C3W4_1_Shakespeare_Tokenizer_Embedding_LSTM_Dropouts_Regularizers.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "C3W4-1-Shakespeare-Tokenizer-Embedding-LSTM-Dropouts-Regularizers",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/georgezoto/TensorFlow-in-Practice/blob/master/C3W4_1_Shakespeare_Tokenizer_Embedding_LSTM_Dropouts_Regularizers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zX4Kg8DUTKWO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BOwsuGQQY9OL",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout, Bidirectional\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "### YOUR CODE HERE\n",
        "from tensorflow.keras import regularizers\n",
        "###\n",
        "import tensorflow.keras.utils as ku \n",
        "import numpy as np "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PRnDnCW-Z7qv",
        "outputId": "4e7d9f43-f277-4879-9e63-c41bc5b318b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "tokenizer = Tokenizer()\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/sonnets.txt \\\n",
        "    -O /tmp/sonnets.txt"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-24 16:54:47--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/sonnets.txt\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.14.112, 2607:f8b0:4007:802::2010\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.14.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 93578 (91K) [text/plain]\n",
            "Saving to: ‘/tmp/sonnets.txt’\n",
            "\n",
            "\r/tmp/sonnets.txt      0%[                    ]       0  --.-KB/s               \r/tmp/sonnets.txt    100%[===================>]  91.38K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2020-04-24 16:54:47 (152 MB/s) - ‘/tmp/sonnets.txt’ saved [93578/93578]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBvxloSRD2qb",
        "colab_type": "code",
        "outputId": "ead295fe-7bb6-48e8-874f-35b93dd9edd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data = open('/tmp/sonnets.txt').read()\n",
        "\n",
        "corpus = data.lower().split(\"\\n\")\n",
        "print(corpus[:3])\n",
        "\n",
        "tokenizer.fit_on_texts(corpus)\n",
        "total_words = len(tokenizer.word_index) + 1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['from fairest creatures we desire increase,', \"that thereby beauty's rose might never die,\", 'but as the riper should by time decease,']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4I_YrBNlD67D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create input sequences using list of tokens\n",
        "input_sequences = []\n",
        "for line in corpus:\n",
        "  #print([line])\n",
        "  #print(tokenizer.texts_to_sequences([line]))\n",
        "  #print(tokenizer.texts_to_sequences([line])[0])\n",
        "  #break\n",
        "  token_list = tokenizer.texts_to_sequences([line])[0]\n",
        "  for i in range(1, len(token_list)):\n",
        "    n_gram_sequence = token_list[:i+1]\n",
        "    input_sequences.append(n_gram_sequence)\n",
        "\n",
        "\n",
        "# pad sequences \n",
        "max_sequence_len = max([len(x) for x in input_sequences])\n",
        "input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))\n",
        "\n",
        "# create predictors and label\n",
        "predictors, label = input_sequences[:,:-1],input_sequences[:,-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1lk080yTD69L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#https://keras.io/api/utils/python_utils/#to_categorical-function\n",
        "label = ku.to_categorical(label, num_classes=total_words)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wlIcZj3MEtwy",
        "colab_type": "code",
        "outputId": "0dfc1474-c88b-42e8-e0f9-a350b9533d69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "label[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWZu2wFPEoPd",
        "colab_type": "code",
        "outputId": "6d5bf389-79e1-4eff-c471-409da3c97922",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(label[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3211"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-FPM71JFnRZ",
        "colab_type": "code",
        "outputId": "72f6ef8a-637c-4c0c-dd71-0c88c0bace36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "total_words"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3211"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "w9vH8Y59ajYL",
        "outputId": "ec69f1e6-f7d8-430d-a410-010a2d479ae6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(input_dim=total_words, output_dim=100, input_length=max_sequence_len-1))\n",
        "model.add(Bidirectional(LSTM(150, return_sequences=True)))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(100))\n",
        "model.add(Dense(total_words/2, activation='relu', kernel_regularizer=regularizers.l2(0.01)))\n",
        "model.add(Dense(total_words, activation='softmax'))\n",
        "\n",
        "my_optimizer = Adam(learning_rate=0.001)\n",
        "model.compile(loss='categorical_crossentropy', optimizer=my_optimizer, metrics=['accuracy'])\n",
