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      },
      "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."
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          "height": 34
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        "outputId": "af14ebeb-2599-40e6-92b5-529f1c5f1c1e"
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      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "# !pip install -q tensorflow-datasets"
      ],
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      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0\n"
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        }
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          ]
        },
        "outputId": "316d27eb-addf-402e-fd45-7bffd38e2cc8"
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "imdb, info = tfds.load(\"imdb_reviews\", with_info=True, as_supervised=True)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset imdb_reviews/plain_text/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6915aa7729744e3bb748f62fd2aec168",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "01dfcc4b4fdc4ba3a764e0b7f716dfb0",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2ef0bbcee0a4499989665022725da24b",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteC5XTYF/imdb_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "bc761daccad14759ad98848975596a7c",
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              "HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "943de42e101245aabd1fe1ae46f46ca1",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteC5XTYF/imdb_reviews-test.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "046c57a07c1b4dc4a640aea93289f798",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "557358669904471697d77f7745271b04",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteC5XTYF/imdb_reviews-unsupervised.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bf935d06c05f478aaa76cb5566d51491",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=50000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
            "\r"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wHQ2Ko0zl7M4",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "train_data, test_data = imdb['train'], imdb['test']\n",
        "\n",
        "training_sentences = []\n",
        "training_labels = []\n",
        "\n",
        "testing_sentences = []\n",
        "testing_labels = []\n",
        "\n",
        "# str(s.tonumpy()) is needed in Python3 instead of just s.numpy()\n",
        "for s,l in train_data:\n",
        "  training_sentences.append(str(s.numpy()))\n",
        "  training_labels.append(l.numpy())\n",
        "  \n",
        "for s,l in test_data:\n",
        "  testing_sentences.append(str(s.numpy()))\n",
        "  testing_labels.append(l.numpy())\n",
        "  \n",
        "training_labels_final = np.array(training_labels)\n",
        "testing_labels_final = np.array(testing_labels)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7n15yyMdmoH1",
        "colab": {}
      },
      "source": [
        "vocab_size = 10000\n",
        "embedding_dim = 16\n",
        "max_length = 120\n",
        "trunc_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(training_sentences)\n",
        "word_index = tokenizer.word_index\n",
        "sequences = tokenizer.texts_to_sequences(training_sentences)\n",
        "padded = pad_sequences(sequences,maxlen=max_length, truncating=trunc_type)\n",
        "\n",
        "testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n",
        "testing_padded = pad_sequences(testing_sequences,maxlen=max_length)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9axf0uIXVMhO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "74d170cf-182e-4dba-a804-1d92e5a7f6f2"
      },
      "source": [
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
        "\n",
        "def decode_review(text):\n",
        "    return ' '.join([reverse_word_index.get(i, '?') for i in text])\n",
        "\n",
        "print(decode_review(padded[1]))\n",
        "print(training_sentences[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "? ? ? ? ? ? ? b'i have been known to fall asleep during films but this is usually due to a combination of things including really tired being warm and comfortable on the <OOV> and having just eaten a lot however on this occasion i fell asleep because the film was rubbish the plot development was constant constantly slow and boring things seemed to happen but with no explanation of what was causing them or why i admit i may have missed part of the film but i watched the majority of it and everything just seemed to happen of its own <OOV> without any real concern for anything else i cant recommend this film at all '\n",
            "b'I have been known to fall asleep during films, but this is usually due to a combination of things including, really tired, being warm and comfortable on the sette and having just eaten a lot. However on this occasion I fell asleep because the film was rubbish. The plot development was constant. Constantly slow and boring. Things seemed to happen, but with no explanation of what was causing them or why. I admit, I may have missed part of the film, but i watched the majority of it and everything just seemed to happen of its own accord without any real concern for anything else. I cant recommend this film at all.'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5NEpdhb8AxID",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "7f22c26e-80b5-45b4-e026-d7a750e92a51"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.GRU(32)),\n",
