NLP_Course_Week_3_Exercise_Question_Different_RNNs.ipynb

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      "name": "NLP Course - Week 3 Exercise Question.ipynb",
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      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EgorBEremeev/SoloLearmML-coursera-deeplearning.ai/blob/master/NLP_Course_Week_3_Exercise_Question_Different_RNNs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmA6EzkQJ5jt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import json\n",
        "import tensorflow as tf\n",
        "import csv\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras import regularizers\n",
        "\n",
        "\n",
        "embedding_dim = 100\n",
        "max_length = 32\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "training_size= 16000#Your dataset size here. Experiment using smaller values (i.e. 16000), but don't forget to train on at least 160000 to see the best effects\n",
        "test_portion=.1\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bM0l_dORKqE0",
        "colab_type": "code",
        "outputId": "bb60d798-1661-4b87-e193-1c4f9638a915",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "\n",
        "# Note that I cleaned the Stanford dataset to remove LATIN1 encoding to make it easier for Python CSV reader\n",
        "# You can do that yourself with:\n",
        "# iconv -f LATIN1 -t UTF8 training.1600000.processed.noemoticon.csv -o training_cleaned.csv\n",
        "# I then hosted it on my site to make it easier to use in this notebook\n",
        "\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/training_cleaned.csv \\\n",
        "    -O /tmp/training_cleaned.csv\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-08-11 12:55:24--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/training_cleaned.csv\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.204.128, 2404:6800:4008:c07::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.204.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 238942690 (228M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/training_cleaned.csv’\n",
            "\n",
            "/tmp/training_clean 100%[===================>] 227.87M   109MB/s    in 2.1s    \n",
            "\n",
            "2019-08-11 12:55:27 (109 MB/s) - ‘/tmp/training_cleaned.csv’ saved [238942690/238942690]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bh891EvIRdDM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corpus =[]\n",
        "num_sentences = 0\n",
        "\n",
        "with open(\"/tmp/training_cleaned.csv\") as csvfile:\n",
        "    reader = csv.reader(csvfile, delimiter=',')\n",
        "    #print(next(reader))\n",
        "    for row in reader:\n",
        "      # Your Code here. Create list items where the first item is the text, found in row[5], and the second is the label. Note that the label is a '0' or a '4' in the text. When it's the former, make\n",
        "      # your label to be 0, otherwise 1. Keep a count of the number of sentences in num_sentences\n",
        "        list_item=[]\n",
        "        list_item.append(row[5])\n",
        "        this_label=row[0]\n",
        "        if this_label=='0':\n",
        "            list_item.append(0)\n",
        "        else:\n",
        "            list_item.append(1)\n",
        "        num_sentences = num_sentences + 1\n",
        "        corpus.append(list_item)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kxblBUjEUX-",
        "colab_type": "code",
        "outputId": "7f41bd39-75f1-4bf5-b5b4-9e0668b65c9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "print(num_sentences)\n",
        "print(len(corpus))\n",
        "print(corpus[1])\n",
        "print(corpus[1][0])\n",
        "\n",
        "# Expected Output:\n",
        "# 1600000\n",
        "# 1600000\n",
        "# [\"is upset that he can't update his Facebook by texting it... and might cry as a result  School today also. Blah!\", 0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1600000\n",
            "1600000\n",
            "[\"is upset that he can't update his Facebook by texting it... and might cry as a result  School today also. Blah!\", '0']\n",
            "is upset that he can't update his Facebook by texting it... and might cry as a result  School today also. Blah!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ohOGz24lsNAD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sentences=[]\n",
        "labels=[]\n",
        "random.shuffle(corpus)\n",
        "for x in range(training_size):\n",
        "    sentences.append(corpus[x][0])\n",
        "    labels.append(corpus[x][1])\n",
