imdb.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "imdb.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/karthikapai/deep-learning/blob/master/imdb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "WV_cLLB8nCDn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "d6573e81-0cf5-478e-afc9-5dd2dc334501"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "tf.test.gpu_device_name()\n",
        "\n",
        "from tensorflow.python.client import device_lib\n",
        "device_lib.list_local_devices()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[name: \"/device:CPU:0\"\n",
              " device_type: \"CPU\"\n",
              " memory_limit: 268435456\n",
              " locality {\n",
              " }\n",
              " incarnation: 987823615315386585, name: \"/device:GPU:0\"\n",
              " device_type: \"GPU\"\n",
              " memory_limit: 11281553818\n",
              " locality {\n",
              "   bus_id: 1\n",
              "   links {\n",
              "   }\n",
              " }\n",
              " incarnation: 4888314916312783036\n",
              " physical_device_desc: \"device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7\"]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "fSMJUnq1oSB4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "\n",
        "from keras.datasets import imdb\n",
        "\n",
        "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xikSPL0PJRDn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3723
        },
        "outputId": "b7ddf00f-79bb-4e45-afa7-29e80c7c0823"
      },
      "cell_type": "code",
      "source": [
        "train_data[0]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1,\n",
              " 14,\n",
              " 22,\n",
              " 16,\n",
              " 43,\n",
              " 530,\n",
              " 973,\n",
              " 1622,\n",
              " 1385,\n",
              " 65,\n",
              " 458,\n",
              " 4468,\n",
              " 66,\n",
              " 3941,\n",
              " 4,\n",
              " 173,\n",
              " 36,\n",
              " 256,\n",
              " 5,\n",
              " 25,\n",
              " 100,\n",
              " 43,\n",
              " 838,\n",
              " 112,\n",
              " 50,\n",
              " 670,\n",
              " 2,\n",
              " 9,\n",
              " 35,\n",
              " 480,\n",
              " 284,\n",
              " 5,\n",
              " 150,\n",
              " 4,\n",
              " 172,\n",
              " 112,\n",
              " 167,\n",
              " 2,\n",
              " 336,\n",
              " 385,\n",
              " 39,\n",
              " 4,\n",
              " 172,\n",
              " 4536,\n",
              " 1111,\n",
              " 17,\n",
              " 546,\n",
              " 38,\n",
              " 13,\n",
              " 447,\n",
              " 4,\n",
              " 192,\n",
              " 50,\n",
              " 16,\n",
              " 6,\n",
              " 147,\n",
              " 2025,\n",
              " 19,\n",
              " 14,\n",
              " 22,\n",
              " 4,\n",
              " 1920,\n",
              " 4613,\n",
              " 469,\n",
              " 4,\n",
              " 22,\n",
              " 71,\n",
              " 87,\n",
              " 12,\n",
              " 16,\n",
              " 43,\n",
              " 530,\n",
              " 38,\n",
              " 76,\n",
              " 15,\n",
              " 13,\n",
              " 1247,\n",
              " 4,\n",
              " 22,\n",
              " 17,\n",
              " 515,\n",
              " 17,\n",
              " 12,\n",
              " 16,\n",
              " 626,\n",
              " 18,\n",
              " 2,\n",
              " 5,\n",
              " 62,\n",
              " 386,\n",
              " 12,\n",
              " 8,\n",
              " 316,\n",
              " 8,\n",
              " 106,\n",
              " 5,\n",
              " 4,\n",
              " 2223,\n",
              " 5244,\n",
              " 16,\n",
              " 480,\n",
              " 66,\n",
              " 3785,\n",
              " 33,\n",
              " 4,\n",
              " 130,\n",
              " 12,\n",
              " 16,\n",
              " 38,\n",
              " 619,\n",
              " 5,\n",
              " 25,\n",
              " 124,\n",
              " 51,\n",
              " 36,\n",
              " 135,\n",
              " 48,\n",
              " 25,\n",
