3_5_Classifying_Newswires.ipynb

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    "colab": {
      "name": "3.5 - Classifying Newswires.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMFN/7B6Y6T7WoK70G0ZO4Q",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Kadakol/Keras/blob/master/3_5_Classifying_Newswires.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPi-TfCOf9ZB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "b0038471-00b2-4989-9844-550b3059c9b7"
      },
      "source": [
        "from keras.datasets import reuters\n",
        "\n",
        "(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/text-datasets/reuters.npz\n",
            "2113536/2110848 [==============================] - 1s 1us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQJZUI5PgrCb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e19ec714-6fb6-48b6-b8a4-85fb78f4187c"
      },
      "source": [
        "len(train_data)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8982"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llhzCw8jgwpe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0859e4f4-2524-433b-8d0c-e20328275e16"
      },
      "source": [
        "len(test_data)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2246"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3ka4J3Pgxqa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "f354c572-9680-4450-d450-13694fb9469b"
      },
      "source": [
        "train_data"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chkZUDvPg2T4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "a0c15696-4491-4b5c-83a7-9a368131f7a6"
      },
      "source": [
        "word_index = reuters.get_word_index()\n",
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
        "\n",
        "decoded_newswire = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])\n",
        "\n",
        "decoded_newswire"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'? ? ? said as a result of its december acquisition of space co it expects earnings per share in 1987 of 1 15 to 1 30 dlrs per share up from 70 cts in 1986 the company said pretax net should rise to nine to 10 mln dlrs from six mln dlrs in 1986 and rental operation revenues to 19 to 22 mln dlrs from 12 5 mln dlrs it said cash flow per share this year should be 2 50 to three dlrs reuter 3'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrIl45pDhX56",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "def vectorize_sequences(sequences, dimension=10000):\n",
        "  results = np.zeros((len(sequences), dimension))\n",
        "  for i, sequence in enumerate(sequences):\n",
        "    results[i, sequence] = 1.\n",
        "  return results\n",
        "\n",
        "x_train = vectorize_sequences(train_data)\n",
        "x_test = vectorize_sequences(test_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZFMSxDbdiv-u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def one_hot_encode(labels, dimension=46):\n",
        "  results = np.zeros((len(labels), dimension))\n",
        "  for i, label in enumerate(labels):\n",
        "    results[i, label] = 1.\n",
        "  return results  \n",
        "\n",
        "y_train = one_hot_encode(train_labels)\n",
        "y_test = one_hot_encode(test_labels)  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kTNcsTIVjYEv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.utils.np_utils import to_categorical\n",
        "\n",
        "y_train = to_categorical(train_labels)\n",
        "y_test = to_categorical(test_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnQQf3B_jjjp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras import models, layers\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "model.add(layers.Dense(46, activation='softmax'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqhv74VakM8V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='rmsprop',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0ernkL1kvTU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_val = x_train[:1000]\n",
        "partial_x_train = x_train[1000:]\n",
        "\n",
        "y_val = y_train[:1000]\n",
        "partial_y_train = y_train[1000:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOEDYC53lBb5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "outputId": "4f4006fc-c0dd-4a2c-e638-fb19aeea1895"
      },