        "\n",
        "\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(sentences)\n",
        "\n",
        "word_index = tokenizer.word_index\n",
        "vocab_size=len(word_index)\n",
        "\n",
        "sequences = tokenizer.texts_to_sequences(sentences)\n",
        "padded = pad_sequences(sequences, maxlen=max_length, padding=padding_type, truncating=trunc_type)\n",
        "\n",
        "split = int(test_portion * training_size)\n",
        "\n",
        "test_sequences = padded[0:split]\n",
        "training_sequences = padded[split:]\n",
        "test_labels = labels[0:split]\n",
        "training_labels = labels[split:]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gIrtRem1En3N",
        "colab_type": "code",
        "outputId": "618e90d6-cdda-4b4b-bb25-eca12f62f5f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "print(vocab_size)\n",
        "print(word_index['i'])\n",
        "print(training_sequences.shape)\n",
        "print(len(training_sequences))\n",
        "# Expected Output\n",
        "# 138858\n",
        "# 1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "26717\n",
            "1\n",
            "(14400, 32)\n",
            "14400\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1zdgJkusRh0",
        "colab_type": "code",
        "outputId": "f60bccb1-ecc2-42ef-ed96-81c796693503",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "# Note this is the 100 dimension version of GloVe from Stanford\n",
        "# I unzipped and hosted it on my site to make this notebook easier\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/glove.6B.100d.txt \\\n",
        "    -O /tmp/glove.6B.100d.txt\n",
        "embeddings_index = {};\n",
        "with open('/tmp/glove.6B.100d.txt') as f:\n",
        "    for line in f:\n",
        "        values = line.split();\n",
        "        word = values[0];\n",
        "        coefs = np.asarray(values[1:], dtype='float32');\n",
        "        embeddings_index[word] = coefs;\n",
        "\n",
        "embeddings_matrix = np.zeros((vocab_size+1, embedding_dim));\n",
        "for word, i in word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word);\n",
        "    if embedding_vector is not None:\n",
        "        embeddings_matrix[i] = embedding_vector;"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-08-11 12:55:38--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/glove.6B.100d.txt\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 64.233.189.128, 2404:6800:4008:c07::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|64.233.189.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 347116733 (331M) [text/plain]\n",
            "Saving to: ‘/tmp/glove.6B.100d.txt’\n",
            "\n",
            "/tmp/glove.6B.100d. 100%[===================>] 331.04M  99.5MB/s    in 3.3s    \n",
            "\n",
            "2019-08-11 12:55:42 (99.5 MB/s) - ‘/tmp/glove.6B.100d.txt’ saved [347116733/347116733]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "71NLk_lpFLNt",
        "colab_type": "code",
        "outputId": "ef46b97c-19df-4f63-be1b-90a4d8ae3e6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "print(len(embeddings_matrix))\n",
        "print(embeddings_matrix.shape)\n",
        "# Expected Output\n",
        "# 138859"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "26916\n",
            "(26916, 100)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iKKvbuEBOGFz",
        "outputId": "4034838d-3df1-46b7-a1b3-1e08a725e9bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size+1, embedding_dim, input_length=max_length, weights=[embeddings_matrix], trainable=False),\n",
        "    # YOUR CODE HERE - experiment with combining different types, such as convolutions and LSTMs\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Conv1D(64, 3, activation='relu'),\n",
        "    #tf.keras.layers.GlobalMaxPooling1D(),\n",
        "    #tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.MaxPool1D(pool_size=4),\n",
        "    #tf.keras.layers.GRU(32,recurrent_dropout=0.2),\n",
        "    tf.keras.layers.LSTM(64, return_sequences=True, unroll=True),\n",
        "    tf.keras.layers.LSTM(32, unroll=True),\n",
        "    #tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(16)),\n",
        "    #tf.keras.layers.Dense(256, activation='relu'),\n",
        "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics=['acc'])\n",
        "model.summary()\n",
        "\n",
        "num_epochs = 10\n",
        "history = model.fit(training_sequences, training_labels, epochs=num_epochs, validation_data=(test_sequences, test_labels), verbose=2)\n",
        "\n",
