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            "c3da9b3e973e4ef9b7c819c5edeec8f2",
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            "f47a618c7c804e63820c3d95a6743e48",
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            "0fdb391f3232416f9593dd4d7510bb07",
            "991c9099d10c4212bebfd82f0b771937",
            "5993a9f0e1864bd18150eed331b0c11d",
            "7bf04c390e394c70bb06e6bf2cbbfc9b",
            "cecd755378a04bda82a95aaa1218da81",
            "5ae1a9d695ed4a3b9d1d965c7044ae41",
            "a01cdc88828f4aa088a8ca3dd6fcfe14",
            "d7f7f724d5f04eceb05c47e927589062",
            "4e9e8efd7f7745a4bd5b26c682f7fd04"
          ]
        }
      },
      "source": [
        "(train_examples,test_examples),ds_info=tfds.load('imdb_reviews',split=['train','test'],batch_size=-1,as_supervised=True,with_info=True)\n",
        "print('info', ds_info)"
      ],
      "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"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e159933ea330488f82130ebf6019c1b9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "713c179cf2f045699658621270b5cdd0",
              "version_minor": 0,
              "version_major": 2
            },
            "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": "ddacb5a9c385477dbc38d492f1cb0b86",
              "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.incompleteBUPTFZ/imdb_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fc0245f1c6e44c20ba7e6bbec1d586b4",
              "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": "17915e7a2c654b08874823d126ae7d48",
              "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.incompleteBUPTFZ/imdb_reviews-test.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d80a6fe0f1f147ee9f8c7bc2974c2ad2",
              "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": "6588acb06f6146beadcfc87c198f9ee1",
              "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.incompleteBUPTFZ/imdb_reviews-unsupervised.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "991c9099d10c4212bebfd82f0b771937",
              "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",
            "\rinfo tfds.core.DatasetInfo(\n",
            "    name='imdb_reviews',\n",
            "    version=1.0.0,\n",
            "    description='Large Movie Review Dataset.\n",
            "This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.',\n",
            "    homepage='http://ai.stanford.edu/~amaas/data/sentiment/',\n",
            "    features=FeaturesDict({\n",
            "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),\n",
            "        'text': Text(shape=(), dtype=tf.string),\n",
            "    }),\n",
            "    total_num_examples=100000,\n",
            "    splits={\n",
            "        'test': 25000,\n",
            "        'train': 25000,\n",
            "        'unsupervised': 50000,\n",
            "    },\n",
            "    supervised_keys=('text', 'label'),\n",
            "    citation=\"\"\"@InProceedings{maas-EtAl:2011:ACL-HLT2011,\n",
            "      author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},\n",
            "      title     = {Learning Word Vectors for Sentiment Analysis},\n",
            "      booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},\n",
            "      month     = {June},\n",
            "      year      = {2011},\n",
            "      address   = {Portland, Oregon, USA},\n",
            "      publisher = {Association for Computational Linguistics},\n",
            "      pages     = {142--150},\n",
            "      url       = {http://www.aclweb.org/anthology/P11-1015}\n",
            "    }\"\"\",\n",
            "    redistribution_info=,\n",
            ")\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3y2RS7FqNPiQ"
      },
      "source": [
        "train_data,train_labels=tfds.as_numpy(train_examples)\n",
        "test_data,test_labels=tfds.as_numpy(test_examples)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PvUepRRhEZ9",
        "outputId": "da705173-0ad0-445e-a2e4-c0a1bf50b195",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        }
      },
      "source": [
        "embedding='https://tfhub.dev/google/nnlm-en-dim50/2'\n",
        "hub_layer=hub.KerasLayer(embedding,input_shape=[],dtype=tf.string,trainable=True)\n",
        "hub_layer(train_data[:3])\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3, 50), dtype=float32, numpy=\n",
              "array([[ 0.5423195 , -0.0119017 ,  0.06337538,  0.06862972, -0.16776837,\n",
              "        -0.10581174,  0.16865303, -0.04998824, -0.31148055,  0.07910346,\n",
              "         0.15442263,  0.01488662,  0.03930153,  0.19772711, -0.12215476,\n",
              "        -0.04120981, -0.2704109 , -0.21922152,  0.26517662, -0.80739075,\n",
              "         0.25833532, -0.3100421 ,  0.28683215,  0.1943387 , -0.29036492,\n",
              "         0.03862849, -0.7844411 , -0.0479324 ,  0.4110299 , -0.36388892,\n",
              "        -0.58034706,  0.30269456,  0.3630897 , -0.15227164, -0.44391504,\n",
              "         0.19462997,  0.19528408,  0.05666234,  0.2890704 , -0.28468323,\n",
              "        -0.00531206,  0.0571938 , -0.3201318 , -0.04418665, -0.08550783,\n",
              "        -0.55847436, -0.23336391, -0.20782952, -0.03543064, -0.17533456],\n",
              "       [ 0.56338924, -0.12339553, -0.10862679,  0.7753425 , -0.07667089,\n",
              "        -0.15752277,  0.01872335, -0.08169781, -0.3521876 ,  0.4637341 ,\n",
              "        -0.08492756,  0.07166859, -0.00670817,  0.12686075, -0.19326553,\n",
              "        -0.52626437, -0.3295823 ,  0.14394785,  0.09043556, -0.5417555 ,\n",
              "         0.02468163, -0.15456742,  0.68333143,  0.09068331, -0.45327246,\n",
              "         0.23180096, -0.8615696 ,  0.34480393,  0.12838456, -0.58759046,\n",
              "        -0.4071231 ,  0.23061076,  0.48426893, -0.27128142, -0.5380916 ,\n",
