown","source":["{'buildings' -> 0, \n","\n","'forest' -> 1,\n","\n","'glacier' -> 2,\n","\n","'mountain' -> 3,\n","\n","'sea' -> 4,\n","\n","'street' -> 5 }"]},{"metadata":{"id":"n3RYV4j1pM_s","colab_type":"text"},"cell_type":"markdown","source":[""]},{"metadata":{"id":"Z0Clg3m3nxeM","colab_type":"code","colab":{}},"cell_type":"code","source":["labels = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']"],"execution_count":0,"outputs":[]},{"metadata":{"id":"XPLyRQHKmdUq","colab_type":"code","colab":{}},"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"f2b1b45d7222c7109884df0029385d81718f7209","id":"CNui6my2ipD2","colab_type":"code","colab":{}},"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import json\n","import os\n","from tqdm import tqdm, tqdm_notebook\n","\n","import tensorflow as tf\n","from tensorflow.keras.models import Sequential, Model\n","from tensorflow.keras.layers import *\n","from tensorflow.keras.optimizers import *\n","from tensorflow.keras.applications import *\n","from tensorflow.keras.callbacks import *\n","from tensorflow.keras.initializers import *\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator"],"execution_count":0,"outputs":[]},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","id":"mK84XzEvipEJ","colab_type":"code","outputId":"a169dc05-3f94-4ae8-d073-a8cf6777f600","executionInfo":{"status":"ok","timestamp":1553924024206,"user_tz":-330,"elapsed":1140,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":54}},"cell_type":"code","source":["home_dir = '/content/gdrive/My Drive/data-science-my-projects/AV-Intel-Scene-Classification-Challenge'\n","print(os.listdir(home_dir))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["['dataset', 'submissions', 'scene-classification-cnn-data-aug-vgg16.ipynb', 'models', 'scene-classification-cnn-vgg16-hybrid1365.ipynb', 'scene-classification-cnn-vgg16-places365.ipynb', 'scene-classification-cnn-data-aug-resnet50.ipynb', 'scene-classification-cnn-data-aug-inception-resnet.ipynb', 'scene-classification-cnn-data-aug-resnet101.ipynb']\n"],"name":"stdout"}]},{"metadata":{"_uuid":"4d3374968aa7f3881f1b83408f272367f0e63619","id":"cwFAsswOipEY","colab_type":"text"},"cell_type":"markdown","source":["## Read and set up data"]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","id":"e-JCRYi3ipEb","colab_type":"code","outputId":"5c409bc5-4460-4e2d-df85-9207ab8ce5f9","executionInfo":{"status":"ok","timestamp":1553924026916,"user_tz":-330,"elapsed":1192,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"cell_type":"code","source":["# Read data\n","dataset_dir = os.path.join(home_dir, \"dataset\")\n","train_dir = os.path.join(dataset_dir, \"train\")\n","\n","\n","train_df = pd.read_csv(dataset_dir + '/train.csv')\n","train_df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>image_name</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.jpg</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2.jpg</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7.jpg</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  image_name  label\n","0      0.jpg      0\n","1      1.jpg      4\n","2      2.jpg      5\n","3      4.jpg      0\n","4      7.jpg      