      "source": [
        "history = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=20,\n",
        "                    batch_size=512,\n",
        "                    validation_data=[x_val, y_val])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 7982 samples, validate on 1000 samples\n",
            "Epoch 1/20\n",
            "7982/7982 [==============================] - 1s 152us/step - loss: 2.6039 - acc: 0.5608 - val_loss: 1.7290 - val_acc: 0.6430\n",
            "Epoch 2/20\n",
            "7982/7982 [==============================] - 1s 112us/step - loss: 1.3934 - acc: 0.7132 - val_loss: 1.3038 - val_acc: 0.7260\n",
            "Epoch 3/20\n",
            "7982/7982 [==============================] - 1s 112us/step - loss: 1.0399 - acc: 0.7749 - val_loss: 1.1488 - val_acc: 0.7400\n",
            "Epoch 4/20\n",
            "7982/7982 [==============================] - 1s 113us/step - loss: 0.8255 - acc: 0.8220 - val_loss: 1.0543 - val_acc: 0.7740\n",
            "Epoch 5/20\n",
            "7982/7982 [==============================] - 1s 113us/step - loss: 0.6543 - acc: 0.8607 - val_loss: 1.0153 - val_acc: 0.7600\n",
            "Epoch 6/20\n",
            "7982/7982 [==============================] - 1s 113us/step - loss: 0.5262 - acc: 0.8901 - val_loss: 0.9328 - val_acc: 0.7990\n",
            "Epoch 7/20\n",
            "7982/7982 [==============================] - 1s 113us/step - loss: 0.4270 - acc: 0.9095 - val_loss: 0.9144 - val_acc: 0.8030\n",
            "Epoch 8/20\n",
            "7982/7982 [==============================] - 1s 113us/step - loss: 0.3448 - acc: 0.9278 - val_loss: 0.9070 - val_acc: 0.8110\n",
            "Epoch 9/20\n",
            "7982/7982 [==============================] - 1s 113us/step - loss: 0.2844 - acc: 0.9359 - val_loss: 0.9494 - val_acc: 0.8050\n",
            "Epoch 10/20\n",
            "7982/7982 [==============================] - 1s 113us/step - loss: 0.2469 - acc: 0.9445 - val_loss: 0.9255 - val_acc: 0.8150\n",
            "Epoch 11/20\n",
            "7982/7982 [==============================] - 1s 113us/step - loss: 0.2088 - acc: 0.9484 - val_loss: 0.9185 - val_acc: 0.8140\n",
            "Epoch 12/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 0.1846 - acc: 0.9535 - val_loss: 0.9369 - val_acc: 0.8110\n",
            "Epoch 13/20\n",
            "7982/7982 [==============================] - 1s 116us/step - loss: 0.1655 - acc: 0.9541 - val_loss: 0.9502 - val_acc: 0.8170\n",
            "Epoch 14/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 0.1538 - acc: 0.9541 - val_loss: 0.9627 - val_acc: 0.8110\n",
            "Epoch 15/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 0.1393 - acc: 0.9563 - val_loss: 0.9798 - val_acc: 0.8120\n",
            "Epoch 16/20\n",
            "7982/7982 [==============================] - 1s 116us/step - loss: 0.1347 - acc: 0.9560 - val_loss: 1.0215 - val_acc: 0.8110\n",
            "Epoch 17/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 0.1295 - acc: 0.9549 - val_loss: 1.1568 - val_acc: 0.7800\n",
            "Epoch 18/20\n",
            "7982/7982 [==============================] - 1s 114us/step - loss: 0.1194 - acc: 0.9578 - val_loss: 1.0361 - val_acc: 0.8070\n",
            "Epoch 19/20\n",
            "7982/7982 [==============================] - 1s 114us/step - loss: 0.1174 - acc: 0.9568 - val_loss: 1.0771 - val_acc: 0.8060\n",
            "Epoch 20/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 0.1124 - acc: 0.9570 - val_loss: 1.0717 - val_acc: 0.8050\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1AZLer9lRAu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6290e14f-ecd1-4f03-833f-29aeecb73559"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training Loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
        "plt.title('Training and Validation Losses')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMJhlvePmME0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "700e3f65-e4b8-4a0f-d888-a8fce83a7426"
      },
      "source": [
        "plt.clf()\n",
        "\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.plot(epochs, acc, 'ro', label='Training Accuracy')\n",
        "plt.plot(epochs, val_acc, 'r', label='Validation Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yb7c5s3wm2dv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "7fc34b7a-a403-435f-edf6-627ed2d5773b"
