Universal_Sentence_Encoder_using_tf_keras.ipynb

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    "colab": {
      "name": "Universal Sentence Encoder using tf.keras.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
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      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "MvwdaMOiTxHo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install tensorflow==2.0.0-alpha0\n",
        "!pip install -q tensorflow-hub\n",
        "!pip install contractions\n",
        "!pip install beautifulsoup4"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9faaqF6YqT4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import tensorflow_hub as tf_hub\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QIwV82Kyxc0O",
        "colab_type": "code",
        "outputId": "9d94c59d-933d-4a88-f821-1473667f1e39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "tf.test.is_gpu_available()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYUSjjxoZCiu",
        "colab_type": "code",
        "outputId": "44c209ce-c400-4c6f-f4de-340851791f67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "url = 'https://github.com/dipanjanS/data_science_for_all/blob/master/tds_deep_transfer_learning_nlp_classification/movie_reviews.csv.bz2?raw=true'\n",
        "dataset = pd.read_csv(url, compression='bz2')\n",
        "dataset.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>One of the other reviewers has mentioned that ...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I thought this was a wonderful way to spend ti...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Basically there's a family where a little boy ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              review sentiment\n",
              "0  One of the other reviewers has mentioned that ...  positive\n",
              "1  A wonderful little production. <br /><br />The...  positive\n",
              "2  I thought this was a wonderful way to spend ti...  positive\n",
              "3  Basically there's a family where a little boy ...  negative\n",
              "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7zulWXAZgMd",
        "colab_type": "code",
        "outputId": "5ba9e85b-662b-4085-c165-2ad5c67b073a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        }
      },
      "source": [
        "dataset.info()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50000 entries, 0 to 49999\n",
            "Data columns (total 2 columns):\n",
            "review       50000 non-null object\n",
            "sentiment    50000 non-null object\n",
            "dtypes: object(2)\n",
            "memory usage: 781.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3o5vhfZ9Ziut",
        "colab_type": "code",
        "outputId": "c235221c-0cff-4156-eba6-c5dcb8fa939c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "dataset['sentiment'] = dataset.sentiment.map({\"positive\": 1, \"negative\": 0})\n",
        "dataset.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>One of the other reviewers has mentioned that ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>I thought this was a wonderful way to spend ti...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Basically there's a family where a little boy ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                                              review  sentiment\n",
              "0  One of the other reviewers has mentioned that ...          1\n",
              "1  A wonderful little production. <br /><br />The...          1\n",
              "2  I thought this was a wonderful way to spend ti...          1\n",
              "3  Basically there's a family where a little boy ...          0\n",
              "4  Petter Mattei's \"Love in the Time of Money\" is...          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNHJkMfBZyNR",
        "colab_type": "code",
        "outputId": "164605bc-c7c2-4bbb-b1d9-ac178dad4a7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "reviews = dataset['review'].values\n",
        "sentiments = dataset['sentiment'].values\n",
        "\n",
        "train_reviews = reviews[:30000]\n",
        "train_sentiments = sentiments[:30000]\n",
        "\n",
        "val_reviews = reviews[30000:35000]\n",
        "val_sentiments = sentiments[30000:35000]\n",
        "\n",
        "test_reviews = reviews[35000:]\n",
        "test_sentiments = sentiments[35000:]\n",
        "\n",
        "train_reviews.shape, val_reviews.shape, test_reviews.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((30000,), (5000,), (15000,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dwfmMjUaF_B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import contractions\n",
        "from bs4 import BeautifulSoup\n",
        "import unicodedata\n",
        "import re\n",
        "\n",
        "def strip_html_tags(text):\n",
        "    soup = BeautifulSoup(text, \"html.parser\")\n",
        "    [s.extract() for s in soup(['iframe', 'script'])]\n",
        "    stripped_text = soup.get_text()\n",
        "    stripped_text = re.sub(r'[\\r|\\n|\\r\\n]+', '\\n', stripped_text)\n",
        "    return stripped_text\n",
        "\n",
        "def remove_accented_chars(text):\n",
        "    text = unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n",
        "    return text\n",
        "\n",