              " 1415,\n",
              " 33,\n",
              " 6,\n",
              " 22,\n",
              " 12,\n",
              " 215,\n",
              " 28,\n",
              " 77,\n",
              " 52,\n",
              " 5,\n",
              " 14,\n",
              " 407,\n",
              " 16,\n",
              " 82,\n",
              " 2,\n",
              " 8,\n",
              " 4,\n",
              " 107,\n",
              " 117,\n",
              " 5952,\n",
              " 15,\n",
              " 256,\n",
              " 4,\n",
              " 2,\n",
              " 7,\n",
              " 3766,\n",
              " 5,\n",
              " 723,\n",
              " 36,\n",
              " 71,\n",
              " 43,\n",
              " 530,\n",
              " 476,\n",
              " 26,\n",
              " 400,\n",
              " 317,\n",
              " 46,\n",
              " 7,\n",
              " 4,\n",
              " 2,\n",
              " 1029,\n",
              " 13,\n",
              " 104,\n",
              " 88,\n",
              " 4,\n",
              " 381,\n",
              " 15,\n",
              " 297,\n",
              " 98,\n",
              " 32,\n",
              " 2071,\n",
              " 56,\n",
              " 26,\n",
              " 141,\n",
              " 6,\n",
              " 194,\n",
              " 7486,\n",
              " 18,\n",
              " 4,\n",
              " 226,\n",
              " 22,\n",
              " 21,\n",
              " 134,\n",
              " 476,\n",
              " 26,\n",
              " 480,\n",
              " 5,\n",
              " 144,\n",
              " 30,\n",
              " 5535,\n",
              " 18,\n",
              " 51,\n",
              " 36,\n",
              " 28,\n",
              " 224,\n",
              " 92,\n",
              " 25,\n",
              " 104,\n",
              " 4,\n",
              " 226,\n",
              " 65,\n",
              " 16,\n",
              " 38,\n",
              " 1334,\n",
              " 88,\n",
              " 12,\n",
              " 16,\n",
              " 283,\n",
              " 5,\n",
              " 16,\n",
              " 4472,\n",
              " 113,\n",
              " 103,\n",
              " 32,\n",
              " 15,\n",
              " 16,\n",
              " 5345,\n",
              " 19,\n",
              " 178,\n",
              " 32]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "0sgyg8ytJWF8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6aef0c36-d517-4ff8-adc3-95da616cbfb8"
      },
      "cell_type": "code",
      "source": [
        "train_labels[0]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "6bl7Tm08JZfp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e954ee27-e9da-4bb8-b79a-27a623e241c1"
      },
      "cell_type": "code",
      "source": [
        "# word_index is a dictionary mapping words to an integer index\n",
        "word_index = imdb.get_word_index()\n",
        "# We reverse it, mapping integer indices to words\n",
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
        "# We decode the review; note that our indices were offset by 3\n",
        "# because 0, 1 and 2 are reserved indices for \"padding\", \"start of sequence\", and \"unknown\".\n",
        "decoded_review = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://s3.amazonaws.com/text-datasets/imdb_word_index.json\n",
            "1646592/1641221 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5KuTZV8SJZ7x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "cab4e2f9-9f13-4877-cb75-a5dd40ade2ac"
      },
      "cell_type": "code",
      "source": [
        "decoded_review"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "metadata": {
        "id": "iYWuxcs0JaGR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def vectorize_sequences(sequences, dimension=10000):\n",
        "    # Create an all-zero matrix of shape (len(sequences), dimension)\n",
        "    results = np.zeros((len(sequences), dimension))\n",
        "    for i, sequence in enumerate(sequences):\n",
        "        results[i, sequence] = 1.  # set specific indices of results[i] to 1s\n",
        "    return results\n",
        "\n",
        "# Our vectorized training data\n",
        "x_train = vectorize_sequences(train_data)\n",
        "# Our vectorized test data\n",
        "x_test = vectorize_sequences(test_data)\n",
        "\n",
        "# Our vectorized labels\n",
        "y_train = np.asarray(train_labels).astype('float32')\n",
