LSTM_vs_Conv1D.ipynb

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            "87cf02bcd9c34f8eacd8c22904e0857e"
          ]
        }
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "imdb, info = tfds.load(\"imdb_reviews\", with_info=True, as_supervised=True)\n"
      ],
      "execution_count": 2,
      "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"
        },
        {
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
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        {
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "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.incompleteZMZHJN/imdb_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4d63e2d4096a465b90d69409839bc03b",
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteZMZHJN/imdb_reviews-test.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
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          },
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        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1262d55e8e7a4a6eb30a6ba010e50461",
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteZMZHJN/imdb_reviews-unsupervised.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e4f358b0280045d9a383658be9dea0a1",
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              "HBox(children=(FloatProgress(value=0.0, max=50000.0), HTML(value='')))"
            ]
          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:Dataset is using deprecated text encoder API which will be removed soon. Please use the plain_text version of the dataset and migrate to `tensorflow_text`.\n"
          ],
          "name": "stderr"
        },
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHQ2Ko0zl7M4"
      },
      "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": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7n15yyMdmoH1"
      },
      "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": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9axf0uIXVMhO",
        "outputId": "dd19f474-2fa2-4193-a388-538dfeb5d550",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 5,
      "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": {
        "id": "5NEpdhb8AxID",
        "outputId": "c34b5172-fd30-4c76-df5b-ee5d91034b82",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 6,
      "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": {
        "id": "V5LLrXC-uNX6",
        "outputId": "2a19ac31-1f07-4599-d3b3-1e5307ac147e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "num_epochs = 50\n",
        "history = model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 0.5875 - accuracy: 0.6790 - val_loss: 0.4338 - val_accuracy: 0.8110\n",