4"]},"metadata":{"tags":[]},"execution_count":5}]},{"metadata":{"_uuid":"2ffaa5aba1e6197e0f2ced15187c06aa87cb4726","id":"BXPM-y_kipEo","colab_type":"code","outputId":"ae80b90d-2fdb-4169-ecba-99497a84b287","executionInfo":{"status":"ok","timestamp":1553924147899,"user_tz":-330,"elapsed":17294,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":367}},"cell_type":"code","source":["# Read and display an image\n","image = plt.imread(os.path.join(train_dir, os.listdir(train_dir)[25]))\n","print(\"Image shape =\", image.shape)\n","train_input_shape = image.shape\n","plt.imshow(image)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Image shape = (150, 150, 3)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAVEAAAFNCAYAAAC5YlyiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsfXe4XGXV/Zo+c3u/6Z10SGihhA6C\noIKgUi2IYPtELCifoD8boojYEBAJggUlNEGkBFFAEkIvkoSanntvyu1t+szvj7X2DHPuncxNLt8X\n/J6znyfPZGbOeds5c8961957bU82m83CNddcc8213TLvnh6Aa6655tp/srl/RF1zzTXXRmHuH1HX\nXHPNtVGY+0fUNddcc20U5v4Rdc0111wbhbl/RF1zzTXXRmH+d7rBK664Ai+//DI8Hg8uvfRS7LPP\nPu90F6655ppr7xp7R/+IPvPMM9i4cSOWLl2KtWvX4tJLL8XSpUvfyS5cc801195V9o5u51euXInj\njjsOADB9+nT09PSgv7//nezCNddcc+1dZe8oEm1vb8e8efNy7+vq6rBjxw5UVFQMe/w1y1tw9r5N\nuPWFbQCAUslTHo+n4NWX4edZz/DHZfS5tets3Zcdvl2zbDaLM/ZtxNIXd+x0XMXG6TTn/Eod5ymR\nS+bxAqcvbMTtLw0/vpKpaLZ+Rca1s3UBAE+JHjweDz68sAF3vdwxbPuZTKbk+QDg9XoL+s2flxky\nTjvWac45ptNpAMCH923CXS/tKHktnK8+//BrktVNl3Xce0PwSiY7ZOxvN4/Hgw8trMdddm09hXPO\nDLnpHWuZtf4KX7M6zGfj9Trm4Wg2U+Q4Pzz48IJ63PFSO793rE+uvWK/ac3Xvk8XuZdsXZzjQiat\n/3BePlh7yLV3zv5j8McX2nS+1tnLA7wZH4/TfeDzF14fjzr06Hp+4Yhxw88DgOedTPv81re+hSOP\nPDKHRs866yxcccUVmDp16rDHdwwkUV8eeKe6d80111z7X7d3FIk2NTWhvb0993779u1obGwsevyt\nL2zDFw+fgF8+sWW3+vM6kKSZ86lZzDwl0EA2m8XnDxuH65a3jqy9Imhmd82JRIc87zxZfO6w8fj1\niuHHV+rp6HWgoxzCHCUSffvx5x/SjJue2j7s96lUatj2nccVQ6LZbHrIOEeKKM0uOHQclqxsG9Jn\nMURlr17f8O0bAswMWRuv491Q5Om08w9pzo+tGBJ1INDc+G0b5kCieYhciOJz8yixe7NmfVngM4eO\nxQ1PthUeXwSTDb13h1/nTImfUG7aufHzQnhzzeR3OV84YgJ+8a9N/Dj3N4Hn+dM+nc/P44kY5+X3\n67XwHr3oqGlFx/SOcqKLFy/GsmXLAACrV69GU1NT0a28a6655tr/BXtHkeh+++2HefPm4cwzz4TH\n48G3v/3tnR5f7KlVDPk432dyxzkQlKM9bxFINloeoxSCGtLfLnKipdrNYvin/3D82vDm7MegPYZ9\nzfVTuOxDWy3BixVDvMWONxt6/M4R89vbGoIkR8idFuvbM2Sb4H37kIbcg87Fyn8//G7AEFMWQtuO\n832504rgIAOeOYSc27ap3SJrX+JX4fwtFbv3St7rRX87O+0+N4+0kKjXuEvHfHweLkDAsWUwpGt/\n+LLa1VSWVwIA4omEPmc/wUiwxID+B+JEL7744ne6Sddcc821d629439Ed8VGipicT7fccSUeW8UQ\nqIGI3UWiu4pAR/q987ii+LEEktvV/krxWMW+zxT53GmGGpzm5AXNRroT2R0Oerg183g8JSNDhowt\nOzziKnmeWe4043dHOhdbS99Oj3L2m82FYuS+KRxOkd3FSJFmsX6LWabIOMzL7uRG8zy4te8bdrzG\n9drHQ3elRKhpr/HpbCcaIwL1+ujojifJkRonujNz0z5dc80110Zh7wokuttPNyeSLdFfnh8a2Qm7\nipSLff5OFw8YEjtXrP8RtleMHxzpOJw2Uu43R8E6vi8WP+pExvkNSfGZjuQee/tno+Wth/DGRcwZ\ncTC0G8U/+gxxFnrThy7RyDhe41izHr/eD78ty0dgFH7t/HxXLXc9HIjR2a/tUvLxo4WvfnnRc/G2\nBm2d3n7HQtm3KTs9meT5Xn6QihOBhsNhAEB5qPSfSBeJuuaaa66Nwt4VSNT5fle9zcU4NONVPBnH\n08m8+SN8nu5u/Oe7vXxVqfGV8lQ70ZTz+901530w0nE6ec6dtV0qW2q0NrR/ByIaIQfqRKr5NfEW\n9pMd/lrk3nqc/Q/PKxe95s73nmzB68h3kcNH0jjjUvOItHAelqeUo9mdyFiOEPN7eH2F3LGd7wso\n3lb3QXUkBACwRLTBnj5+n7CO6ovOzUWirrnmmmujsD2KREcaD1nsPF8+gLHwe6cj1OtAuDleZldG\nO3R8Qzm60SGw/23LPc0d3GJ+XkVOzDmodx4PWurzkYZHFN95DEVXu7ub2dXjhuKPQs4yBwBza+RE\nfMJE2SI4xlOYlZXNOrk9cZpOL3fJWOKR6RXkxzn8vT3UPTGyiAozr/M3aeNEkd+SjrM/WMmc17xw\nfS033qNdku1Gbfdpy1gRLNNxUQDAM/9iktCE+hoAwPFHLuZx4kZ3Zi4Sdc0111wbhb2rkOhIEWje\nQ+jwUsssPnRIHm6Rp9t/ig31tI4MCRf73NBAMQ92MaTt5LdGa6WyiIrFww7Ha+5OXONIvPvDfLPz\n97m3pa5NMSRa2H/x4Tty4oeY88Rdw03FY5EL57Wr/H8eods9likYnTNzytm9V5lIdp7HEUeQ0r2R\nTBOxJoRcM/KPPPX4YwCALeteAwBc9JmPAQAmNRCh9rTw80l7TVe7kaJzcZGoa6655too7F3hnd/V\nTB4zy2/N5c36Cj2UXodWYTG+a1e5zLz3f/inZc57nS08fqjtmod2CDL0lQx05fG5t8642uFj6zzO\n44shwSFoYdd4xrxHVh7zHGWdxyPsx7Jt0ni7ZZVtkkjEAQChoD+fU54uRCiJlNR7FF+YeRt8yPo8\nOdUjJ4dmCC/juLblaj/juLcy5gW3e8/QsrcwosHiJOG4V5w6owH9RDOaa0rz8EESksadem3c6YJx\nGSLL3etaoGBcpwfZbkz6nBmNM635hQLsPxkjdxgJqt+0cbbqxxClXTtHPKjPUxj/CQ+RYSot7jIb\n1HG6N5LMICqX13wwxvhNb5DjCaaU4x7gePqTbD8cEteZ5HjRRz3WSZVsp1a58Jdcdg4AoFfU6nW/\nvxMA8NN/PAkAOOOcjwIAtqzm+ece1YRi5iJR11xzzbVR2B5FomYj9W4XRXRDPH0j62e0GUnFOMGS\n3m3HcaX7Kfw+jxB3fl5JlXHn+EqNx/n9Lj6Ci+8E9H4X43YDCUNHQmsAJFSOZFrHBBX/F3Kem8+J\nDiQAWwyjZe3V0HpG3JopoQ/4xZE5kaMygbxCXgFrN2PIjK9Jb6JgTum8kCfyowHSlllkS+Mnsksh\nXtCvoXTjib3KrS9XLnhW003G+Z/BCk4wJYQXFDCuFpKLxuIF4w+F2c5AlOV+Apq/ZVSZNm0iXZhr\nnuO5Na+EEGQyo/ZD9H77AkGNU9EIguad/Z0AgEhFOQAgpl1HUEg8lmL/FXX0qrdufJPjb3sDWDwR\nM+v5/aHzyG3WlHHcb7VQ9/jhx1cCAB5b/iwA4MsXfwMAsOatDXx98y0AwLlH5St2OM1Foq655ppr\no7B3BRLdbSuiYD+EQyzBiRazUnnXxbzZOS50hP04x51rv4ReZtZTOK9djdP0loCSpcc9Mo/sSDPT\nRmo5Hs5D9OL1EmbGEnH4xZEFNDVRfkjGBwEAGUNC5fkMlHgoiExGSDPFXOpsgq9ezdEQl18NR8Qt\nGjJNZ3l8Kqd3yc+T6UTBcRb3mUlxzDn+3KHK5PHxp9kVE7TWvW5Is9yf1twtc0mvOX5ZXGdSyNTL\ntfKEuD418W7OS8isO8b16bMMn2p+nkmyPV+U/UUyRHwJaF7mHdel9Dt3EzlOWccJGVcHqgvmE+9P\nFKyP38/jwn7qfKY4PNRWNQAAksm4+uP5rz/zKABg7hR+f+DxhwIAjjtoIQBgWyvrfD37/BoAwO3/\negkAUF5RBQCYMGNvAMATy58BAOzo5PG+EdzjLhJ1zTXXXBuF/Ucj0ZFyqPak8Dhei2ZHOM4faV6w\nM0d/yHGO4/Ppv7saY1fIiQ753NFu0WySErWoSiLpXVTdGvLevPG5TxwpVLkYQGfD8rT7rE4OP06l\n0/CKYzOEk0oWcnRBfZ8dsLpKNfDG23NFkzxCbHEPkVBCiE7OX6QJOOHvYO0ev5BguTi9unJyd3Uq\ni1MTIaKrCLLfYEDIroxIKicsJi4wbq8xdnRIM8c/ECdSS0h5fe0ga5dFB3o1d3Kb5WUcd1mEr16f\nZUYNcH5azS5xiuV69WQ5zlQ/+41IvSgtr31MSLusksjNo3EawvZYRpHUkOxap4TwLWvQL446mixU\n7vcGhch9hdEIGYVRhIRMB+V1X7uN3Od4ccNnzB8LADhwwT6cXys5z06mwOPqB4gwY5VE0ms72X+V\nVJviPeReAy0tAIDNb60GAMycPB6lzEWirrnmmmujsHcFEt3VONFdVRMv5QXf3dz3IfVmio2j2Pl5\naFowjnx7jhY9hZ87OdORWn5dRqpkNHz73t2M783nlxtP6HyWO3cAw1emTCfpKfb6iPLCYS+8ij/s\nF2eWQ0o6Ny3EGYpMfNtEqhESkqv28/wxVUQ+Y6r5E6lVaW8ruRMs37tgLOa9z6kI5WKINUf0F04x\nKa+2ea/FFUL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E0qVLMW7cOHzwgx98xwZZSgF+pFyo8/hi3uxSyDffjqO9/DfDfu6Muxwt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156iZmZvf3t55qZWX8vrkOSJhsSt4/tY+WZRsVdPVFunxZ55PFkP5bnLK321E9cDqOTHTtg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oxVuXjV7D6Zly3MmwjI+T1Y9+dpE4clRyxRnZMk5jg/RlICMfH6U7FlUNDrpYBSb/sk7U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\nAABJRU5ErkJggg==\n","text/plain":["<Figure size 576x396 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"B7VO2ODbtTeG","colab_type":"code","outputId":"27de0dea-4b29-48a8-8ea7-6025115d1ff4","executionInfo":{"status":"ok","timestamp":1553924201838,"user_tz":-330,"elapsed":1317,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["# Number of unique classes\n","n_classes = len(train_df.label.unique())\n","print(\"Number of unique classes =\", n_classes)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Number of unique classes = 6\n"],"name":"stdout"}]},{"metadata":{"_uuid":"9afc1b8533ad9c6167e3a50432127e4c6e9f44e0","id":"R7b2hbCpipEy","colab_type":"text"},"cell_type":"markdown","source":["## Image