        "def expand_contractions(text):\n",
        "    return contractions.fix(text)\n",
        "\n",
        "def remove_special_characters(text, remove_digits=False):\n",
        "    pattern = r'[^a-zA-Z0-9\\s]' if not remove_digits else r'[^a-zA-Z\\s]'\n",
        "    text = re.sub(pattern, '', text)\n",
        "    return text\n",
        "\n",
        "def pre_process_document(document):\n",
        "    # strip HTML\n",
        "    document = strip_html_tags(document)\n",
        "    # lower case\n",
        "    document = document.lower()\n",
        "    # remove extra newlines (often might be present in really noisy text)\n",
        "    document = document.translate(document.maketrans(\"\\n\\t\\r\", \"   \"))\n",
        "    # remove accented characters\n",
        "    document = remove_accented_chars(document)\n",
        "    # expand contractions    \n",
        "    document = expand_contractions(document)  \n",
        "    # remove special characters and\\or digits    \n",
        "    # insert spaces between special characters to isolate them    \n",
        "    special_char_pattern = re.compile(r'([{.(-)!}])')\n",
        "    document = special_char_pattern.sub(\" \\\\1 \", document)\n",
        "    document = remove_special_characters(document, remove_digits=True)  \n",
        "    # remove extra whitespace\n",
        "    document = re.sub(' +', ' ', document)\n",
        "    document = document.strip()\n",
        "    \n",
        "    return document\n",
        "\n",
        "\n",
        "pre_process_corpus = np.vectorize(pre_process_document)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3y9H3IXaT8j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_reviews = pre_process_corpus(train_reviews)\n",
        "val_reviews = pre_process_corpus(val_reviews)\n",
        "test_reviews = pre_process_corpus(test_reviews)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kt9nq6vXaXAb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = np.asarray(train_reviews)\n",
        "X_valid = np.asarray(val_reviews)\n",
        "\n",
        "y_train = np.array(train_sentiments)\n",
        "y_valid = np.array(val_sentiments)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFdbq3FJH6O8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.compat.v1.disable_eager_execution()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GV2ux5Cbcw3W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "download_url = \"https://tfhub.dev/google/universal-sentence-encoder/2\"\n",
        "embed = tf_hub.Module(download_url, trainable=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YEd-vagbhqbD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Input, Lambda, Dense\n",
        "from tensorflow.keras.models import Model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0waG1NvhwgD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def UniversalEmbedding(x):\n",
        "    return embed(tf.squeeze(tf.cast(x, tf.string)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9G5Osw9tmD3p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_text = Input(shape=(1,), dtype=tf.string)\n",
        "embedding = Lambda(UniversalEmbedding, output_shape=(512, ))(input_text)\n",
        "dense = Dense(128, activation='relu')(embedding)\n",
        "pred = Dense(1, activation='sigmoid')(dense)\n",
        "model = Model(inputs=[input_text], outputs=pred)\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adagrad(lr=0.005)\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxncDxmUmliz",
        "colab_type": "code",
        "outputId": "694a7197-965f-43b1-a48f-12de2d20a88c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         [(None, 1)]               0         \n",
            "_________________________________________________________________\n",
            "lambda_2 (Lambda)            (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 128)               65664     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 65,793\n",
            "Trainable params: 65,793\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sn6_Z4Z8DKel",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "es_cb = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYC2ifCFmnt9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "outputId": "c351a84d-33a3-4811-8a2e-ef5d29a07e33"
      },
      "source": [
        "with tf.compat.v1.Session() as session:\n",
        "    session.run(tf.compat.v1.global_variables_initializer())  \n",
        "    session.run(tf.compat.v1.tables_initializer())\n",
        "    history = model.fit(X_train, y_train, validation_data=(X_valid, y_valid),\n",
        "                        epochs=12, batch_size=256, callbacks=[es_cb])"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 30000 samples, validate on 5000 samples\n",
            "Epoch 1/12\n",
            "30000/30000 [==============================] - 58s 2ms/sample - loss: 0.6905 - accuracy: 0.5556 - val_loss: 0.6865 - val_accuracy: 0.6384\n",
            "Epoch 2/12\n",
            "30000/30000 [==============================] - 55s 2ms/sample - loss: 0.6829 - accuracy: 0.6764 - val_loss: 0.6792 - val_accuracy: 0.7162\n",
            "Epoch 3/12\n",
            "30000/30000 [==============================] - 56s 2ms/sample - loss: 0.6755 - accuracy: 0.7353 - val_loss: 0.6718 - val_accuracy: 0.7538\n",
            "Epoch 4/12\n",
