Baby_Names_Gender_Classification.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Baby_Names Gender Classification.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPW8BCCZeNa+29R26NprUgq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DataMinati/Super-Notebooks-1K/blob/main/Baby_Names_Gender_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Th3_HP157D3k"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uvFt4hUo7YVm",
        "outputId": "cf28bdaa-72cb-4a36-a529-7e49caa2b86b"
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/baby_names.csv'\n",
        "df = pd.read_csv(url,error_bad_lines=False)\n",
        "df.head()"
      ],
      "execution_count": 2,
      "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>Unnamed: 0</th>\n",
              "      <th>State</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Year</th>\n",
              "      <th>Name</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>GA</td>\n",
              "      <td>F</td>\n",
              "      <td>1910</td>\n",
              "      <td>Mary</td>\n",
              "      <td>841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>GA</td>\n",
              "      <td>F</td>\n",
              "      <td>1910</td>\n",
              "      <td>Annie</td>\n",
              "      <td>553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>GA</td>\n",
              "      <td>F</td>\n",
              "      <td>1910</td>\n",
              "      <td>Mattie</td>\n",
              "      <td>320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>GA</td>\n",
              "      <td>F</td>\n",
              "      <td>1910</td>\n",
              "      <td>Ruby</td>\n",
              "      <td>279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>GA</td>\n",
              "      <td>F</td>\n",
              "      <td>1910</td>\n",
              "      <td>Willie</td>\n",
              "      <td>275</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0 State Sex  Year    Name  Count\n",
              "0           0    GA   F  1910    Mary    841\n",
              "1           1    GA   F  1910   Annie    553\n",
              "2           2    GA   F  1910  Mattie    320\n",
              "3           3    GA   F  1910    Ruby    279\n",
              "4           4    GA   F  1910  Willie    275"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ShTbWKO18v-B"
      },
      "source": [
        "all_categories = ['F', 'M']\n",
        "category_lines = {\"F\" : df.Name[df.Sex == 'F'].tolist(), \"M\" : df.Name[df.Sex == 'M'].tolist()}\n",
        "n_categories = 2\n",
        "\n",
        "import string\n",
        "\n",
        "all_letters = string.ascii_letters + \" .,;'\"\n",
        "n_letters = len(all_letters)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yggJ-X3D81dC"
      },
      "source": [
        "## Creating the Model\n",
        "import torch\n",
        "\n",
        "# Find letter index from all_letters, e.g. \"a\" = 0\n",
        "def letterToIndex(letter):\n",
        "    return all_letters.find(letter)\n",
        "\n",
        "# Just for demonstration, turn a letter into a <1 x n_letters> Tensor\n",
        "def letterToTensor(letter):\n",
        "    tensor = torch.zeros(1, n_letters)\n",
        "    tensor[0][letterToIndex(letter)] = 1\n",
        "    return tensor\n",
        "\n",
        "# Turn a line into a <line_length x 1 x n_letters>,\n",
        "# or an array of one-hot letter vectors\n",
        "def lineToTensor(line):\n",
        "    tensor = torch.zeros(len(line), 1, n_letters)\n",
        "    for li, letter in enumerate(line):\n",
        "        tensor[li][0][letterToIndex(letter)] = 1\n",