Augmentation"]},{"metadata":{"_uuid":"5bdbbb3b523d3e82c9c83508842c8536ddb645a9","id":"Hf2H6y0ZipE1","colab_type":"code","outputId":"22123a70-b104-4273-87f5-f33ab7d07cce","executionInfo":{"status":"ok","timestamp":1553924283999,"user_tz":-330,"elapsed":81465,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"cell_type":"code","source":["# Augment data\n","batch_size = 16\n","#train_input_shape = (64, 64, 3)\n","\n","train_datagen = ImageDataGenerator(validation_split=0.2,\n","                                   rescale=1./255.,\n","                                   rotation_range=45,\n","                                   width_shift_range=0.2,\n","                                   height_shift_range=0.2,\n","                                   shear_range=0.7,\n","                                   zoom_range=0.7,\n","                                   horizontal_flip=True,\n","                                   #vertical_flip=True,\n","                                  )\n","\n","train_generator = train_datagen.flow_from_dataframe(dataframe=train_df, \n","                                                    directory=train_dir, \n","                                                    x_col=\"image_name\", \n","                                                    y_col=\"label\", \n","                                                    class_mode=\"other\",\n","                                                    subset=\"training\",\n","                                                    target_size=train_input_shape[0:2],\n","                                                    shuffle=True,\n","                                                    batch_size=batch_size)\n","\n","valid_generator = train_datagen.flow_from_dataframe(dataframe=train_df, \n","                                                    directory=train_dir, \n","                                                    x_col=\"image_name\", \n","                                                    y_col=\"label\", \n","                                                    class_mode=\"other\",\n","                                                    subset=\"validation\",\n","                                                    target_size=train_input_shape[0:2],\n","                                                    shuffle=True,\n","                                                    batch_size=batch_size)\n","\n","STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size\n","STEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size\n","print(\"Total number of batches =\", STEP_SIZE_TRAIN, \"and\", STEP_SIZE_VALID)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Found 13628 images.\n","Found 3406 images.