              "         0.47016326,  0.22572741, -0.00830663,  0.2846242 , -0.304985  ,\n",
              "         0.04400365,  0.25025874,  0.14867121,  0.40717036, -0.15422426,\n",
              "        -0.06878027, -0.40825695, -0.3149215 ,  0.09283665, -0.20183425],\n",
              "       [ 0.7456154 ,  0.21256861,  0.14400336,  0.5233862 ,  0.11032254,\n",
              "         0.00902788, -0.3667802 , -0.08938274, -0.24165542,  0.33384594,\n",
              "        -0.11194605, -0.01460047, -0.0071645 ,  0.19562712,  0.00685216,\n",
              "        -0.24886718, -0.42796347,  0.18620004, -0.05241098, -0.66462487,\n",
              "         0.13449019, -0.22205497,  0.08633006,  0.43685386,  0.2972681 ,\n",
              "         0.36140734, -0.7196889 ,  0.05291241, -0.14316116, -0.1573394 ,\n",
              "        -0.15056328, -0.05988009, -0.08178931, -0.15569411, -0.09303783,\n",
              "        -0.18971172,  0.07620788, -0.02541647, -0.27134508, -0.3392682 ,\n",
              "        -0.10296468, -0.27275252, -0.34078008,  0.20083304, -0.26644835,\n",
              "         0.00655449, -0.05141488, -0.04261917, -0.45413622,  0.20023568]],\n",
              "      dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBM6lVNwxtO2",
        "outputId": "55a43316-3536-4517-8d25-eaca43b87292",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 271
        }
      },
      "source": [
        "model=tf.keras.Sequential()\n",
        "model.add(hub_layer)\n",
        "#model.add(tf.keras.layers.Dropout(0.5))\n",
        "model.add(tf.keras.layers.Dense(16,activation='relu'))\n",
        "#model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))\n",
        "model.summary()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer (KerasLayer)     (None, 50)                48190600  \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 16)                816       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 48,191,433\n",
            "Trainable params: 48,191,433\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnhJLB3V0b8D"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.losses.BinaryCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBMPbr1o0gsE"
      },
      "source": [
        "x_val=train_data[:10000]\n",
        "p_x_train=train_data[10000:]\n",
        "y_val=train_labels[:10000]\n",
        "p_y_train=train_labels[10000:]\n"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7M1hVmE2RqF",
        "outputId": "233ac216-fd89-4b7e-ee12-30d143a83ca5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(y_val[:3])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YpLd0KC303WE",
        "outputId": "dd20e8d5-ceb4-4a4b-8177-3ad6f3181388",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        }
      },
      "source": [
        "history = model.fit(p_x_train,\n",
        "                    p_y_train,\n",
        "                    epochs=10,\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val)\n",
        "                    )\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "30/30 [==============================] - 2s 67ms/step - loss: 0.5510 - accuracy: 0.6683 - val_loss: 0.4854 - val_accuracy: 0.7769\n",
            "Epoch 2/10\n",
            "30/30 [==============================] - 2s 63ms/step - loss: 0.3212 - accuracy: 0.9213 - val_loss: 0.3696 - val_accuracy: 0.8405\n",
            "Epoch 3/10\n",
            "30/30 [==============================] - 2s 62ms/step - loss: 0.1883 - accuracy: 0.9728 - val_loss: 0.3178 - val_accuracy: 0.8578\n",
            "Epoch 4/10\n",
            "30/30 [==============================] - 2s 62ms/step - loss: 0.1162 - accuracy: 0.9875 - val_loss: 0.3002 - val_accuracy: 0.8657\n",
            "Epoch 5/10\n",
            "30/30 [==============================] - 2s 62ms/step - loss: 0.0763 - accuracy: 0.9942 - val_loss: 0.2982 - val_accuracy: 0.8713\n",
            "Epoch 6/10\n",
            "30/30 [==============================] - 2s 62ms/step - loss: 0.0525 - accuracy: 0.9973 - val_loss: 0.3045 - val_accuracy: 0.8704\n",
            "Epoch 7/10\n",
            "30/30 [==============================] - 2s 63ms/step - loss: 0.0375 - accuracy: 0.9989 - val_loss: 0.3123 - val_accuracy: 0.8717\n",
            "Epoch 8/10\n",
            "30/30 [==============================] - 2s 63ms/step - loss: 0.0275 - accuracy: 0.9993 - val_loss: 0.3226 - val_accuracy: 0.8721\n",
            "Epoch 9/10\n",
            "30/30 [==============================] - 2s 63ms/step - loss: 0.0208 - accuracy: 0.9995 - val_loss: 0.3335 - val_accuracy: 0.8723\n",
            "Epoch 10/10\n",
            "30/30 [==============================] - 2s 62ms/step - loss: 0.0159 - accuracy: 0.9997 - val_loss: 0.3446 - val_accuracy: 0.8718\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itW-Ud6h4HK3",
        "outputId": "079d519d-1960-4732-a19a-b3e1b0a9dca6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "results = model.evaluate(test_data, test_labels)\n",
        "\n",
        "print(results)\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "782/782 [==============================] - 3s 4ms/step - loss: 0.3797 - accuracy: 0.8532\n",
            "[0.3797491490840912, 0.8532400131225586]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4VX0N-Q4gA5",
        "outputId": "27dd878b-488b-4a38-ee5c-f1cd95a5b6ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "history_dict = history.history\n",
        "history_dict.keys()\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-WYmMqIR4k0w",
        "outputId": "55896825-44d3-4fd7-8e65-32cd899eb033",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\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",
        "# \"bo\" is for \"blue dot\"\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "# b is for \"solid blue line\"\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()\n",
        "plt.clf()   # clear figure\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 19,
      "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"
          }
        }
      ]
    }
  ]
}