            "Epoch 2/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.3446 - accuracy: 0.8572 - val_loss: 0.3881 - val_accuracy: 0.8338\n",
            "Epoch 3/50\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 0.2555 - accuracy: 0.9025 - val_loss: 0.3706 - val_accuracy: 0.8398\n",
            "Epoch 4/50\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 0.2087 - accuracy: 0.9230 - val_loss: 0.4232 - val_accuracy: 0.8309\n",
            "Epoch 5/50\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 0.1651 - accuracy: 0.9412 - val_loss: 0.4310 - val_accuracy: 0.8351\n",
            "Epoch 6/50\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 0.1213 - accuracy: 0.9601 - val_loss: 0.5120 - val_accuracy: 0.8229\n",
            "Epoch 7/50\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 0.0865 - accuracy: 0.9720 - val_loss: 0.5925 - val_accuracy: 0.8224\n",
            "Epoch 8/50\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 0.0623 - accuracy: 0.9809 - val_loss: 0.6491 - val_accuracy: 0.8196\n",
            "Epoch 9/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0464 - accuracy: 0.9857 - val_loss: 0.7741 - val_accuracy: 0.8220\n",
            "Epoch 10/50\n",
            "782/782 [==============================] - 14s 17ms/step - loss: 0.0407 - accuracy: 0.9871 - val_loss: 0.8011 - val_accuracy: 0.8186\n",
            "Epoch 11/50\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 0.0271 - accuracy: 0.9924 - val_loss: 0.8440 - val_accuracy: 0.8158\n",
            "Epoch 12/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0219 - accuracy: 0.9935 - val_loss: 0.9920 - val_accuracy: 0.8095\n",
            "Epoch 13/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0193 - accuracy: 0.9938 - val_loss: 0.9352 - val_accuracy: 0.8018\n",
            "Epoch 14/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0159 - accuracy: 0.9954 - val_loss: 1.1153 - val_accuracy: 0.8117\n",
            "Epoch 15/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0120 - accuracy: 0.9960 - val_loss: 1.1134 - val_accuracy: 0.8152\n",
            "Epoch 16/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0148 - accuracy: 0.9953 - val_loss: 1.0109 - val_accuracy: 0.8087\n",
            "Epoch 17/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0093 - accuracy: 0.9971 - val_loss: 1.2774 - val_accuracy: 0.8118\n",
            "Epoch 18/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0098 - accuracy: 0.9962 - val_loss: 1.2431 - val_accuracy: 0.8154\n",
            "Epoch 19/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0083 - accuracy: 0.9974 - val_loss: 1.2014 - val_accuracy: 0.8083\n",