      },
      "source": [
        "model = models.Sequential()\n",
        "model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "model.add(layers.Dense(46, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer='rmsprop',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['acc'])\n",
        "model.fit(partial_x_train,\n",
        "          partial_y_train,\n",
        "          epochs=9,\n",
        "          batch_size=512,\n",
        "          validation_data=[x_val, y_val])\n",
        "\n",
        "results = model.evaluate(x_test, y_test)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 7982 samples, validate on 1000 samples\n",
            "Epoch 1/9\n",
            "7982/7982 [==============================] - 1s 131us/step - loss: 2.7365 - acc: 0.4907 - val_loss: 1.8245 - val_acc: 0.6440\n",
            "Epoch 2/9\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 1.4725 - acc: 0.7015 - val_loss: 1.3394 - val_acc: 0.7250\n",
            "Epoch 3/9\n",
            "7982/7982 [==============================] - 1s 114us/step - loss: 1.0755 - acc: 0.7769 - val_loss: 1.1510 - val_acc: 0.7590\n",
            "Epoch 4/9\n",
            "7982/7982 [==============================] - 1s 114us/step - loss: 0.8490 - acc: 0.8222 - val_loss: 1.0625 - val_acc: 0.7810\n",
            "Epoch 5/9\n",
            "7982/7982 [==============================] - 1s 116us/step - loss: 0.6813 - acc: 0.8572 - val_loss: 1.0062 - val_acc: 0.7860\n",
            "Epoch 6/9\n",
            "7982/7982 [==============================] - 1s 114us/step - loss: 0.5490 - acc: 0.8854 - val_loss: 0.9530 - val_acc: 0.7920\n",
            "Epoch 7/9\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 0.4398 - acc: 0.9105 - val_loss: 0.9067 - val_acc: 0.8110\n",
            "Epoch 8/9\n",
            "7982/7982 [==============================] - 1s 116us/step - loss: 0.3567 - acc: 0.9253 - val_loss: 0.9041 - val_acc: 0.8150\n",
            "Epoch 9/9\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 0.2944 - acc: 0.9375 - val_loss: 0.8958 - val_acc: 0.8230\n",
            "2246/2246 [==============================] - 0s 80us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hr-HpHEnn2OZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3f62e009-4870-4281-d4c3-b3f5f6e522aa"
      },
      "source": [
        "results"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9775136215603999, 0.7902938723564148]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_g1hP_Mn4yC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "16c2448c-e54a-4789-9aca-3b8782a1a419"
      },
      "source": [
        "import copy\n",
        "test_labels_copy = copy.copy(test_labels)\n",
        "np.random.shuffle(test_labels_copy)\n",
        "hits_array = np.array(test_labels) == np.array(test_labels_copy)\n",
        "float(np.sum(hits_array)) / len(test_labels)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.19456812110418523"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZKsiv_BofhF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "37602392-dfcf-4d9c-f817-25ad682d8fd3"
      },
      "source": [
        "predictions = model.predict(x_test)\n",
        "\n",
        "predictions[0].shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(46,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ov3vjBUporPC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3f732254-9da7-43b5-a55d-11aa96932df0"
      },
      "source": [
        "np.sum(predictions[0])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oWZqlgHnotIM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "64002cd9-acdc-40d8-d642-83541e5edefa"
      },
      "source": [
        "np.argmax(predictions[0])"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmRUQSH4oxVN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Casting the labels as integer tensors instead of one-hot encoding\n",
        "\n",
        "y_train = np.array(train_labels)\n",
        "y_test = np.array(test_labels)\n",
        "\n",
        "# Use sparse_cateogrical_crossentropy in this case\n",
        "model.compile(optimizer='rmsprop',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBNsxgsZpdm-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "outputId": "d4b00d7f-44a5-460c-bd28-95f3f8949fc0"
      },
      "source": [
