IMDB DATASET NLP with tensorflow apply deep learning in google colab.ipynb

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            "73b500fc07a94998981047b74a88ea57"
          ]
        },
        "outputId": "26821d88-d60e-434e-c17a-486d965a5f1c"
      },
      "source": [
        "import tensorflow_datasets as tfds # add tensorflow inbuild datset\n",
        "imdb, info = tfds.load(\"imdb_reviews\", with_info=True, as_supervised=True) # load method for load data set into imdb, info\n",
        "#info = meta data\n",
        "# imbd =main dataset.\n",
        "# its by default dataset so some numpay operations required of this data .becoz we dont know how this data will precced becoz it by defualt dataset of tensorflow\n",
        "print(imdb)"
      ],
      "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": "9d288b1e298e4912a6ad18a63885fecf",
              "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": "4620abe659534fde96118b0304c30349",
              "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": "3fe5c56c099c4c1889a570e2c127bd0d",
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              "version_major": 2
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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.incomplete1VVZLU/imdb_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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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": "071309b113d44183ad1c7c9fe55237ba",
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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.incomplete1VVZLU/imdb_reviews-test.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "50121f821e3049358b9f94dc8f06ddb0",
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              "HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1d3d660f99b34cb4a9f73a37a508aed3",
              "version_minor": 0,
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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.incomplete1VVZLU/imdb_reviews-unsupervised.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4454cd3c9e9f4cc5a930f51d44f02ddd",
              "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{'test': <DatasetV1Adapter shapes: ((), ()), types: (tf.string, tf.int64)>, 'train': <DatasetV1Adapter shapes: ((), ()), types: (tf.string, tf.int64)>, 'unsupervised': <DatasetV1Adapter shapes: ((), ()), types: (tf.string, tf.int64)>}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qHR-XEPEstFB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "07e5fc42-7dc5-4735-c67c-5616d80b840d"
      },
      "source": [
        "import numpy as np # import numpy to deal linear algebra\n",
        "print(imdb)# its show meta data\n",
        "print(info)# its show some info not dataset\n",
        "train_data, test_data = imdb['train'], imdb['test'] # spilte 25000 record for training and 25000 for testing  \n",
        "\n",
        "training_sentences = [] # its for senetences\n",
        "training_labels = [] # its for label output for particular sentences\n",
        "\n",
        "testing_sentences = [] # same\n",
        "testing_labels = [] # same\n",
        "\n",
        "# str(s.tonumpy()) is needed in Python3 instead of just s.numpy()\n",
        "for s,l in train_data: # here this loop goes till 25000\n",
        "  training_sentences.append(s.numpy().decode('utf8')) # here you use numpy array becoz its inbuild data , so inka alag process hai extracte krnai ka dont go in depth.  \n",
        "  training_labels.append(l.numpy()) # extract labels\n",
        "  \n",
        "for s,l in test_data: # its for test loop will go till 25000 test data\n",
        "  testing_sentences.append(s.numpy().decode('utf8')) # same \n",
        "  testing_labels.append(l.numpy()) # same\n",
        "  \n",
        "training_labels_final = np.array(training_labels) # training labels converts into numpy homogenous single dimesional array or vrctor....\n",
        "testing_labels_final = np.array(testing_labels) # same but its testing \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'test': <DatasetV1Adapter shapes: ((), ()), types: (tf.string, tf.int64)>, 'train': <DatasetV1Adapter shapes: ((), ()), types: (tf.string, tf.int64)>, 'unsupervised': <DatasetV1Adapter shapes: ((), ()), types: (tf.string, tf.int64)>}\n",
            "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": "lQQzqWOcvO19",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# after extract data from inbuild data set of tensorflow and time to preprocessing ,padding ,tokenize\n",
        "# tokenize mean words convert into some  number / mean assign some no to the words that is called tokenaziations. \n",
        "vocab_size = 10000\n",
        "embedding_dim = 16\n",
        "max_length = 120\n",
        "trunc_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "\n",
        "# we knew all about it.\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": {