            "Epoch 20/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0082 - accuracy: 0.9977 - val_loss: 1.2293 - val_accuracy: 0.8121\n",
            "Epoch 21/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0061 - accuracy: 0.9977 - val_loss: 1.4280 - val_accuracy: 0.8089\n",
            "Epoch 22/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0077 - accuracy: 0.9973 - val_loss: 1.4236 - val_accuracy: 0.8076\n",
            "Epoch 23/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0072 - accuracy: 0.9976 - val_loss: 1.3408 - val_accuracy: 0.8149\n",
            "Epoch 24/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0062 - accuracy: 0.9981 - val_loss: 1.3022 - val_accuracy: 0.8119\n",
            "Epoch 25/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0020 - accuracy: 0.9994 - val_loss: 1.5847 - val_accuracy: 0.8069\n",
            "Epoch 26/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0040 - accuracy: 0.9987 - val_loss: 1.4557 - val_accuracy: 0.8091\n",
            "Epoch 27/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0076 - accuracy: 0.9971 - val_loss: 1.3890 - val_accuracy: 0.8111\n",
            "Epoch 28/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0051 - accuracy: 0.9982 - val_loss: 1.5048 - val_accuracy: 0.8116\n",
            "Epoch 29/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 1.4481 - val_accuracy: 0.8099\n",
            "Epoch 30/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0036 - accuracy: 0.9990 - val_loss: 1.5021 - val_accuracy: 0.8080\n",
            "Epoch 31/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0025 - accuracy: 0.9991 - val_loss: 1.5257 - val_accuracy: 0.8149\n",
            "Epoch 32/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0042 - accuracy: 0.9988 - val_loss: 1.4565 - val_accuracy: 0.7999\n",
            "Epoch 33/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 0.0053 - accuracy: 0.9982 - val_loss: 1.6099 - val_accuracy: 0.8082\n",
            "Epoch 34/50\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 0.0047 - accuracy: 0.9982 - val_loss: 1.5484 - val_accuracy: 0.8086\n",
            "Epoch 35/50\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 9.2210e-04 - accuracy: 0.9997 - val_loss: 1.6813 - val_accuracy: 0.8113\n",
            "Epoch 36/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 7.1345e-05 - accuracy: 1.0000 - val_loss: 1.7930 - val_accuracy: 0.8112\n",
            "Epoch 37/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 2.1873e-05 - accuracy: 1.0000 - val_loss: 1.8549 - val_accuracy: 0.8113\n",
            "Epoch 38/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 1.3883e-05 - accuracy: 1.0000 - val_loss: 1.9186 - val_accuracy: 0.8120\n",