\n","Total number of batches = 851 and 212\n"],"name":"stdout"}]},{"metadata":{"_uuid":"14162cea761f425a19a7f1a0589e445c8f0b06c7","id":"FUjYGRNdipFB","colab_type":"text"},"cell_type":"markdown","source":["## Build model"]},{"metadata":{"_uuid":"0015c78383b92457b131f2f79379847503f01833","id":"vBmJPc8yipFE","colab_type":"code","outputId":"2c902a91-9e16-4d01-ea8f-7b5553450f26","executionInfo":{"status":"ok","timestamp":1553928286680,"user_tz":-330,"elapsed":52346,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"cell_type":"code","source":["# Load pre-trained model\n","from keras_applications import resnet\n","import keras\n","\n","base_model = resnet.ResNet152(weights='imagenet', include_top=False, input_shape=train_input_shape, pooling='avg',\n","                              backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils)\n","\n","#for layer in resnet.layers:\n","#    layer.trainable = False\n","\n","#resnet.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Downloading data from https://github.com/keras-team/keras-applications/releases/download/resnet/resnet152_weights_tf_dim_ordering_tf_kernels_notop.h5\n","234700800/234698864 [==============================] - 3s 0us/step\n"],"name":"stdout"}]},{"metadata":{"_uuid":"e0a60b2a913c2d77e9af8580e65af63c08b136af","id":"pkPMPx20ipFQ","colab_type":"code","outputId":"d83400fc-b30c-4bb0-96d3-d366decdb4cc","executionInfo":{"status":"error","timestamp":1553928293722,"user_tz":-330,"elapsed":1001,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":727}},"cell_type":"code","source":["# Add layers at the end\n","X = base_model.output\n","X = Flatten()(X)\n","\n","X = Dense(16, kernel_initializer='he_uniform')(X)\n","X = BatchNormalization()(X)\n","X = Activation('relu')(X)\n","\n","output = Dense(n_classes, activation='softmax')(X)\n","\n","model = Model(inputs=base_model.input, outputs=output)\n","\n","model.summary()"],"execution_count":0,"outputs":[{"output_type":"error","ename":"AttributeError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)","\u001b[0;32m<ipython-input-39-37b730240c83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFlatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_initializer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'he_uniform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBatchNormalization\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    582\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0mbase_layer_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhave_all_keras_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m             inputs, outputs = self._set_connectivity_metadata_(\n\u001b[0;32m--> 584\u001b[0;31m                 inputs, outputs, args, kwargs)\n\u001b[0m\u001b[1;32m    585\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_set_inputs'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m             \u001b[0;31m# Subclassed network: explicitly set metadata normally set by\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_set_connectivity_metadata_\u001b[0;34m(self, inputs, outputs, args, kwargs)\u001b[0m\n\u001b[1;32m   1414\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mask'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# `mask` should not be serialized.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1415\u001b[0m     self._add_inbound_node(\n\u001b[0;32m-> 1416\u001b[0;31m         input_tensors=inputs, output_tensors=outputs, arguments=kwargs)\n\u001b[0m\u001b[1;32m   1417\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_add_inbound_node\u001b[0;34m(self, input_tensors, output_tensors, arguments)\u001b[0m\n\u001b[1;32m   1522\u001b[0m         \u001b[0minput_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1523\u001b[0m         \u001b[0moutput_tensors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_tensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1524\u001b[0;31m         arguments=arguments)\n\u001b[0m\u001b[1;32m   1525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m     \u001b[0;31m# Update tensor history metadata.