        "\n",
        "print(model.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_3 (Embedding)      (None, 10, 100)           321100    \n",
            "_________________________________________________________________\n",
            "bidirectional_6 (Bidirection (None, 10, 300)           301200    \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 10, 300)           0         \n",
            "_________________________________________________________________\n",
            "lstm_7 (LSTM)                (None, 100)               160400    \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1605)              162105    \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 3211)              5156866   \n",
            "=================================================================\n",
            "Total params: 6,101,671\n",
            "Trainable params: 6,101,671\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AIg2f1HBxqof",
        "outputId": "32dad8da-a475-444b-dc21-a5934258dcb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = model.fit(predictors, label, epochs=100, verbose=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 5.6682 - accuracy: 0.0564\n",
            "Epoch 2/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 5.5644 - accuracy: 0.0653\n",
            "Epoch 3/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 5.4746 - accuracy: 0.0687\n",
            "Epoch 4/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 5.3714 - accuracy: 0.0761\n",
            "Epoch 5/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 5.2699 - accuracy: 0.0823\n",
            "Epoch 6/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 5.1707 - accuracy: 0.0894\n",
            "Epoch 7/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 5.0740 - accuracy: 0.1002\n",
            "Epoch 8/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 4.9721 - accuracy: 0.1068\n",
            "Epoch 9/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 4.8807 - accuracy: 0.1138\n",
            "Epoch 10/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 4.7824 - accuracy: 0.1260\n",
            "Epoch 11/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 4.6837 - accuracy: 0.1360\n",
            "Epoch 12/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 4.5882 - accuracy: 0.1440\n",
            "Epoch 13/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 4.4890 - accuracy: 0.1555\n",
            "Epoch 14/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 4.3745 - accuracy: 0.1693\n",
            "Epoch 15/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 4.2824 - accuracy: 0.1785\n",
            "Epoch 16/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 4.1904 - accuracy: 0.1931\n",
            "Epoch 17/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 4.0830 - accuracy: 0.2051\n",
            "Epoch 18/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.9772 - accuracy: 0.2203\n",
            "Epoch 19/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.8847 - accuracy: 0.2354\n",
            "Epoch 20/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.7842 - accuracy: 0.2521\n",
            "Epoch 21/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.6994 - accuracy: 0.2668\n",
            "Epoch 22/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.5931 - accuracy: 0.2868\n",
            "Epoch 23/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.5113 - accuracy: 0.3035\n",
            "Epoch 24/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.4228 - accuracy: 0.3202\n",
            "Epoch 25/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.3420 - accuracy: 0.3378\n",
            "Epoch 26/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.2673 - accuracy: 0.3569\n",
            "Epoch 27/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.1884 - accuracy: 0.3732\n",
            "Epoch 28/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.1033 - accuracy: 0.3904\n",
            "Epoch 29/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 3.0384 - accuracy: 0.4045\n",
            "Epoch 30/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.9588 - accuracy: 0.4251\n",
            "Epoch 31/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.8917 - accuracy: 0.4399\n",
            "Epoch 32/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.8310 - accuracy: 0.4532\n",
            "Epoch 33/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.7758 - accuracy: 0.4635\n",
            "Epoch 34/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.7130 - accuracy: 0.4807\n",
            "Epoch 35/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.6554 - accuracy: 0.4924\n",
            "Epoch 36/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.5997 - accuracy: 0.5082\n",
            "Epoch 37/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.5421 - accuracy: 0.5151\n",
            "Epoch 38/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.4914 - accuracy: 0.5285\n",