            "Epoch 39/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 9.3768e-06 - accuracy: 1.0000 - val_loss: 1.9821 - val_accuracy: 0.8120\n",
            "Epoch 40/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 6.3568e-06 - accuracy: 1.0000 - val_loss: 2.0471 - val_accuracy: 0.8120\n",
            "Epoch 41/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 4.3271e-06 - accuracy: 1.0000 - val_loss: 2.1125 - val_accuracy: 0.8116\n",
            "Epoch 42/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 2.9326e-06 - accuracy: 1.0000 - val_loss: 2.1734 - val_accuracy: 0.8118\n",
            "Epoch 43/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 1.9666e-06 - accuracy: 1.0000 - val_loss: 2.2410 - val_accuracy: 0.8118\n",
            "Epoch 44/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 1.3041e-06 - accuracy: 1.0000 - val_loss: 2.3121 - val_accuracy: 0.8118\n",
            "Epoch 45/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 8.7008e-07 - accuracy: 1.0000 - val_loss: 2.3784 - val_accuracy: 0.8122\n",
            "Epoch 46/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 5.7936e-07 - accuracy: 1.0000 - val_loss: 2.4469 - val_accuracy: 0.8129\n",
            "Epoch 47/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 3.8455e-07 - accuracy: 1.0000 - val_loss: 2.5174 - val_accuracy: 0.8130\n",
            "Epoch 48/50\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 2.5369e-07 - accuracy: 1.0000 - val_loss: 2.5887 - val_accuracy: 0.8128\n",
            "Epoch 49/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 1.6783e-07 - accuracy: 1.0000 - val_loss: 2.6581 - val_accuracy: 0.8121\n",
            "Epoch 50/50\n",
            "782/782 [==============================] - 13s 16ms/step - loss: 1.1045e-07 - accuracy: 1.0000 - val_loss: 2.7258 - val_accuracy: 0.8123\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHGYuU4jPYaj",
        "outputId": "adf1eae1-49ea-4d8a-db9b-a97f64f9f45a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "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": 8,
      "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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eaCJQSuXd0YMw9maIL2HXD/BrbXNhpP9qSqm8MQYm3QN7foNbJ0Pxil5HpPJISwRKqbxZ8A6s+gIu/ifUaut1NOoMaCJQSuXe1p9h5uNwdle48F6vo1FnSBOBUip3UvbaCeRKVIUeb4FPv0YKO20jUErlzswn4OAOuP0bKFLa62hUPtBUrpTKuU3fwS8fQ9t7oep5Xkej8okmAqVUzqQdgcn3QZmzoMMjXkej8pFWDSmlcmb2c7B3M/SdCoEiXkej8pGWCJSKdtuXw/xhdubQ7GxbDPPfgvP7aVfRCKSJQKloFkyH8f1h+iPwXhdI3pj1MZPuhYSKcMkzBR+jcp0mAqXy6tAu+yW6P9HrSPJu8YeQ/Cu0uQv2bIJh7WDpJ3bU8DE//Bd2roQr/gPxJT0LVblHE4FSebViHKycABPvPvGLs7BI3Q/fPQ+12sGlz8GgH6BKc/hykE1wKfsgaT18/xI07AENrvA6YuUSbSxWKq/WTQN/HGyaDb98BOfd4nVEuTP3P3BkD3R51i4jWbKaXVx+3msw+9+QuNCOEwgUha