        "y_test = np.asarray(test_labels).astype('float32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Z_U4xE17J4W3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))\n",
        "model.add(layers.Dense(16, activation='relu'))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QKirsmKeJ8l4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XZY_TpR6J_8O",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras import optimizers\n",
        "\n",
        "model.compile(optimizer=optimizers.RMSprop(lr=0.001),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3s47dzOdKAE-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras import losses\n",
        "from keras import metrics\n",
        "\n",
        "model.compile(optimizer=optimizers.RMSprop(lr=0.001),\n",
        "              loss=losses.binary_crossentropy,\n",
        "              metrics=[metrics.binary_accuracy])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3K1JGPWpKEHk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x_val = x_train[:10000]\n",
        "partial_x_train = x_train[10000:]\n",
        "\n",
        "y_val = y_train[:10000]\n",
        "partial_y_train = y_train[10000:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hrW3BUo8KJfm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "e3ddd60c-63a2-4669-8fbc-d5118a0c526c"
      },
      "cell_type": "code",
      "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": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 15000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "15000/15000 [==============================] - 3s 199us/step - loss: 0.5344 - binary_accuracy: 0.7746 - val_loss: 0.3943 - val_binary_accuracy: 0.8687\n",
            "Epoch 2/20\n",
            "15000/15000 [==============================] - 2s 131us/step - loss: 0.3181 - binary_accuracy: 0.8973 - val_loss: 0.3277 - val_binary_accuracy: 0.8713\n",
            "Epoch 3/20\n",
            "15000/15000 [==============================] - 2s 127us/step - loss: 0.2343 - binary_accuracy: 0.9245 - val_loss: 0.2862 - val_binary_accuracy: 0.8881\n",
            "Epoch 4/20\n",
            "15000/15000 [==============================] - 2s 129us/step - loss: 0.1844 - binary_accuracy: 0.9400 - val_loss: 0.2745 - val_binary_accuracy: 0.8905\n",
            "Epoch 5/20\n",
            "15000/15000 [==============================] - 2s 128us/step - loss: 0.1526 - binary_accuracy: 0.9509 - val_loss: 0.2996 - val_binary_accuracy: 0.8806\n",
            "Epoch 6/20\n",
            "15000/15000 [==============================] - 2s 130us/step - loss: 0.1225 - binary_accuracy: 0.9625 - val_loss: 0.2882 - val_binary_accuracy: 0.8865\n",
            "Epoch 7/20\n",
            "15000/15000 [==============================] - 2s 132us/step - loss: 0.1070 - binary_accuracy: 0.9658 - val_loss: 0.3037 - val_binary_accuracy: 0.8846\n",
            "Epoch 8/20\n",
            "15000/15000 [==============================] - 2s 135us/step - loss: 0.0854 - binary_accuracy: 0.9753 - val_loss: 0.3229 - val_binary_accuracy: 0.8810\n",
            "Epoch 9/20\n",
            "15000/15000 [==============================] - 2s 134us/step - loss: 0.0689 - binary_accuracy: 0.9825 - val_loss: 0.3462 - val_binary_accuracy: 0.8802\n",
            "Epoch 10/20\n",
            "15000/15000 [==============================] - 2s 134us/step - loss: 0.0576 - binary_accuracy: 0.9860 - val_loss: 0.4012 - val_binary_accuracy: 0.8718\n",
            "Epoch 11/20\n",
            "15000/15000 [==============================] - 2s 133us/step - loss: 0.0465 - binary_accuracy: 0.9901 - val_loss: 0.3947 - val_binary_accuracy: 0.8750\n",
            "Epoch 12/20\n",
            "15000/15000 [==============================] - 2s 134us/step - loss: 0.0373 - binary_accuracy: 0.9928 - val_loss: 0.4176 - val_binary_accuracy: 0.8745\n",
            "Epoch 13/20\n",
            "15000/15000 [==============================] - 2s 136us/step - loss: 0.0311 - binary_accuracy: 0.9944 - val_loss: 0.4966 - val_binary_accuracy: 0.8690\n",
            "Epoch 14/20\n",
            "15000/15000 [==============================] - 2s 134us/step - loss: 0.0245 - binary_accuracy: 0.9953 - val_loss: 0.4864 - val_binary_accuracy: 0.8737\n",
            "Epoch 15/20\n",
            "15000/15000 [==============================] - 2s 128us/step - loss: 0.0160 - binary_accuracy: 0.9978 - val_loss: 0.5753 - val_binary_accuracy: 0.8543\n",
            "Epoch 16/20\n",
            "15000/15000 [==============================] - 2s 128us/step - loss: 0.0161 - binary_accuracy: 0.9980 - val_loss: 0.5391 - val_binary_accuracy: 0.8695\n",