        "    return tensor"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6caS9S1U85K5",
        "outputId": "3046373b-eaf6-4e73-bc2a-72373bfeeec6"
      },
      "source": [
        "letterToIndex(\"n\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQOZD1ub88aR",
        "outputId": "d854b0ad-9dc5-4b70-b3ee-21f4f5ef049b"
      },
      "source": [
        "letterToTensor(\"n\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "         0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1GGAkXG898T",
        "outputId": "a5c211b3-d570-405b-ab3f-851f3969cf04"
      },
      "source": [
        "lineToTensor(\"name\"), lineToTensor(\"name\").size()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0.]],\n",
              " \n",
              "         [[1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0.]],\n",
              " \n",
              "         [[0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "           0., 0., 0., 0., 0., 0.]]]), torch.Size([4, 1, 57]))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0o5XmNKN8_Ot"
      },
      "source": [
        "## Creating Model Arch\n",
        "import torch.nn as nn\n",
        "\n",
        "class RNN(nn.Module):\n",
        "    def __init__(self, input_size, hidden_size, output_size):\n",
        "        super(RNN, self).__init__()\n",
        "        \n",
        "        self.hidden_size = hidden_size\n",
        "        # in linear Layer :  Input @ linear_layer shape\n",
        "        self.i2h = nn.Linear(input_size + hidden_size, hidden_size)\n",
        "        self.i2o = nn.Linear(input_size + hidden_size, output_size)\n",
        "        self.softmax = nn.LogSoftmax(dim=1)\n",
        "        \n",
        "    def forward(self, input, hidden):\n",
        "        combined = torch.cat((input, hidden), dim=1)\n",
        "        hidden = self.i2h(combined)\n",
        "        output = self.i2o(combined)\n",
        "        output = self.softmax(output)\n",
        "        \n",
        "        return output, hidden\n",
        "    \n",
        "    def initHidden(self):\n",
        "        return torch.zeros(1, self.hidden_size)\n",
        "    \n",
        "n_hidden = 128\n",
        "rnn = RNN(n_letters, n_hidden, n_categories)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zTpBCVmz9FYa",
        "outputId": "c497e437-bb2f-448b-e211-d6468a41f138"
      },
      "source": [
        "x = torch.zeros(2,3)\n",
        "x2 = torch.ones(2,3)\n",
        "\n",
        "torch.cat((x, x2), dim=0), torch.cat((x, x2), dim=1)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[0., 0., 0.],\n",
              "         [0., 0., 0.],\n",
              "         [1., 1., 1.],\n",
              "         [1., 1., 1.]]), tensor([[0., 0., 0., 1., 1., 1.],\n",
              "         [0., 0., 0., 1., 1., 1.]]))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9CSC3CgU9HQO"
      },
      "source": [
        "input = letterToTensor('A')\n",
        "hidden = torch.zeros(1, n_hidden)\n",
        "output, next_hidden = rnn(input, hidden)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rab3arN39I5f",
        "outputId": "2c8bc6a7-07b9-45cd-ede1-290d8c37507f"
      },
      "source": [
        "input = lineToTensor('Albert')\n",
        "hidden = torch.zeros(1, n_hidden)\n",
        "\n",
        "output, next_hidden = rnn(input[0], hidden)\n",
        "print(output)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[-0.7960, -0.5999]], grad_fn=<LogSoftmaxBackward>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8RJNAxy9K4J",
        "outputId": "18644c14-ae23-48ed-eb55-4a2242eae7b2"
      },
      "source": [
        "# Training\n",
        "def categoryFromOutput(output):\n",