        "    tf.keras.layers.Dense(6, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 120, 16)           160000    \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 64)                9600      \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 6)                 390       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 169,997\n",
            "Trainable params: 169,997\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "V5LLrXC-uNX6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4e48dbb4-ca85-402b-b46b-b5414da37a9c"
      },
      "source": [
        "num_epochs = 50\n",
        "history = model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "782/782 [==============================] - 16s 20ms/step - loss: 0.5884 - accuracy: 0.6568 - val_loss: 0.3976 - val_accuracy: 0.8284\n",
            "Epoch 2/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.3322 - accuracy: 0.8580 - val_loss: 0.3558 - val_accuracy: 0.8424\n",
            "Epoch 3/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 0.2519 - accuracy: 0.9025 - val_loss: 0.3840 - val_accuracy: 0.8378\n",
            "Epoch 4/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.2028 - accuracy: 0.9252 - val_loss: 0.4342 - val_accuracy: 0.8206\n",
            "Epoch 5/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 0.1611 - accuracy: 0.9414 - val_loss: 0.4471 - val_accuracy: 0.8255\n",
            "Epoch 6/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.1234 - accuracy: 0.9571 - val_loss: 0.4892 - val_accuracy: 0.8174\n",
            "Epoch 7/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0898 - accuracy: 0.9698 - val_loss: 0.6034 - val_accuracy: 0.8143\n",
            "Epoch 8/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 0.0628 - accuracy: 0.9802 - val_loss: 0.6995 - val_accuracy: 0.8158\n",
            "Epoch 9/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 0.0467 - accuracy: 0.9853 - val_loss: 0.7711 - val_accuracy: 0.8051\n",
            "Epoch 10/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0366 - accuracy: 0.9881 - val_loss: 0.8491 - val_accuracy: 0.8084\n",
            "Epoch 11/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 0.0264 - accuracy: 0.9911 - val_loss: 0.9382 - val_accuracy: 0.8120\n",
            "Epoch 12/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0154 - accuracy: 0.9948 - val_loss: 1.0953 - val_accuracy: 0.8044\n",
            "Epoch 13/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0214 - accuracy: 0.9928 - val_loss: 1.0876 - val_accuracy: 0.8014\n",
            "Epoch 14/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0147 - accuracy: 0.9955 - val_loss: 1.1366 - val_accuracy: 0.8022\n",
            "Epoch 15/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0112 - accuracy: 0.9963 - val_loss: 1.2140 - val_accuracy: 0.8059\n",
            "Epoch 16/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0081 - accuracy: 0.9972 - val_loss: 1.5559 - val_accuracy: 0.7926\n",
            "Epoch 17/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0113 - accuracy: 0.9961 - val_loss: 1.2924 - val_accuracy: 0.8076\n",
            "Epoch 18/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0085 - accuracy: 0.9971 - val_loss: 1.2357 - val_accuracy: 0.8055\n",
            "Epoch 19/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0049 - accuracy: 0.9982 - val_loss: 1.4387 - val_accuracy: 0.8096\n",
            "Epoch 20/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 0.0085 - accuracy: 0.9968 - val_loss: 1.4558 - val_accuracy: 0.8022\n",
            "Epoch 21/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0059 - accuracy: 0.9977 - val_loss: 1.4382 - val_accuracy: 0.8014\n",
            "Epoch 22/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0096 - accuracy: 0.9967 - val_loss: 1.3273 - val_accuracy: 0.8021\n",
            "Epoch 23/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0053 - accuracy: 0.9981 - val_loss: 1.6671 - val_accuracy: 0.7988\n",
            "Epoch 24/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 0.0040 - accuracy: 0.9989 - val_loss: 1.4953 - val_accuracy: 0.8034\n",
            "Epoch 25/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0054 - accuracy: 0.9982 - val_loss: 1.4787 - val_accuracy: 0.8049\n",
            "Epoch 26/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0063 - accuracy: 0.9976 - val_loss: 1.4342 - val_accuracy: 0.8084\n",
            "Epoch 27/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 0.0024 - accuracy: 0.9993 - val_loss: 1.6294 - val_accuracy: 0.8068\n",
            "Epoch 28/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0016 - accuracy: 0.9995 - val_loss: 1.6348 - val_accuracy: 0.8050\n",
            "Epoch 29/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 2.1931e-04 - accuracy: 1.0000 - val_loss: 1.6892 - val_accuracy: 0.8079\n",