4vex2tcpGWCJTKi5S9sOVHuOAue0c94/HCVUW0dwvMfxua3gBVmh3f7vND+3/AbTPt6x3L4dJ/Q0IF72JVrnMtEYjI+yKyS0RWZrO/o4jsF5GlzuOfbsWiVL779RswQVtd0v1NCAVtH3svq4iMsVU5S0Zl3eib2bfPgPih85NZ76/WAu6cBzd/Cc1uyv9YVVhxs2roQ+BNYNQpjplrjLnSxRiUcse6qVCsAlQ5z861c8kzMPUfsHQ0NO9TcHGk7IVN38PGb2HjbNi/1W6PKwHXvAtnX/bXn0lcZNs22j8EJatmf+644nBWJ3fiVmHFtRKBMWYOsMet8yvlmYw02PAN1L/0+IRrLW6zK3RNfwz2b8v9OWc+Ae+0h60Lc3b85h/gvUvhpTow7lZYNdFW8Vz5Gtz2DZSuBZ/eAHNePrGUYgzMeMwmsbb35T5OFZG8biO4QESWicg0EWmU3UEiMlBEFonIoqSkpIKMT6m/+v1HOHrATsF8jM8H3d+AUDp8NTh3VUSrvoAf37DVOu93sUkhPSXrY1P22sVgPuwKB/6Adv+A/jPg4U3Q62No0R+qt7TbGveEWc/aRHH0kP351RNh6wLo/Li941cKbxPBEqCmMaYp8AbwZXYHGmOGG2NaGGNalC9fvsACVCpL66ZBTDzU6Xji9jJ14G9Pw68zYdmnOTvXnt/sYK1qLeGB1bbn0Y9vwLCL4PcFx48zBlaMhzdbwi+j7RoAd823X+g12vx1icjYotBzBFzyf7Bmsh0slrQevnkKKjSE5jfn/fpVxPEsERhjDhhjDjmvpwIBESnnVTxK5Ygxtn2gTkf7ZXuylgOgxoUwbcjpexFlpMH4frbrZs/3oGgZuOp/toE2Iw3ev9RWNe1aAx/3hAm3QcnqMPA76PJ/EFvs1OcXsbOE9h4HBxLh7QvsXEFd/s/2CFLK4VkiEJFKIiLO61ZOLMlexaNUjuxaA/t+h7Mvz3q/z+f0IsqAEZfYlbyy8+0z8Mcv0O1NKF3z+PazOsHff4SWt8H8ofBWG1udc9mLdg2AyufmLua6f4MBs6HCOXBON/teqUxy1GtIRO4DPgAOAiOA5sAQY8zMU/zMp0BHoJyIJAJPAQEAY8ww4FpgkIhkACnADcYUxuGZKmytmw5phyA2AeISnOfi9lGsvL1jzvU5p9rn+ln0xjmm7Flw2wwY0wc+6Gr74bcacOLnrZsOP71pSxANu/31HHHF7ZQODXvA2ilw4d12wFdelT3LdgfV/8VUFiQn370isswY01RELgXuAJ4EPjLGFPjKFC1atDCLFi0q6I9Vhc3v823VSnbqdYHrRmZdvXMq714MJgQDZ5/+2JS98MWdsH46NLkervqvrc7Zvw2GtbVf7Ld9A4H43MWgVB6IyGJjTIus9uV0HMGxW5mu2ASw6li1jlJhacEwO0Fav2mQkWp7zaQdss/Jv8KcV+CT6+HGz2xpIScO7rTTMXd6PGfHFykNN3wK8/4Ds56zE7dd9yFMHmzbAK79UJOACgs5TQSLRWQmUBt4VESKAyH3wlLqDOzfBqsn2ekfKmbTK7lcffjiDvj4GtuYmpNZNX+dAZisB2llx+ezA7eqng/jb4O3LrAjkq8eDuXq5vw8Srkop43FtwFDgJbGmCPYuv5+rkWl1JlYOAIw0PL27I8593q49gN7hz+qh5187XTWTbe9dio2zn1MZ3WGO+ZAzQuh9SBo2iv351DKJTktEVwALDXGHBaRPsB5wP/cC0upPEpPsXPsn931xJ44WWnUA2LiYOwtMKqb7bZZLJsezOkpsHEWnHdz3hqZAUpVh75f5e1nlXJRTksEbwNHRKQp8CCwkVPPIaSUN1aMg5Q90GZQzo4/+3K48VPY/St8eIVtB8jKpu8hI+XUvYWUKqRymggynK6d3YE3jTFDAR2frsKLMbDgHVt1UzMX6+rW/Rv0Hg/7tsLbF8J3L8Dh3Sces34axBaHWhflb8xKhYGcJoKDIvIocDMwRUR8OGMClAobW36wPXNa35H76pva7aD/NNuo+93z8Foj27tn968QCtn2gboX26okpSJMTtsIegE3Af2NMTtEpAagSxap8DL/bShSBppcl7efr9wUeo+FpHV2sNfST2DxB3bKiEM7sh9NrFQhl6MSgTFmBzAaKCkiVwKpxhhtI1DhY+8WO+r3/L4QKHJm5yp/NnR7A+5fCR0egd3rIKaIHYSmVATKUSIQkeuBn4HrgOuBBSJyrZuBqQj36zfw29z8m/Jg4buA2Pl58ktCBej0GAxeCfcstpPCKRWBclo19Dh2DMEuABEpD3wDjHcrMBXBjuyxo3pNEMrWtXfxTW+CYmXzdr60w3Z5xobdzmw+nuzEFs39VBRKFSI5bSz2HUsCjuRc/KxSJ1o31SaBjo9B0XJ2IZZXG9iRt5vzMDHa8jGQuh9a3+lOvEpFuJyWCKaLyAzg2GobvYCp7oSkIt6ayVCqBnR4GDo+AjtX20Fgyz6DlePtwintHoRGV59+3vxjXUYrN4XqrQskfKUiTU4bix8ChgPnOo/hxphH3AxMRajUA3aE7jndjnfxrNgQur4ED66F7kMhFLSLsAxtZXvuBNP/ep7Du23y+KgHJK210zboPIhK5UlOSwQYYyYAE1yMRUWDX2dCMA3Oueqv+2KLQvM+tr1gzSQ7Q+iXg+wAr4vut/3418+w6+5u+cFOB126FrR/GJpo3wWl8uqUiUBEDgJZVdgKYIwxJVyJSkWuNZMhoSJUa5X9MT6fnQeoYXf7xT/nJbsg/DHl6tuqo3O6QaUmWhJQ6gydMhEYY3QaCZV/0lPg16/tzJu+HNRKitgpn+tfCptmw85VUPcSqNDA/ViViiI5rhpS6oxtnAXph+2dfG6I2Gmcz+rsTlxKRTntAqoKzprJEF9KJ25TKsxoIlAFI5huxw+c3RX8Ol+hUuFEE4EqGL/NsYO+suotpJTylLYRqJxbP9MuA+kPgC8G/LHOIwZKVIW292U/TfOayRAopvX8SoUhTQQqZ4IZMPVBOHoIileGULodDxDMsM+Hd8GeTdDj7b925wwFYe0UqN8FAvHexK+UypYmApUzKyfAvt/hxs+ynpf/uxfhu39D+QZw0eAT921dYBOFVgspFZY0EajTC4Vg3qt2DqB6l2Z9TIeH7bz93zwN5epBgyuO71szGfxxOp+/UmFKG4vV6a2baufzueiB7AeCidh5gqo0hwkDYMcKu90YmwjO6gxxOj5RqXCkiUCdmjEw9z92Tp9GV5/62EARuPFTiC8Jn9wAB3fC9qWwf6tWCykVxlxLBCLyvojsEpGV2ewXEXldRDaIyHIROc+tWNQZ+O17+GMJtB1sewedTvFKcNNnkLIHxvSG5eNA/Lrer1JhzM0SwYfAZafYfzlQz3kMBN52MRaVV3P/AwmVoNlNOf+Zyk3h6ncgcSHMH2pHEusyj0qFLdcSgTFmDrDnFId0B0YZaz5QSkQquxWPyoOtC+1AsAvvzn58QHYadoPOT9rXjXrkf2xKqXzjZa+hqsDWTO8TnW3bTz5QRAZiSw3UqFGjQIKLeMF0O7VzpabQ8vasG4HnvWrnBjq/X94+o92DUKcjVNFaP6XCWaFoLDbGDDfGtDDGtChfvrzX4USGhe/BLx/DtIdg5JWQvPHE/TtX2d5CbQZBXELePkMEqrXI2ZTTSinPePl/6Dageqb31Zxtym2Hk+3gr9odoPtbsGMlvN0W5r9txwwAzHvNTgnRaqC3sSqlXOdlIpgE3OL0HmoD7DfG/KVaSLlg9nN2qojLXoDmveGu+VC7PUwfAh9cDr9+Y0cSt+yvjbxKRQHX2ghE5FOgI1BORBKBp4AAgDFmGDAV6ApsAI4AeayIVrmyYyUs/sC2C1RsaLeVqAI3jYHlY2DawzC6p51M7oK7vY1VKVUgXEsExpgbT7PfAHe59fkqC8bYu/74ktDx0RP3iUDTG2zj7swnoWIjOyZAKRXxdK6haLJmMmyeC11fyb7Kp3gl6PluwcallPKUdueIFumpMPNxO3FcXruDKqUikpYIosVPb9pppG+ZmLOpIpRSUUNLBNHgwB8w91VocKVtA1BKqUw0EUSDb56GUAZ0edbrSJRSYUgTQSTb8xtMvMt2C73wbihT2+uIlFJhSCuLI9Ge32DuK7D0U7vQfOtB0P4hr6NSSoUpTQSRJHMC8MXY6SEuGqzjAZRSp6SJoLA7tAvWTrGPjbOOJ4C290EJndVbKXV6mggKoz2/wdqvYM1XsHUBYKBUTdsO0HqQJgClVK5oIihspg2BBc5ibhWbQMchtltoxUZ2mgillMolTQSFyfoZNgk072Mbf0vX8joipVQE0ERQWBxOhol3Q8XGcMWruV86UimlsqGJoDAwBqY8ACl74eYvNAkopfKVDigrDFZOgNVfQqfHoFJjr6NRSkUYTQTh7sAftjRQrZXtEqqUUvlME0E4M8a2CwTT4eph4PN7HZFSKgJpG0E4W/Q+bPzWLiRT9iyvo1FKRSgtEYSr5I0w8wmo08muL6yUUi7RRBCOdq2FMTeDLwDdh+pAMaWUq7RqKJwEM+CH/8L3L0JsAlz7PpSs6nVUSqkIp4kgXOxYCRP/DtuXQcPutl0goYLXUSmlooAmAq8F0+0yknNehviScN1IaNTD66iUUlEkahLB1j1H+HzJNu7qdBYx/jBpGjnwB3zSC3Ysh8bXwuUvQbGyXkellIoyUZMIVm8/wGvfrKdZjVJ0qF/e63DsVNKjusORZOj1MZxzldcRKaWiVJjcGruv49nlKR4fw8Sl27wOBXatgfcvg6MH4NZJmgSUUp6KmkQQF+Ona+PKzFi5g9T0oHeBbFsMH1xuX/edClXP9y4WpZTC5UQgIpeJyDoR2SAiQ7LY31dEkkRkqfNwdeRU9+ZVOJwW5Js1O938mOxtngcju0Ncceg/DSo29CYOpZTKxLVEICJ+YChwOdAQuFFEsvrmG2OMaeY8RrgVD0Dr2mWpWCKOiUv/cPNjsrZ+BnzcE0pUgf4zoEydgo9BKaWy4GaJoBWwwRizyRiTBnwGdHfx807L7xOuOrcK363bxf4j6QX3wSs/h89ugvJnQ7+pNhkopVSYcDMRVAW2Znqf6Gw7WU8RWS4i40WkuovxANC9WVXSg4apK7e7/VHWklEwvj9Uawm3ToZi5Qrmc5VSKoe8biyeDNQyxpwLfA2MzOogERkoIotEZFFSUtIZfWDjqiWoU75YwfQe+uktmHQPnNUJ+nxuB4wppVSYcTMRbAMy3+FXc7b9yRiTbIw56rwdAWTZhcYYM9wY08IY06J8+TMbAyAidG9alQW/7WH7/pQzOle2jIHvX4IZj9quoTd+BrFF3fkspZQ6Q24mgoVAPRGpLSKxwA3ApMwHiEjlTG+7AWtcjOdP3ZtVwRj4apkL1UPGwNdPwuznoOmNcO2HusawUiqsuZYIjDEZwN3ADOwX/FhjzCoR+ZeIdHMOu1dEVonIMuBeoK9b8ZCeYhttQyFqlStG0+ql+DK/q4cyjsJX98OPb0DLAdD9LfBHzeBtpVQh5eq3lDFmKjD1pG3/zPT6UeBRN2P404s8oz8AAA49SURBVPKxMPleqPQqXPw03c+tw7+mrGHDroPUrVA85+cxxnYF3TgLDu+CQ0nO805I3W+Pueh+uPgpXUdAKVUoRM/tavObIVAEZj0Lo3vSp9qFTPF1YdLSujzQ5eycnWPvZpj6MPw6A2KLQ/GKUKwCVDgH6nS0rys2ggZdXbwQpZTKX9GTCHw+OPd6aNgDlowk9vsXmRD7I3MXTMU0ex2pcE72P5uRBj++DnNeAfFBl+eg9Z1a7aOUighijPE6hlxp0aKFWbRo0Zmf6OghVn7+AjXWjqC4pNpEUOU8qNIMqp4HFRvbRt7f5sKUB2D3ejinG1z2gq4appQqdERksTGmRVb7oveWNi6BGlc/xSXPNuH56gvpXGwzrJ8GSz+2+30BKHsWJK2FUjXhpnFQv4unISullBuiNxEAJeIDnNegLg9vLsv82zsT4xPYvxW2LYE/foEdK+yykW0H6zgApVTEiupEAHZMwbSVO5i6cgfdmlaBUjXsQ5eLVEpFCa+nmPBcpwYVaFK1JA+NW8aPG3Z7HY5SShW4qE8EcTF+RvVvRa2yxbh91CIWbd7jdUhKKVWgoj4RAJQuFstHt7eiUol4+n2wkOWJ+7wOSSmlCowmAkeF4vGMHtCaUsUC3PL+z6zdccDrkJRSqkBoIsikcskifHJ7G+Jj/PQZsYCNSYe8DkkppVynieAk1csU5ZMBrQGh97uaDJRSkU8TQRbqlE9g9O2tOZoR5LL/zuFfk1ez93Ca12EppZQrNBFk4+xKxZk+uD3Xnl+ND3/8jfYvz+bt7zaSmh70OjSllMpXmghOoWKJeJ6/5lxmDG5P69pleHH6Wjq98h3jFm0lGCpcczQppVR2NBHkQL2KxRlxa0s+G9iGCsXjeGj8ci7/3xymrdhOSBOCUqqQ00SQC23qlOXLu9oy9KbzCIYMg0Yv4co35vH16p0UtllclVLqGE0EuSQiXHFuZWbe34HXejXlSFoGA0YtotubPzB77S5NCEqpQid61yPIJxnBEJ//so3Xv/2VxL0pNKteigcuqU+7euUQXapSKRUmTrUegSaCfJKWEWLc4q0MnbWBP/an0qpWGR7oUp82dcp6HZpSSmkiKEhHM4KMWbiVN2dtYNfBo7StW5YHLqnP+TXLeB2aUiqKaSLwQGp6kI/nb2HY9xvZfSiNDvXLc9tFtbmobjl8Pq0yUkoVLE0EHjqSlsGon7bw7pxNJB9Oo0aZotzUugbXnV+NsglxXoenlIoSmgjCwNGMIDNW7WT0/C0s+G0PsX4flzepRO/WNWlZq7Q2LCulXKWJIMxs2HWQ0Qt+Z8LiRA6kZlC+eBwd6penQ/3ytKtXjlJFY70OUSkVYTQRhKmUtCDTV21n1tok5v6axL4j6fgEmlUvRYf6FWhVuwwNK5egZNGA16EqpQo5TQSFQDBkWJa4j+/WJfH9+iSWJ+7j2D9NlZLxnFO5BA2rlOCcyiWoXa4YJYsEKFU0QJGAX6uVlFKnpYmgENpzOI0V2/azZvsBVv9xgDXbD7Bp9+G/THYX8AsliwQoUSRAuWJx1C5XjLoVEqhbIYGzyidQtXQR/NpLSamo51kiEJHLgP8BfmCEMeaFk/bHAaOA84FkoJcxZvOpzhktiSArqelB1u88yNY9KexPST/hcSAlnV0HU9mUdJjkTGsnxMX4qF2uGKWLxlI8PoYSRQL2Od4+F4n1ExfjJzbGR9yfDz/xAR9FYv0UCfiPPwf8xPh1VhKlCqNTJYIYFz/UDwwFLgESgYUiMskYszrTYbcBe40xdUXkBuBFoJdbMRV28QE/51YrxbnVSp3yuL2H09iYdIgNu+xjc/Jh9qeksyX5CAdT0zmYmsHBoxl5iiHWbxNEsVibIIrFxVAk4Ccu4CctI8jRjBBH00OkZgQ5mh4iPRiiWJxNQCXijz0HKFEkhhifYAwYcJ6dNwIBnw+/T4jxCX6/ffaJnFANduyVCAT8PmL9PmJjfAT8PgJ+IRDjIxQyZIQMGUFDRihEetAQDIUwBud8dv4on9jz+MR+jt93/Nnvgxifj2Jx9nqLxcbY5zg/sU5iTA8a0oP2etOCIdIyQqSmB0lJC3EkLYMj6UFS0uzDYBN0rPOI8x9/fSz+Y9cS6/fh9zu/J2MIGQgZ8+d7Eed3c+x35cQdMjaejKAhPeQ8B0NkhAyhkCFoDMGQIRSCjFAIg/P74Pjv5dj1F3X+rYs6NwRaFRl5XEsEQCtggzFmE4CIfAZ0BzIngu7A087r8cCbIiKmsNVXhZnSxWJpUawMLWplP5o5GDIcOprB0XTny/vYl7jzBXY0PUTKsS+vTM9H0oKkpGVw2PlSO+K83p+STlyMj4S4GMoW8xMXsKWLgM/HkfTgn6WWbftSOJCSwYHUdIIhg2C/gAXB+Q/jxFcY1nzwCRSCMPONCBQJ2KTg9wl+JznbBAQ+n3CqNOF2Eon0FNWrZXVub1cn38/rZiKoCmzN9D4RaJ3dMcaYDBHZD5QFdmc+SEQGAgMBatSo4Va8UcXvs20LFAnfHknGuWvNcB4nJIZML4/d/aYF7R1/Woa9Mz+aEfqzVBHw+4jxiy1p+OXPhHNsPYljd9khYwgZQzB0PBkFnfMfSQty+GjG8YeTDP0+ce7kxSmN2NfxAT9FY2P+ekeNkBYMkpp+vPSQ5iThY6WKP19n2Lv4Y6UWn/OMc/ee+XcUMubPO36fz15rjF+I8fsI+Jxn/4klnmOliGO/kBN/D/Z3cCQt48+bgCNpQY4ctSWckPP7CZ3wezNkmxddTpin+OSIUc6lQahuJoJ8Y4wZDgwH20bgcTiqgIiI80XmdSRKRTY3W/62AdUzva/mbMvyGBGJAUpiG42VUkoVEDcTwUKgnojUFpFY4AZg0knHTAJudV5fC8zS9gGllCpYrlUNOXX+dwMzsN1H3zfGrBKRfwGLjDGTgPeAj0RkA7AHmyyUUkoVIFfbCIwxU4GpJ237Z6bXqcB1bsaglFLq1HR0kFJKRTlNBEopFeU0ESilVJTTRKCUUlGu0M0+KiJJwJY8/ng5Thq1HEWi9dr1uqOLXnf2ahpjyme1o9AlgjMhIouym30v0kXrtet1Rxe97rzRqiGllIpymgiUUirKRVsiGO51AB6K1mvX644uet15EFVtBEoppf4q2koESimlTqKJQCmlolzUJAIRuUxE1onIBhEZ4nU8bhGR90Vkl4iszLStjIh8LSK/Os+lvYzRDSJSXURmi8hqEVklIvc52yP62kUkXkR+FpFlznU/42yvLSILnL/3Mc5U8BFHRPwi8ouIfOW8j/jrFpHNIrJCRJaKyCJn2xn9nUdFIhARPzAUuBxoCNwoIg29jco1HwKXnbRtCPCtMaYe8K3zPtJkAA8aYxoCbYC7nH/jSL/2o0BnY0xToBlwmYi0AV4EXjPG1AX2Ard5GKOb7gPWZHofLdfdyRjTLNPYgTP6O4+KRAC0AjYYYzYZY9KAz4DuHsfkCmPMHOzaDpl1B0Y6r0cCPQo0qAJgjNlujFnivD6I/XKoSoRfu7EOOW8DzsMAnYHxzvaIu24AEakGXAGMcN4LUXDd2Tijv/NoSQRVga2Z3ic626JFRWPMduf1DqCil8G4TURqAc2BBUTBtTvVI0uBXcDXwEZgnzEmwzkkUv/e/ws8DISc92WJjus2wEwRWSwiA51tZ/R3XigWr1f5xxhjRCRi+wyLSAIwARhsjDlgbxKtSL12Y0wQaCYipYAvgAYeh+Q6EbkS2GWMWSwiHb2Op4BdZIzZJiIVgK9FZG3mnXn5O4+WEsE2oHqm99WcbdFip4hUBnCed3kcjytEJIBNAqONMZ87m6Pi2gGMMfuA2cAFQCkROXajF4l/722BbiKyGVvV2xn4H5F/3RhjtjnPu7CJvxVn+HceLYlgIVDP6VEQi10beZLHMRWkScCtzutbgYkexuIKp374PWCNMebVTLsi+tpFpLxTEkBEigCXYNtHZgPXOodF3HUbYx41xlQzxtTC/v88yxjTmwi/bhEpJiLFj70GugArOcO/86gZWSwiXbF1in7gfWPMcx6H5AoR+RToiJ2WdifwFPAlMBaogZ3C+3pjzMkNyoWaiFwEzAVWcLzO+DFsO0HEXruInIttHPRjb+zGGmP+JSJ1sHfKZYBfgD7GmKPeReoep2roH8aYKyP9up3r+8J5GwN8Yox5TkTKcgZ/51GTCJRSSmUtWqqGlFJKZUMTgVJKRTlNBEopFeU0ESilVJTTRKCUUlFOE4FSDhEJOjM6Hnvk2wR1IlIr84ywSoUTnWJCqeNSjDHNvA5CqYKmJQKlTsOZ//0lZw74n0WkrrO9lojMEpHlIvKtiNRwtlcUkS+cNQKWiciFzqn8IvKus27ATGckMCJyr7OOwnIR+cyjy1RRTBOBUscVOalqqFemffuNMU2AN7Ej1AHeAEYaY84FRgOvO9tfB7531gg4D1jlbK8HDDXGNAL2AT2d7UOA5s557nTr4pTKjo4sVsohIoeMMQlZbN+MXfxlkzOx3Q5jTFkR2Q1UNsakO9u3G2PKiUgSUC3z1AbO1NhfOwuHICKPAAFjzLMiMh04hJ0K5MtM6wsoVSC0RKBUzphsXudG5jlvghxvo7sCu4LeecDCTLNnKlUgNBEolTO9Mj3/5Lz+ETvzJUBv7KR3YJcKHAR/LhpTMruTiogPqG6MmQ08ApQE/lIqUcpNeueh1HFFnJW+jplujDnWhbS0iCzH3tXf6Gy7B/hARB4CkoB+zvb7gOEichv2zn8QsJ2s+YGPnWQhwOvOugJKFRhtI1DqNJw2ghbGmN1ex6KUG7RqSCmlopyWCJRSKsppiUAppaKcJgKllIpymgiUUirKaSJQSqkop4lAKaWi3P8DQ+morZxxlm8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wSualgGPPK0S",
        "outputId": "6f681456-9fe7-4b61-8691-b6349ad7aa0e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 9,
      "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": {
        "id": "K_Jc7cY3Qxke",
        "outputId": "7fd8db83-931e-4b43-8f04-7f910b1dccdc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": 10,
      "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"
        }
      ]
    }
  ]
}