        "# Using hidden layers with number of hidden units lesser than that of the output layer\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))\n",
        "model.add(layers.Dense(4, activation='relu'))\n",
        "model.add(layers.Dense(46, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer='rmsprop',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['acc'])\n",
        "\n",
        "history = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=20,\n",
        "                    batch_size=512,\n",
        "                    validation_data=[x_val, y_val])"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 7982 samples, validate on 1000 samples\n",
            "Epoch 1/20\n",
            "7982/7982 [==============================] - 1s 162us/step - loss: 3.5437 - acc: 0.0833 - val_loss: 3.2039 - val_acc: 0.3360\n",
            "Epoch 2/20\n",
            "7982/7982 [==============================] - 1s 117us/step - loss: 2.9378 - acc: 0.3727 - val_loss: 2.7030 - val_acc: 0.3870\n",
            "Epoch 3/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 2.4270 - acc: 0.4747 - val_loss: 2.2887 - val_acc: 0.5210\n",
            "Epoch 4/20\n",
            "7982/7982 [==============================] - 1s 116us/step - loss: 2.0414 - acc: 0.5710 - val_loss: 2.0118 - val_acc: 0.5640\n",
            "Epoch 5/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 1.7730 - acc: 0.6067 - val_loss: 1.8414 - val_acc: 0.5690\n",
            "Epoch 6/20\n",
            "7982/7982 [==============================] - 1s 114us/step - loss: 1.5926 - acc: 0.6155 - val_loss: 1.7239 - val_acc: 0.5770\n",
            "Epoch 7/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 1.4681 - acc: 0.6189 - val_loss: 1.6516 - val_acc: 0.5870\n",
            "Epoch 8/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 1.3693 - acc: 0.6240 - val_loss: 1.5799 - val_acc: 0.5910\n",
            "Epoch 9/20\n",
            "7982/7982 [==============================] - 1s 114us/step - loss: 1.2833 - acc: 0.6343 - val_loss: 1.5489 - val_acc: 0.5880\n",
            "Epoch 10/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 1.2030 - acc: 0.6465 - val_loss: 1.4981 - val_acc: 0.6010\n",
            "Epoch 11/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 1.1292 - acc: 0.6713 - val_loss: 1.4657 - val_acc: 0.6160\n",
            "Epoch 12/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 1.0576 - acc: 0.6901 - val_loss: 1.4262 - val_acc: 0.6400\n",
            "Epoch 13/20\n",
            "7982/7982 [==============================] - 1s 116us/step - loss: 0.9948 - acc: 0.7284 - val_loss: 1.4176 - val_acc: 0.6730\n",
            "Epoch 14/20\n",
            "7982/7982 [==============================] - 1s 116us/step - loss: 0.9381 - acc: 0.7615 - val_loss: 1.3867 - val_acc: 0.6740\n",
            "Epoch 15/20\n",
            "7982/7982 [==============================] - 1s 116us/step - loss: 0.8850 - acc: 0.7694 - val_loss: 1.4275 - val_acc: 0.6630\n",
            "Epoch 16/20\n",
            "7982/7982 [==============================] - 1s 116us/step - loss: 0.8405 - acc: 0.7737 - val_loss: 1.3795 - val_acc: 0.6900\n",
            "Epoch 17/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 0.7995 - acc: 0.7844 - val_loss: 1.3753 - val_acc: 0.6950\n",
            "Epoch 18/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 0.7633 - acc: 0.8001 - val_loss: 1.3879 - val_acc: 0.6950\n",
            "Epoch 19/20\n",
            "7982/7982 [==============================] - 1s 115us/step - loss: 0.7291 - acc: 0.8103 - val_loss: 1.3912 - val_acc: 0.7000\n",
            "Epoch 20/20\n",
            "7982/7982 [==============================] - 1s 116us/step - loss: 0.6998 - acc: 0.8220 - val_loss: 1.3934 - val_acc: 0.7090\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3rq3rwfqLaj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "66340c79-8c1e-4569-f4f3-d1d08ecde8de"
      },
      "source": [
        "plt.clf()\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training Loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
        "plt.plot(epochs, acc, 'ro', label='Training Accuracy')\n",
        "plt.plot(epochs, val_acc, 'r', label='Validation Accuracy')\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUyBDoQfq0eb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}