        "id": "_ZRCxCJ5wKVg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "4b431948-722b-46b9-a84b-d6d8bf8f1afc"
      },
      "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[3]))\n",
        "print(training_sentences[3])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? this is the kind of film for a snowy sunday afternoon when the rest of the world can go ahead with its own business as you <OOV> into a big arm chair and <OOV> for a couple of hours wonderful performances from cher and nicolas cage as always gently row the plot along there are no <OOV> to cross no dangerous waters just a warm and witty <OOV> through new york life at its best a family film in every sense and one that deserves the praise it received\n",
            "This is the kind of film for a snowy Sunday afternoon when the rest of the world can go ahead with its own business as you descend into a big arm-chair and mellow for a couple of hours. Wonderful performances from Cher and Nicolas Cage (as always) gently row the plot along. There are no rapids to cross, no dangerous waters, just a warm and witty paddle through New York life at its best. A family film in every sense and one that deserves the praise it received.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6XuxwWjwSuT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "57fde717-1b8e-4add-d92a-f164ec16a450"
      },
      "source": [
        "# now perform ANN using keras and tensorflow\n",
        "# we all knew about it in ANN practical you can go for it on github and deep learning , how ANN implements.\n",
        "# you can implement ANN like this or in another way using add keywords go and take a look of my implementation from ANN for second way.\n",
        "import tensorflow as tf\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length), # its first emedding layer, this is another type of keras layer , this layer use in NLP\n",
        "    \n",
        "    tf.keras.layers.Flatten(),# its flatten which communicate between simple layer to dense layer and it convert N dimesional array into single D vector , becoz  dense 1 D vector take as input.\n",
        "    \n",
        "    tf.keras.layers.Dense(6, activation='relu'), # its fully connected layer with 6 nureons and relu actiivation fun.\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid') # its output layer with single nuerons using sigmoid function.\n",
        "])\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy']) # here compile this declare and made ANN using loss function and optimizer and metrics\n",
        "# we knew go and take a look ANN implements on github ->deep leaning \n",
        "model.summary() # its all about summery of ANN , look at deeply.\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",
            "flatten (Flatten)            (None, 1920)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 6)                 11526     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 171,533\n",
            "Trainable params: 171,533\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngw-aM6ayKpC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "d6a6f1e9-581c-472e-b58e-8ae586ab8c69"
      },
      "source": [
        "num_epochs = 10\n",
        "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 1/10\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.4886 - accuracy: 0.7466 - val_loss: 0.3383 - val_accuracy: 0.8537\n",
            "Epoch 2/10\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.2414 - accuracy: 0.9070 - val_loss: 0.3635 - val_accuracy: 0.8424\n",
            "Epoch 3/10\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.0980 - accuracy: 0.9741 - val_loss: 0.4349 - val_accuracy: 0.8318\n",
            "Epoch 4/10\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.0251 - accuracy: 0.9963 - val_loss: 0.5138 - val_accuracy: 0.8324\n",
            "Epoch 5/10\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.0061 - accuracy: 0.9996 - val_loss: 0.5797 - val_accuracy: 0.8310\n",
            "Epoch 6/10\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.0021 - accuracy: 0.9999 - val_loss: 0.6314 - val_accuracy: 0.8314\n",
            "Epoch 7/10\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 9.1776e-04 - accuracy: 1.0000 - val_loss: 0.6719 - val_accuracy: 0.8325\n",
            "Epoch 8/10\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 4.9940e-04 - accuracy: 1.0000 - val_loss: 0.7120 - val_accuracy: 0.8322\n",
            "Epoch 9/10\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 2.8729e-04 - accuracy: 1.0000 - val_loss: 0.7488 - val_accuracy: 0.8327\n",
            "Epoch 10/10\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 1.7304e-04 - accuracy: 1.0000 - val_loss: 0.7857 - val_accuracy: 0.8332\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f0b30e672b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bmF4Sxw25vS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}