            "Epoch 17/20\n",
            "15000/15000 [==============================] - 2s 129us/step - loss: 0.0102 - binary_accuracy: 0.9990 - val_loss: 0.5733 - val_binary_accuracy: 0.8699\n",
            "Epoch 18/20\n",
            "15000/15000 [==============================] - 2s 131us/step - loss: 0.0108 - binary_accuracy: 0.9986 - val_loss: 0.6012 - val_binary_accuracy: 0.8670\n",
            "Epoch 19/20\n",
            "15000/15000 [==============================] - 2s 130us/step - loss: 0.0051 - binary_accuracy: 0.9998 - val_loss: 0.6319 - val_binary_accuracy: 0.8661\n",
            "Epoch 20/20\n",
            "15000/15000 [==============================] - 2s 128us/step - loss: 0.0074 - binary_accuracy: 0.9985 - val_loss: 0.6599 - val_binary_accuracy: 0.8672\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SesR77LcKdEu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0404ede9-bd49-4855-c12a-425d87dcfd27"
      },
      "cell_type": "code",
      "source": [
        "history_dict = history.history\n",
        "history_dict.keys()\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['val_loss', 'val_binary_accuracy', 'loss', 'binary_accuracy'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "8nYJekTWKq7u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "fe8107f6-47d0-4cc0-e99e-30fc61376e4b"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "acc = history.history['binary_accuracy']\n",
        "val_acc = history.history['val_binary_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "# \"bo\" is for \"blue dot\"\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "# b is for \"solid blue line\"\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFnCAYAAACPasF4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f54ab1fea58>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Hc4tnlypK-kR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "673f8bdf-96af-4595-a026-4a81e91cd156"
      },
      "cell_type": "code",
      "source": [
        "plt.clf()   # clear figure\n",
        "acc_values = history_dict['binary_accuracy']\n",
        "val_acc_values = history_dict['val_binary_accuracy']\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFnCAYAAAC/5tBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhKZsx2IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f54ab1fe6a0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "L45RIEUiLI4V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "40bee8bf-4b43-4f4b-988c-0093b46f77d7"
      },
      "cell_type": "code",
      "source": [
        "model = models.Sequential()\n",
        "model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))\n",
        "model.add(layers.Dense(16, activation='relu'))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer='rmsprop',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train, y_train, epochs=4, batch_size=512)\n",
        "results = model.evaluate(x_test, y_test)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/4\n",
            "25000/25000 [==============================] - 2s 94us/step - loss: 0.4749 - acc: 0.8217\n",
            "Epoch 2/4\n",
            "25000/25000 [==============================] - 2s 81us/step - loss: 0.2666 - acc: 0.9096\n",
            "Epoch 3/4\n",
            "25000/25000 [==============================] - 2s 82us/step - loss: 0.1987 - acc: 0.9292\n",
            "Epoch 4/4\n",
            "25000/25000 [==============================] - 2s 82us/step - loss: 0.1679 - acc: 0.9402\n",
            "25000/25000 [==============================] - 3s 124us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "s3UPUYuwLLxY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e1a04332-7751-4b02-8810-0d1c98aaa698"
      },
      "cell_type": "code",
      "source": [
        "results\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.32385540641784666, 0.87308]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "Q4ecanVNLR0c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "b3ecf187-44db-4738-f30e-030c12b5135b"
      },
      "cell_type": "code",
      "source": [
        "model.predict(x_test)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.1356007 ],\n",
              "       [0.9997118 ],\n",
              "       [0.27816316],\n",
              "       ...,\n",
              "       [0.07139969],\n",
              "       [0.04283794],\n",
              "       [0.47383875]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    }
  ]
}