        "    top_n, top_i = output.topk(1)\n",
        "    category_i = top_i[0].item()\n",
        "    return all_categories[category_i], category_i\n",
        "\n",
        "print(categoryFromOutput(output))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('M', 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IgYOQ6KO9Njh",
        "outputId": "6a9497f0-86a5-43be-acbf-9db66742e084"
      },
      "source": [
        "import random\n",
        "\n",
        "def randomChoice(l):\n",
        "    return l[random.randint(0, len(l) - 1)]\n",
        "\n",
        "def randomTrainingExample():\n",
        "    category = randomChoice(all_categories)\n",
        "    line = randomChoice(category_lines[category])\n",
        "    category_tensor = torch.tensor([all_categories.index(category)], dtype=torch.long)\n",
        "    line_tensor = lineToTensor(line)\n",
        "    return category, line, category_tensor, line_tensor\n",
        "\n",
        "for i in range(10):\n",
        "    category, line, category_tensor, line_tensor = randomTrainingExample()\n",
        "    print('category =', category, '/ line =', line)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "category = F / line = Lorie\n",
            "category = F / line = Virginia\n",
            "category = M / line = Ralph\n",
            "category = F / line = Emmy\n",
            "category = M / line = Brandon\n",
            "category = M / line = Cedrick\n",
            "category = F / line = Lena\n",
            "category = M / line = Wayne\n",
            "category = M / line = Stuart\n",
            "category = M / line = Walter\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rYkPJEpw9Qzg"
      },
      "source": [
        "criterion = nn.NLLLoss()"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpHX7VLV9SwL"
      },
      "source": [
        "learning_rate = 0.005 # If you set this too high, it might explode. If too low, it might not learn\n",
        "\n",
        "def train(category_tensor, line_tensor):\n",
        "    hidden = rnn.initHidden()\n",
        "\n",
        "    rnn.zero_grad()\n",
        "\n",
        "    for i in range(line_tensor.size()[0]):\n",
        "        output, hidden = rnn(line_tensor[i], hidden)\n",
        "\n",
        "    loss = criterion(output, category_tensor)\n",
        "    loss.backward()\n",
        "\n",
        "    # Add parameters' gradients to their values, multiplied by learning rate\n",
        "    for p in rnn.parameters():\n",
        "        p.data.add_(p.grad.data, alpha=-learning_rate)\n",
        "        # this method doc : https://pytorch.org/docs/stable/generated/torch.Tensor.add.html#torch.Tensor.add\n",
        "\n",
        "    return output, loss.item()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "joDAQag_9Ul0",
        "outputId": "2e570893-bbfd-49fa-ced1-f2c3e3e79b33"
      },
      "source": [
        "import time\n",
        "import math\n",
        "\n",
        "n_iters = 2000000\n",
        "print_every = 5000\n",
        "plot_every = 1000\n",
        "\n",
        "\n",
        "\n",
        "# Keep track of losses for plotting\n",
        "current_loss = 0\n",
        "all_losses = []\n",
        "\n",
        "def timeSince(since):\n",
        "    now = time.time()\n",
        "    s = now - since\n",
        "    m = math.floor(s / 60)\n",
        "    s -= m * 60\n",
        "    return '%dm %ds' % (m, s)\n",
        "\n",
        "start = time.time()\n",
        "\n",
        "for iter in range(1, n_iters + 1):\n",
        "    category, line, category_tensor, line_tensor = randomTrainingExample()\n",
        "    output, loss = train(category_tensor, line_tensor)\n",