            "Epoch 30/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 6.7207e-05 - accuracy: 1.0000 - val_loss: 1.7838 - val_accuracy: 0.8083\n",
            "Epoch 31/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 4.4880e-05 - accuracy: 1.0000 - val_loss: 1.8296 - val_accuracy: 0.8084\n",
            "Epoch 32/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 3.8405e-05 - accuracy: 1.0000 - val_loss: 1.8870 - val_accuracy: 0.8089\n",
            "Epoch 33/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 3.4243e-05 - accuracy: 1.0000 - val_loss: 1.9351 - val_accuracy: 0.8094\n",
            "Epoch 34/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 3.1208e-05 - accuracy: 1.0000 - val_loss: 1.9954 - val_accuracy: 0.8095\n",
            "Epoch 35/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 2.8935e-05 - accuracy: 1.0000 - val_loss: 2.0507 - val_accuracy: 0.8094\n",
            "Epoch 36/50\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 2.7292e-05 - accuracy: 1.0000 - val_loss: 2.1184 - val_accuracy: 0.8094\n",
            "Epoch 37/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 2.5985e-05 - accuracy: 1.0000 - val_loss: 2.1762 - val_accuracy: 0.8090\n",
            "Epoch 38/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 2.4910e-05 - accuracy: 1.0000 - val_loss: 2.2396 - val_accuracy: 0.8092\n",
            "Epoch 39/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 2.4068e-05 - accuracy: 1.0000 - val_loss: 2.3052 - val_accuracy: 0.8090\n",
            "Epoch 40/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 2.3412e-05 - accuracy: 1.0000 - val_loss: 2.3713 - val_accuracy: 0.8089\n",
            "Epoch 41/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 2.2822e-05 - accuracy: 1.0000 - val_loss: 2.4376 - val_accuracy: 0.8087\n",
            "Epoch 42/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 2.2332e-05 - accuracy: 1.0000 - val_loss: 2.5058 - val_accuracy: 0.8086\n",
            "Epoch 43/50\n",
            "782/782 [==============================] - 16s 20ms/step - loss: 0.0060 - accuracy: 0.9986 - val_loss: 1.7872 - val_accuracy: 0.8068\n",
            "Epoch 44/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 0.0302 - accuracy: 0.9903 - val_loss: 1.3782 - val_accuracy: 0.7970\n",
            "Epoch 45/50\n",
            "782/782 [==============================] - 15s 20ms/step - loss: 0.0030 - accuracy: 0.9990 - val_loss: 1.4377 - val_accuracy: 0.8031\n",
            "Epoch 46/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 1.3512e-04 - accuracy: 1.0000 - val_loss: 1.5110 - val_accuracy: 0.8045\n",
            "Epoch 47/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 4.8525e-05 - accuracy: 1.0000 - val_loss: 1.5550 - val_accuracy: 0.8054\n",
            "Epoch 48/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 3.1580e-05 - accuracy: 1.0000 - val_loss: 1.6002 - val_accuracy: 0.8057\n",
            "Epoch 49/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 2.1622e-05 - accuracy: 1.0000 - val_loss: 1.6484 - val_accuracy: 0.8062\n",
            "Epoch 50/50\n",
            "782/782 [==============================] - 15s 19ms/step - loss: 1.5054e-05 - accuracy: 1.0000 - val_loss: 1.6952 - val_accuracy: 0.8060\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nHGYuU4jPYaj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "outputId": "51e58522-90a7-43e2-92cf-7334d4c2b11c"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "\n",
        "plot_graphs(history, 'accuracy')\n",
        "plot_graphs(history, 'loss')"
      ],
      "execution_count": null,
      "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": "wSualgGPPK0S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "1eea33a5-ce35-4c74-fb57-a5e9465ef7cf"
      },
      "source": [
        "# Model Definition with LSTM\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "    tf.keras.layers.Dense(6, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 120, 16)           160000    \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, 64)                12544     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 6)                 390       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 172,941\n",
            "Trainable params: 172,941\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "K_Jc7cY3Qxke",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "fa27c23e-5473-4352-d719-7f3fde293d09"
      },
      "source": [
        "# Model Definition with Conv1D\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),\n",
        "    tf.keras.layers.Conv1D(128, 5, activation='relu'),\n",
        "    tf.keras.layers.GlobalAveragePooling1D(),\n",
        "    tf.keras.layers.Dense(6, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, 120, 16)           160000    \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 116, 128)          10368     \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d (Gl (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 6)                 774       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 171,149\n",
            "Trainable params: 171,149\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MCmZU9SE7-wQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}