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, outbound_layer, inbound_layers, node_indices, tensor_indices, input_tensors, output_tensors, arguments)\u001b[0m\n\u001b[1;32m   1740\u001b[0m         \u001b[0;31m# For compatibility with external Keras, we use the deprecated\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1741\u001b[0m         \u001b[0;31m# accessor here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1742\u001b[0;31m         \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutbound_nodes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1743\u001b[0m     \u001b[0;31m# For compatibility with external Keras, we use the deprecated\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1744\u001b[0m     \u001b[0;31m# accessor here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mAttributeError\u001b[0m: 'GlobalAveragePooling2D' object has no attribute 'outbound_nodes'"]}]},{"metadata":{"_uuid":"6ce184ce000b658fc951097822dc1c5605152d47","id":"xPxVLN3YipFk","colab_type":"code","colab":{}},"cell_type":"code","source":["#optimizer = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n","optimizer=Adam(lr=0.0001)\n","\n","model.compile(loss='sparse_categorical_crossentropy',\n","              optimizer=optimizer, \n","              metrics=['accuracy'])"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"4206d90e8c4dd006d25c29ff9c318fb937826ba2","id":"mDZtRHTiipFu","colab_type":"code","colab":{}},"cell_type":"code","source":["early_stop = EarlyStopping(monitor='val_acc', patience=20, verbose=1, \n","                           mode='auto', restore_best_weights=True)\n","\n","reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.3, patience=5, \n","                              verbose=1, mode='auto')"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"b2cd4cfb400f9d849029593191dd283667cfae9c","id":"Ai4nSL66ipF0","colab_type":"code","colab":{}},"cell_type":"code","source":["%%time\n","n_epoch = 20\n","\n","history = model.fit_generator(generator=train_generator, steps_per_epoch=STEP_SIZE_TRAIN,\n","                              validation_data=valid_generator, validation_steps=STEP_SIZE_VALID,\n","                              epochs=n_epoch,\n","                              shuffle=True,\n","                              verbose=1,\n","                              callbacks=[early_stop, reduce_lr],\n","                              use_multiprocessing=True,\n","                              workers=10\n","                             )"],"execution_count":0,"outputs":[]},{"metadata":{"id":"x8uIuuit8paW","colab_type":"code","colab":{}},"cell_type":"code","source":["model_dir = os.path.join(home_dir, \"models\")\n","model.save(model_dir + '/resnet50_model_sgd.h5')\n","model.save_weights(model_dir + '/resnet50_weights_sgd.h5')"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"898075f768db94965133df52fe650334f744541d","id":"8-bDEvFOipF-","colab_type":"code","colab":{}},"cell_type":"code","source":["# Plot the training graph\n","def plot_training(history):\n","    acc = history.history['acc']\n","    val_acc = history.history['val_acc']\n","    loss = history.history['loss']\n","    val_loss = history.history['val_loss']\n","    epochs = range(len(acc))\n","\n","    fig, axes = plt.subplots(1, 2, figsize=(15,5))\n","    \n","    axes[0].plot(epochs, acc, 'r-', label='Training Accuracy')\n","    axes[0].plot(epochs, val_acc, 'b--', label='Validation Accuracy')\n","    axes[0].set_title('Training and Validation Accuracy')\n","    axes[0].legend(loc='best')\n","\n","    axes[1].plot(epochs, loss, 'r-', label='Training Loss')\n","    axes[1].plot(epochs, val_loss, 'b--', label='Validation Loss')\n","    axes[1].set_title('Training and Validation Loss')\n","    axes[1].legend(loc='best')\n","    \n","    plt.show()\n","    \n","plot_training(history)"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"cd4bf4945a12d11ce13c9cda6befe55c392c0d8d","id":"NQtv0MCgipGI","colab_type":"code","outputId":"5ba27d2e-18bc-4074-c014-757f3a970a31","executionInfo":{"status":"ok","timestamp":1553802502950,"user_tz":-330,"elapsed":42960,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":51}},"cell_type":"code","source":["# Evaluate on validation set\n","result = model.evaluate_generator(generator=valid_generator, verbose=1)\n","result"],"execution_count":0,"outputs":[{"output_type":"stream","text":["213/213 [==============================] - 42s 197ms/step - loss: 0.2745 - acc: 0.9181\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["[0.27447363327174257, 0.91808575]"]},"metadata":{"tags":[]},"execution_count":33}]},{"metadata":{"_uuid":"cd427b2643d0a1e94227977ecf9b579415f9f7d4","id":"ATjwrbViipGR","colab_type":"code","colab":{}},"cell_type":"code","source":["# Classification report and confusion matrix\n","from sklearn.metrics import *\n","import seaborn as sns\n","\n","def showClassficationReport_Generator(model, valid_generator, STEP_SIZE_VALID):\n","    # Loop on each generator batch and predict\n","    y_pred, y_true = [], []\n","    for i in range(STEP_SIZE_VALID):\n","        (X,y) = next(valid_generator)\n","        y_pred.append(model.predict(X))\n","        y_true.append(y)\n","    \n","    # Create a flat list for y_true and y_pred\n","    y_pred = [subresult for result in y_pred for subresult in result]\n","    y_true = [subresult for result in y_true for subresult in result]\n","    \n","    y_true = np.asarray(y_true).ravel()\n","    \n","    # Update Prediction vector based on argmax\n","    #y_pred = np.asarray(y_pred).astype('float32').ravel()\n","    #y_pred = y_pred >= 0.5\n","    #y_pred = y_pred.astype('int').ravel()\n","    y_pred = np.argmax(y_pred, axis=1)\n","    y_pred = np.asarray(y_pred).ravel()\n","    \n","    # Confusion Matrix\n","    conf_matrix = confusion_matrix(y_true, y_pred, labels=[0,1,2,3,4,5])\n","    sns.heatmap(conf_matrix, annot=True, fmt=\"d\", square=True, cbar=False, \n","                cmap=plt.cm.gray, xticklabels=labels, yticklabels=labels)\n","    plt.ylabel('Actual')\n","    plt.xlabel('Predicted')\n","    