            "Epoch 39/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.4422 - accuracy: 0.5413\n",
            "Epoch 40/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.3856 - accuracy: 0.5485\n",
            "Epoch 41/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.3452 - accuracy: 0.5623\n",
            "Epoch 42/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.2964 - accuracy: 0.5711\n",
            "Epoch 43/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.2496 - accuracy: 0.5814\n",
            "Epoch 44/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.2215 - accuracy: 0.5891\n",
            "Epoch 45/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.1712 - accuracy: 0.6034\n",
            "Epoch 46/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.1210 - accuracy: 0.6101\n",
            "Epoch 47/100\n",
            "484/484 [==============================] - 6s 13ms/step - loss: 2.0850 - accuracy: 0.6191\n",
            "Epoch 48/100\n",
            "484/484 [==============================] - 6s 13ms/step - loss: 2.0475 - accuracy: 0.6285\n",
            "Epoch 49/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 2.0123 - accuracy: 0.6348\n",
            "Epoch 50/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.9768 - accuracy: 0.6444\n",
            "Epoch 51/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.9433 - accuracy: 0.6499\n",
            "Epoch 52/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.9172 - accuracy: 0.6550\n",
            "Epoch 53/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.8831 - accuracy: 0.6620\n",
            "Epoch 54/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.8411 - accuracy: 0.6734\n",
            "Epoch 55/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.8184 - accuracy: 0.6770\n",
            "Epoch 56/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.7982 - accuracy: 0.6793\n",
            "Epoch 57/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.7684 - accuracy: 0.6883\n",
            "Epoch 58/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.7396 - accuracy: 0.6907\n",
            "Epoch 59/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.7080 - accuracy: 0.6980\n",
            "Epoch 60/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.6925 - accuracy: 0.7027\n",
            "Epoch 61/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.6774 - accuracy: 0.7037\n",
            "Epoch 62/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.6312 - accuracy: 0.7144\n",
            "Epoch 63/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.6200 - accuracy: 0.7167\n",
            "Epoch 64/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.6055 - accuracy: 0.7174\n",
            "Epoch 65/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.5676 - accuracy: 0.7253\n",
            "Epoch 66/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.5443 - accuracy: 0.7340\n",
            "Epoch 67/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.5164 - accuracy: 0.7393\n",
            "Epoch 68/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.4968 - accuracy: 0.7421\n",
            "Epoch 69/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.4979 - accuracy: 0.7391\n",
            "Epoch 70/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.4890 - accuracy: 0.7387\n",
            "Epoch 71/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.4622 - accuracy: 0.7444\n",
            "Epoch 72/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.4361 - accuracy: 0.7477\n",
            "Epoch 73/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.4230 - accuracy: 0.7529\n",
            "Epoch 74/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.4118 - accuracy: 0.7566\n",
            "Epoch 75/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.3859 - accuracy: 0.7623\n",
            "Epoch 76/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.3740 - accuracy: 0.7625\n",
            "Epoch 77/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.3673 - accuracy: 0.7599\n",
            "Epoch 78/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.3408 - accuracy: 0.7698\n",
            "Epoch 79/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.3303 - accuracy: 0.7698\n",
            "Epoch 80/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.3200 - accuracy: 0.7733\n",
            "Epoch 81/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.3032 - accuracy: 0.7754\n",
            "Epoch 82/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.2901 - accuracy: 0.7752\n",
            "Epoch 83/100\n",
            "484/484 [==============================] - 6s 13ms/step - loss: 1.2814 - accuracy: 0.7781\n",
            "Epoch 84/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.2731 - accuracy: 0.7780\n",
            "Epoch 85/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.2693 - accuracy: 0.7777\n",
            "Epoch 86/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.2523 - accuracy: 0.7818\n",
            "Epoch 87/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.2257 - accuracy: 0.7883\n",