            "30000/30000 [==============================] - 56s 2ms/sample - loss: 0.6679 - accuracy: 0.7619 - val_loss: 0.6643 - val_accuracy: 0.7708\n",
            "Epoch 5/12\n",
            "30000/30000 [==============================] - 56s 2ms/sample - loss: 0.6601 - accuracy: 0.7748 - val_loss: 0.6564 - val_accuracy: 0.7774\n",
            "Epoch 6/12\n",
            "30000/30000 [==============================] - 57s 2ms/sample - loss: 0.6517 - accuracy: 0.7817 - val_loss: 0.6480 - val_accuracy: 0.7802\n",
            "Epoch 7/12\n",
            "30000/30000 [==============================] - 57s 2ms/sample - loss: 0.6427 - accuracy: 0.7867 - val_loss: 0.6389 - val_accuracy: 0.7836\n",
            "Epoch 8/12\n",
            "30000/30000 [==============================] - 57s 2ms/sample - loss: 0.6331 - accuracy: 0.7917 - val_loss: 0.6291 - val_accuracy: 0.7888\n",
            "Epoch 9/12\n",
            "30000/30000 [==============================] - 55s 2ms/sample - loss: 0.6227 - accuracy: 0.7940 - val_loss: 0.6186 - val_accuracy: 0.7900\n",
            "Epoch 10/12\n",
            "30000/30000 [==============================] - 56s 2ms/sample - loss: 0.6118 - accuracy: 0.7959 - val_loss: 0.6075 - val_accuracy: 0.7920\n",
            "Epoch 11/12\n",
            "30000/30000 [==============================] - 57s 2ms/sample - loss: 0.6004 - accuracy: 0.7977 - val_loss: 0.5962 - val_accuracy: 0.7946\n",
            "Epoch 12/12\n",
            "30000/30000 [==============================] - 56s 2ms/sample - loss: 0.5887 - accuracy: 0.7988 - val_loss: 0.5846 - val_accuracy: 0.7960\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DsG43JjDCA1V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "fde41721-5ac1-400f-d9e2-af62ae4107d6"
      },
      "source": [
        "history_dict = history.history\n",
        "history_dict.keys()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-AT3EJl7pWo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "a268d43f-bbeb-46ea-ee7d-e30fd28d07e7"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "acc = history_dict['accuracy']\n",
        "val_acc = history_dict['val_accuracy']\n",
        "loss = history_dict['loss']\n",
        "val_loss = history_dict['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "plt.figure(figsize=(10,8))\n",
        "plt.plot(epochs, loss, label='Training loss')\n",
        "plt.plot(epochs, val_loss, label='Validation loss')\n",
        "plt.plot(epochs, acc, label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, label='Validation accuracy')\n",
        "\n",
        "plt.title('Training and validation accuracy and loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy/Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Lq0kZR40aRe/evRk3bhxff/01x44dY/Pmzfz73/8GUEeTjhkzhnXr1nHs2DG+++47dfLi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6znsSy47d69m+eee45wOExpaSkLFiy46DWVlZW8/PLLGIZB//79+f73vw9ARUUFK1euBOD6669n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\n4CYiIiLSRSi4iYiIiHQR/x9g5fIftyfOHwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWp9ZsnYB-jr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189
        },
        "outputId": "03f3a1f2-c9fb-4fb7-aadd-ab0f894049b5"
      },
      "source": [
        "with tf.compat.v1.Session() as session:\n",
        "  session.run(tf.compat.v1.global_variables_initializer())\n",
        "  session.run(tf.compat.v1.tables_initializer())\n",
        "  model.evaluate(np.array(test_reviews), np.array(test_sentiments))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   96/15000 [..............................] - ETA: 2:24 - loss: 0.6910 - accuracy: 0.5208"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: <bound method BaseSession._Callable.__del__ of <tensorflow.python.client.session.BaseSession._Callable object at 0x7ff7f98a5cc0>>\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\", line 1456, in __del__\n",
            "    self._session._session, self._handle, status)\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/errors_impl.py\", line 548, in __exit__\n",
            "    c_api.TF_GetCode(self.status.status))\n",
            "tensorflow.python.framework.errors_impl.CancelledError: Session has been closed.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "15000/15000 [==============================] - 26s 2ms/sample - loss: 0.6923 - accuracy: 0.5186\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gLsj_IoICSLk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "outputId": "d8b9cd75-8931-4cd3-9d6c-5317fa023eda"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "with tf.compat.v1.Session() as session:\n",
        "  session.run(tf.compat.v1.global_variables_initializer())\n",
        "  session.run(tf.compat.v1.tables_initializer())\n",
        "  y_pred = model.predict(np.array(test_reviews))\n",
        "\n",
        "print(classification_report(np.array(test_sentiments), np.argmax(y_pred, axis=1)))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.50      1.00      0.67      7490\n",
            "           1       0.00      0.00      0.00      7510\n",
            "\n",
            "   micro avg       0.50      0.50      0.50     15000\n",
            "   macro avg       0.25      0.50      0.33     15000\n",
            "weighted avg       0.25      0.50      0.33     15000\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}