plt.title('Confusion Matrix')\n","    plt.show()\n","    \n","    print(classification_report(y_true, y_pred))\n","    #print(\"\\nAUC: \", roc_auc_score(y_true, y_pred, average='micro'))\n","\n","showClassficationReport_Generator(model, valid_generator, STEP_SIZE_VALID)"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"c841e3ab0b59e0a3bf9ae1885d99533b05f06fc4","id":"YQSdReezipGd","colab_type":"text"},"cell_type":"markdown","source":["## Prepare data for prediction on test set"]},{"metadata":{"id":"6e7bQQBIqtBu","colab_type":"code","colab":{}},"cell_type":"code","source":["test_df = pd.read_csv(dataset_dir + \"/test_WyRytb0.csv\")\n","test_df.shape"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"33c7560628f0fe446b6355d26f9fee67da4fc58a","id":"9WSL1H-WipGf","colab_type":"code","colab":{}},"cell_type":"code","source":["test_datagen = ImageDataGenerator(rescale=1./255.)\n","\n","test_generator = test_datagen.flow_from_dataframe(dataframe=test_df, \n","                                                    directory=train_dir, \n","                                                    x_col=\"image_name\", \n","                                                    #y_col=\"label\", \n","                                                    class_mode=None,\n","                                                    target_size=train_input_shape[0:2], \n","                                                    batch_size=1,\n","                                                    shuffle=False\n","                                                 )"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"08afdd8c3000a63d5846644d83bebd55bb2e7fdb","id":"6hixVyr-ipGw","colab_type":"text"},"cell_type":"markdown","source":["## Predict and Submit"]},{"metadata":{"_uuid":"067733fbfd186fa1d65d4110165b345a6c9f1898","id":"xX7gSEVUipG5","colab_type":"code","colab":{}},"cell_type":"code","source":["# Predict on test data\n","test_generator.reset()\n","predictions = model.predict_generator(test_generator,verbose=1)\n","predictions = np.argmax(predictions, axis=1)\n","#predictions = predictions.astype('int').ravel()\n","predictions.shape"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"3c52db7624172d624771f0a00205568676fcc853","id":"QK9ubPZwipHB","colab_type":"code","colab":{}},"cell_type":"code","source":["# Retrieve filenames\n","import re\n","#test_img_ids = [re.split(\"/\", val)[1] for val in test_generator.filenames]\n","test_img_ids = test_generator.filenames\n","len(test_img_ids)"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"5785b0499ce1d7425bce81e98c2d20d29dc7cc7e","id":"QpsDW_baipHL","colab_type":"code","colab":{}},"cell_type":"code","source":["# Create dataframe for submission\n","submission_df = pd.DataFrame({'image_name' : test_img_ids,\n","                              'label' : predictions })\n","submission_df.head()"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"f2b7c10b6dd4103cf2477fe6f492a635f312b1e1","id":"O16A0M70ipHW","colab_type":"code","colab":{}},"cell_type":"code","source":["# Create submission file\n","submission_dir = os.path.join(home_dir, \"submissions\")\n","submission_df.to_csv(submission_dir + '/submission_resnet50_4.csv', index=False)"],"execution_count":0,"outputs":[]}]}