        "    current_loss += loss\n",
        "\n",
        "    # Print iter number, loss, name and guess\n",
        "    if iter % print_every == 0:\n",
        "        guess, guess_i = categoryFromOutput(output)\n",
        "        correct = '✓' if guess == category else '✗ (%s)' % category\n",
        "        print('%d %d%% (%s) %.4f %s / %s %s' % (iter, iter / n_iters * 100, timeSince(start), loss, line, guess, correct))\n",
        "\n",
        "    # Add current loss avg to list of losses\n",
        "    if iter % plot_every == 0:\n",
        "        all_losses.append(current_loss / plot_every)\n",
        "        current_loss = 0\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5000 0% (0m 5s) 0.5897 Wendy / F ✓\n",
            "10000 0% (0m 9s) 0.5681 Aubrey / M ✓\n",
            "15000 0% (0m 14s) 0.6040 Malcolm / M ✓\n",
            "20000 1% (0m 19s) 0.5665 Murry / M ✓\n",
            "25000 1% (0m 24s) 0.3806 Evonne / F ✓\n",
            "30000 1% (0m 29s) 0.2895 Randy / M ✓\n",
            "35000 1% (0m 33s) 0.1267 Karl / M ✓\n",
            "40000 2% (0m 38s) 0.1903 Deshawn / M ✓\n",
            "45000 2% (0m 43s) 0.0612 Brad / M ✓\n",
            "50000 2% (0m 47s) 0.2320 Darnell / M ✓\n",
            "55000 2% (0m 52s) 0.0668 Dameon / M ✓\n",
            "60000 3% (0m 57s) 0.1240 Raymond / M ✓\n",
            "65000 3% (1m 1s) 0.2327 Edgar / M ✓\n",
            "70000 3% (1m 6s) 0.0236 Nita / F ✓\n",
            "75000 3% (1m 11s) 0.0162 Jayda / F ✓\n",
            "80000 4% (1m 15s) 0.3509 Emanuel / M ✓\n",
            "85000 4% (1m 20s) 0.3410 Kelsi / F ✓\n",
            "90000 4% (1m 25s) 0.1906 Johnnie / F ✓\n",
            "95000 4% (1m 29s) 0.2921 Abygail / F ✓\n",
            "100000 5% (1m 34s) 0.0202 Lisa / F ✓\n",
            "105000 5% (1m 39s) 0.8323 May / M ✗ (F)\n",
            "110000 5% (1m 43s) 0.1851 Jadarius / M ✓\n",
            "115000 5% (1m 48s) 0.5926 Christin / F ✓\n",
            "120000 6% (1m 53s) 0.0589 Brenton / M ✓\n",
            "125000 6% (1m 57s) 0.0045 Malinda / F ✓\n",
            "130000 6% (2m 2s) 0.0242 Della / F ✓\n",
            "135000 6% (2m 7s) 0.0038 Kaylea / F ✓\n",
            "140000 7% (2m 11s) 0.2577 Sam / M ✓\n",
            "145000 7% (2m 16s) 0.3610 Lynn / M ✓\n",
            "150000 7% (2m 21s) 0.0534 Flora / F ✓\n",
            "155000 7% (2m 25s) 0.2690 Darian / M ✓\n",
            "160000 8% (2m 30s) 0.1750 Wade / M ✓\n",
            "165000 8% (2m 35s) 0.1724 Nadine / F ✓\n",
            "170000 8% (2m 39s) 0.2867 Nannie / F ✓\n",
            "175000 8% (2m 44s) 0.3611 Jaylah / F ✓\n",
            "180000 9% (2m 49s) 0.6706 Karley / F ✓\n",
            "185000 9% (2m 53s) 0.0163 Ada / F ✓\n",
            "190000 9% (2m 58s) 1.2300 Marion / M ✗ (F)\n",
            "195000 9% (3m 3s) 1.0092 Remy / F ✗ (M)\n",
            "200000 10% (3m 7s) 0.5357 Cerenity / F ✓\n",
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            "1720000 86% (26m 49s) 0.1974 Jacoby / M ✓\n",
            "1725000 86% (26m 54s) 1.5985 Lane / F ✗ (M)\n",
            "1730000 86% (26m 58s) 0.4511 Addie / F ✓\n",
            "1735000 86% (27m 3s) 0.1499 Vivienne / F ✓\n",
            "1740000 87% (27m 8s) 0.6472 Maxim / M ✓\n",
            "1745000 87% (27m 12s) 1.4551 Zane / F ✗ (M)\n",
            "1750000 87% (27m 17s) 0.0172 Anna / F ✓\n",
            "1755000 87% (27m 22s) 0.2233 Shirley / F ✓\n",
            "1760000 88% (27m 26s) 0.1256 Andrew / M ✓\n",
            "1765000 88% (27m 31s) 1.3122 Daniel / M ✗ (F)\n",
            "1770000 88% (27m 36s) 0.7240 Lynnette / M ✗ (F)\n",
            "1775000 88% (27m 40s) 1.3791 Eli / F ✗ (M)\n",
            "1780000 89% (27m 45s) 0.4914 Milton / M ✓\n",