        "print(\"Training Complete\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_33\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_37 (Embedding)     (None, 32, 100)           2691600   \n",
            "_________________________________________________________________\n",
            "dropout_23 (Dropout)         (None, 32, 100)           0         \n",
            "_________________________________________________________________\n",
            "conv1d_21 (Conv1D)           (None, 30, 64)            19264     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_14 (MaxPooling (None, 7, 64)             0         \n",
            "_________________________________________________________________\n",
            "lstm_21 (LSTM)               (None, 7, 64)             33024     \n",
            "_________________________________________________________________\n",
            "lstm_22 (LSTM)               (None, 32)                12416     \n",
            "_________________________________________________________________\n",
            "dense_46 (Dense)             (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 2,756,337\n",
            "Trainable params: 64,737\n",
            "Non-trainable params: 2,691,600\n",
            "_________________________________________________________________\n",
            "Train on 14400 samples, validate on 1600 samples\n",
            "Epoch 1/10\n",
            "14400/14400 - 15s - loss: 0.6198 - acc: 0.6490 - val_loss: 0.5569 - val_acc: 0.7262\n",
            "Epoch 2/10\n",
            "14400/14400 - 9s - loss: 0.5502 - acc: 0.7194 - val_loss: 0.5353 - val_acc: 0.7431\n",
            "Epoch 3/10\n",
            "14400/14400 - 9s - loss: 0.5244 - acc: 0.7390 - val_loss: 0.5337 - val_acc: 0.7312\n",
            "Epoch 4/10\n",
            "14400/14400 - 9s - loss: 0.5040 - acc: 0.7524 - val_loss: 0.5373 - val_acc: 0.7419\n",
            "Epoch 5/10\n",
            "14400/14400 - 9s - loss: 0.4886 - acc: 0.7618 - val_loss: 0.5140 - val_acc: 0.7538\n",
            "Epoch 6/10\n",
            "14400/14400 - 9s - loss: 0.4625 - acc: 0.7795 - val_loss: 0.5281 - val_acc: 0.7500\n",
            "Epoch 7/10\n",
            "14400/14400 - 9s - loss: 0.4471 - acc: 0.7856 - val_loss: 0.5442 - val_acc: 0.7319\n",
            "Epoch 8/10\n",
            "14400/14400 - 9s - loss: 0.4321 - acc: 0.7940 - val_loss: 0.5159 - val_acc: 0.7525\n",
            "Epoch 9/10\n",
            "14400/14400 - 9s - loss: 0.4173 - acc: 0.8044 - val_loss: 0.5243 - val_acc: 0.7519\n",
            "Epoch 10/10\n",
            "14400/14400 - 9s - loss: 0.4089 - acc: 0.8081 - val_loss: 0.5324 - val_acc: 0.7412\n",
            "Training Complete\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxju4ItJKO8F",
        "colab_type": "code",
        "outputId": "e8b4d78d-b04c-466f-fbf0-8c5f1f17e013",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "import matplotlib.image  as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#-----------------------------------------------------------\n",
        "# Retrieve a list of list results on training and test data\n",
        "# sets for each training epoch\n",
        "#-----------------------------------------------------------\n",
        "acc=history.history['acc']\n",
        "val_acc=history.history['val_acc']\n",
        "loss=history.history['loss']\n",
        "val_loss=history.history['val_loss']\n",
        "\n",
        "epochs=range(len(acc)) # Get number of epochs\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation accuracy per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, acc, 'r')\n",
        "plt.plot(epochs, val_acc, 'b')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend([\"Accuracy\", \"Validation Accuracy\"])\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "#------------------------------------------------\n",
        "# Plot training and validation loss per epoch\n",
        "#------------------------------------------------\n",
        "plt.plot(epochs, loss, 'r')\n",
        "plt.plot(epochs, val_loss, 'b')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend([\"Loss\", \"Validation Loss\"])\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "\n",
        "# Expected Output\n",
        "# A chart where the validation loss does not increase sharply!"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AG1R1I4CITAX6AXE5nD8YeDyb/QOAeap6MCRRGpMXVddtd+RIN2HilVfCc89Z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\nxhTI/wOJxks6XTQhAQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lf54sCitVmxeif3+bwnPiRKhbl337YNGiQ9VYKdvmzXZa0aI2BUN8vE0lccYZ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}