            "Epoch 88/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.2077 - accuracy: 0.7912\n",
            "Epoch 89/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.2114 - accuracy: 0.7911\n",
            "Epoch 90/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.2119 - accuracy: 0.7883\n",
            "Epoch 91/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.1963 - accuracy: 0.7884\n",
            "Epoch 92/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.1909 - accuracy: 0.7942\n",
            "Epoch 93/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.1948 - accuracy: 0.7881\n",
            "Epoch 94/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.1661 - accuracy: 0.7971\n",
            "Epoch 95/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.1476 - accuracy: 0.7974\n",
            "Epoch 96/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.1390 - accuracy: 0.7992\n",
            "Epoch 97/100\n",
            "484/484 [==============================] - 6s 12ms/step - loss: 1.1367 - accuracy: 0.7984\n",
            "Epoch 98/100\n",
            "484/484 [==============================] - 6s 13ms/step - loss: 1.1323 - accuracy: 0.8000\n",
            "Epoch 99/100\n",
            "484/484 [==============================] - 6s 13ms/step - loss: 1.1167 - accuracy: 0.7999\n",
            "Epoch 100/100\n",
            "484/484 [==============================] - 6s 13ms/step - loss: 1.1205 - accuracy: 0.8018\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1fXTEO3GJ282",
        "outputId": "fd56354e-9a0b-4874-9774-d369a4b6cfce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['accuracy']\n",
        "loss = history.history['loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'b', label='Training accuracy')\n",
        "plt.title('Training accuracy')\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, loss, 'b', label='Training Loss')\n",
        "plt.title('Training loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "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"
          }
        },
        {
          "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": {
        "colab_type": "code",
        "id": "6Vc6PHgxa6Hm",
        "outputId": "1145f9f0-eb82-44b1-8349-6a1bbaa43757",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "seed_text = \"Help me Obi Wan Kenobi, you're my only hope\"\n",
        "next_words = 100\n",
        "  \n",
        "for _ in range(next_words):\n",
        "\ttoken_list = tokenizer.texts_to_sequences([seed_text])[0]\n",
        "\ttoken_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')\n",
        "\tpredicted = model.predict_classes(token_list, verbose=0)\n",
        "\toutput_word = \"\"\n",
        "\tfor word, index in tokenizer.word_index.items():\n",
        "\t\tif index == predicted:\n",
        "\t\t\toutput_word = word\n",
        "\t\t\tbreak\n",
        "\tseed_text += \" \" + output_word\n",
        "print(seed_text)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help me Obi Wan Kenobi, you're my only hope thyself away desired be cross afford twain date of men ' doth groan ' be free free live away away your days here brought of one one more must grow of free ride hits green thee burn pleasure pleasure me of days much brought of pleasure pleasure of pleasure green die burn'd hits dwells beard ward fall by days another rolling ' doth ride ride ride ride ' her view free grew to ride cherish plight grow thus days did tell away away night exchanged mad so meant free his senses can alive pleasure ride ride light decease translate must\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oliB864cIlGA",
        "colab_type": "code",
        "outputId": "2102ede6-8362-4182-9f10-7d227c833c30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "\"Help me Obi Wan Kenobi, you're my only hope of every pen shows know confounds thine thine eye aside bright than gently cheeks express'd place in mind keep something new hate of die much date cheeks day was me away the rhyme ' may be bevel disdain erred light light stand bright ' write me flowers so look me doth grow ill old old glory still say thee but now come away a gainer his part torn torn away away ill young new date lies be express'd rare rare ill ill near feast so 'will ' hast me well seen live up memory prove keep invention spent kind check\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Help me Obi Wan Kenobi, you're my only hope of every pen shows know confounds thine thine eye aside bright than gently cheeks express'd place in mind keep something new hate of die much date cheeks day was me away the rhyme ' may be bevel disdain erred light light stand bright ' write me flowers so look me doth grow ill old old glory still say thee but now come away a gainer his part torn torn away away ill young new date lies be express'd rare rare ill ill near feast so 'will ' hast me well seen live up memory prove keep invention spent kind check\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zYWmvGg9OHmp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}