            "1785000 89% (27m 50s) 0.1475 Theodore / M ✓\n",
            "1790000 89% (27m 54s) 0.0742 Baron / M ✓\n",
            "1795000 89% (27m 59s) 0.0347 Rakeem / M ✓\n",
            "1800000 90% (28m 4s) 0.0835 Travis / M ✓\n",
            "1805000 90% (28m 8s) 0.0383 Furman / M ✓\n",
            "1810000 90% (28m 13s) 0.1176 Luciano / M ✓\n",
            "1815000 90% (28m 17s) 0.0301 Dena / F ✓\n",
            "1820000 91% (28m 22s) 2.7556 Avis / M ✗ (F)\n",
            "1825000 91% (28m 27s) 0.0391 Scott / M ✓\n",
            "1830000 91% (28m 31s) 0.0628 Elva / F ✓\n",
            "1835000 91% (28m 36s) 0.5277 Skylar / M ✓\n",
            "1840000 92% (28m 41s) 0.5590 Maurice / M ✓\n",
            "1845000 92% (28m 45s) 0.4523 Allan / M ✓\n",
            "1850000 92% (28m 50s) 0.0920 Destinee / F ✓\n",
            "1855000 92% (28m 55s) 0.2531 Jimmy / M ✓\n",
            "1860000 93% (28m 59s) 0.0186 Martika / F ✓\n",
            "1865000 93% (29m 4s) 0.0065 Loretta / F ✓\n",
            "1870000 93% (29m 9s) 0.6573 Mitzi / F ✓\n",
            "1875000 93% (29m 14s) 0.1917 Elsie / F ✓\n",
            "1880000 94% (29m 18s) 0.0609 Sheldon / M ✓\n",
            "1885000 94% (29m 23s) 0.0080 Craig / M ✓\n",
            "1890000 94% (29m 27s) 0.4453 Alberto / M ✓\n",
            "1895000 94% (29m 32s) 0.8726 Queen / M ✗ (F)\n",
            "1900000 95% (29m 37s) 0.0724 Emma / F ✓\n",
            "1905000 95% (29m 41s) 0.5777 Leroy / M ✓\n",
            "1910000 95% (29m 46s) 0.4333 Margaret / F ✓\n",
            "1915000 95% (29m 51s) 2.7885 Brynn / M ✗ (F)\n",
            "1920000 96% (29m 55s) 1.0098 Jere / F ✗ (M)\n",
            "1925000 96% (30m 0s) 0.0350 Wilmer / M ✓\n",
            "1930000 96% (30m 4s) 0.3428 Noah / M ✓\n",
            "1935000 96% (30m 9s) 0.6908 Ashly / F ✓\n",
            "1940000 97% (30m 14s) 0.7010 Lesley / M ✗ (F)\n",
            "1945000 97% (30m 18s) 0.4711 Danyell / F ✓\n",
            "1950000 97% (30m 23s) 0.3977 Chimere / F ✓\n",
            "1955000 97% (30m 27s) 0.0064 Shabria / F ✓\n",
            "1960000 98% (30m 32s) 0.1015 Kylan / M ✓\n",
            "1965000 98% (30m 37s) 0.0181 Ted / M ✓\n",
            "1970000 98% (30m 42s) 0.2133 Aaron / M ✓\n",
            "1975000 98% (30m 46s) 0.1237 Braxton / M ✓\n",
            "1980000 99% (30m 51s) 0.0438 Jacob / M ✓\n",
            "1985000 99% (30m 56s) 0.1674 Jeffrey / M ✓\n",
            "1990000 99% (31m 0s) 0.1051 Stefanie / F ✓\n",
            "1995000 99% (31m 5s) 0.0204 Victoria / F ✓\n",
            "2000000 100% (31m 9s) 0.0162 Erick / M ✓\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "BgMxI15l9ZYU",
        "outputId": "c2cdaca2-7c33-42c8-cd60-089bfd491396"
      },
      "source": [
        "import matplotlib.ticker as ticker\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(all_losses)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3183e9dd50>]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "6k3MOoSeE_Lh",
        "outputId": "f2f8bf84-637b-4c89-982e-55cadfe31b0c"
      },
      "source": [
        "# Keep track of correct guesses in a confusion matrix\n",
        "confusion = torch.zeros(n_categories, n_categories)\n",
        "n_confusion = 10000\n",
        "\n",
        "# Just return an output given a line\n",
        "def evaluate(line_tensor):\n",
        "    hidden = rnn.initHidden()\n",
        "\n",
        "    for i in range(line_tensor.size()[0]):\n",
        "        output, hidden = rnn(line_tensor[i], hidden)\n",
        "\n",
        "    return output\n",
        "\n",
        "# Go through a bunch of examples and record which are correctly guessed\n",
        "for i in range(n_confusion):\n",
        "    category, line, category_tensor, line_tensor = randomTrainingExample()\n",
        "    output = evaluate(line_tensor)\n",
        "    guess, guess_i = categoryFromOutput(output)\n",
        "    category_i = all_categories.index(category)\n",
        "    confusion[category_i][guess_i] += 1\n",
        "\n",
        "# Normalize by dividing every row by its sum\n",
        "for i in range(n_categories):\n",
        "    confusion[i] = confusion[i] / confusion[i].sum()\n",
        "\n",
        "# Set up plot\n",
        "fig = plt.figure()\n",
        "ax = fig.add_subplot(111)\n",
        "cax = ax.matshow(confusion.numpy())\n",
        "fig.colorbar(cax)\n",
        "\n",
        "# Set up axes\n",
        "ax.set_xticklabels([''] + all_categories, rotation=90)\n",
        "ax.set_yticklabels([''] + all_categories)\n",
        "\n",
        "# Force label at every tick\n",
        "ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
        "ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
        "\n",
        "# sphinx_gallery_thumbnail_number = 2\n",
        "plt.show()\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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geDNwduD4HHD3kPreA/z9sBsaUtK0qxrllVYXqmrnOG6b5BeAncBbh11nSEka569754GtA8db+ueeJ8m9wG8Cb62qi8MqNKQkUeN7OegJYHuSbfTCaQ/w4OAFSd4I/BEwW1XfWKtCQ0qaeuPb9K6qLifZDxwHZoCjVXUyySFgoarmgN8FXgr8dRKAr1fVA1er05CSpt2YHzCuqnlgfsW5gwOf7x2lPkNKmnIFlI/FSGpWuemdpMZVw/tJpdbxwcIkTwH/tW43mJxNwIVJN0Ij2ah/s9dU1auup4Ikn6P376eLC1U1ez33G9W6htRGlWRhXAvadGP4N7t5+eyepKYZUpKaZkhdmyNrX6LG+De7STknJalp9qQkNc2QktQ0Q0pS0wwpSU0zpCQ17f8A0dx1xoe2XXQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1EFY-aYrFFKW",
        "outputId": "ad586b53-0e82-4a9e-dc75-356dc57b2505"
      },
      "source": [
        "def predict(input_line, n_predictions=2):\n",
        "    print('\\n> %s' % input_line)\n",
        "    with torch.no_grad():\n",
        "        output = evaluate(lineToTensor(input_line))\n",
        "        print(output)\n",
        "        # Get top N categories\n",
        "        topv, topi = output.topk(n_predictions, 1, True)\n",
        "        predictions = []\n",
        "\n",
        "        for i in range(n_predictions):\n",
        "            value = topv[0][i].item()\n",
        "            category_index = topi[0][i].item()\n",
        "            print('(%.2f) %s' % (value, all_categories[category_index]))\n",
        "            predictions.append([value, all_categories[category_index]])\n",
        "\n",
        "predict('Dovesky')\n",
        "predict('Jackson')\n",
        "predict('Satoshi')\n",
        "predict('Mohammad')\n",
        "predict('Masoud')\n",
        "predict(\"Kate\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "> Dovesky\n",
            "tensor([[-0.9058, -0.5179]])\n",
            "(-0.52) M\n",
            "(-0.91) F\n",
            "\n",
            "> Jackson\n",
            "tensor([[-4.1664, -0.0156]])\n",
            "(-0.02) M\n",
            "(-4.17) F\n",
            "\n",
            "> Satoshi\n",
            "tensor([[-0.0933, -2.4180]])\n",
            "(-0.09) F\n",
            "(-2.42) M\n",
            "\n",
            "> Mohammad\n",
            "tensor([[-2.9330, -0.0547]])\n",
            "(-0.05) M\n",
            "(-2.93) F\n",
            "\n",
            "> Masoud\n",
            "tensor([[-4.4782, -0.0114]])\n",
            "(-0.01) M\n",
            "(-4.48) F\n",
            "\n",
            "> Kate\n",
            "tensor([[-0.6928, -0.6935]])\n",
            "(-0.69) F\n",
            "(-0.69) M\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGZd9tJRFLNG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}