NLP_ASSIGNMENT3.ipynb

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  "metadata": {
    "colab": {
      "name": "NLP_ASSIGNMENT3.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jPCILYFpT_r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 82
        },
        "outputId": "9884630b-4af9-450a-d101-24c5f6aa5609"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from keras.utils import to_categorical\n",
        "from numpy import array\n",
        "import keras\n",
        "from keras import regularizers\n",
        "from keras.preprocessing.text import one_hot\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.models import Sequential\n",
        "from keras.layers.recurrent import LSTM\n",
        "from keras.layers import Conv1D,GlobalMaxPooling1D\n",
        "from keras.layers.core import Activation, Dropout, Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import GlobalMaxPooling1D, MaxPooling1D\n",
        "from keras.layers.embeddings import Embedding\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from numpy import array\n",
        "from numpy import asarray\n",
        "from numpy import zeros\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from keras.models import Model\n",
        "from keras.layers import LSTM, Activation, Dense, Dropout, Input, Embedding\n",
        "from keras.optimizers import Adam\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing import sequence\n",
        "from keras.utils import to_categorical\n",
        "from keras.callbacks import EarlyStopping\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fiIwsBJJPtMl",
        "colab_type": "code",
        "outputId": "1ddd4ea6-2804-4078-b0f2-9fe171fb18f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_fY-RU9Py7X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.chdir('drive/My Drive/nlp assignment 3')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dPcDk_97r9-E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Preprocessing Train and Test data.\n",
        "a = open('train.txt')\n",
        "b = open('test.txt')\n",
        "sentences = []\n",
        "test_sentences = []\n",
        "train_text = a.readlines()\n",
        "test_text = b.readlines()\n",
        "sentence = [train_text[0].split()[2]]\n",
        "test_sentence = [test_text[0].split()[2]]\n",
        "test_sentence = [test_text[0].split()[2]]\n",
        "for line in test_text[1:]:\n",
        "  ls = line.split()\n",
        "  try:\n",
        "    # print(ls)\n",
        "    if ls[0]=='meta':\n",
        "      test_sentences.append(test_sentence)\n",
        "      test_sentence = [ls[2]]\n",
        "    else:\n",
        "      test_sentence.append([ls[0], ls[1]])\n",
        "  except:\n",
        "    continue\n",
        "for line in train_text[1:]:\n",
        "  # print(line.split())\n",
        "  ls = line.split()\n",
        "  try:\n",
        "    # print(ls)\n",
        "    if ls[0]=='meta':\n",
        "      sentences.append(sentence)\n",
        "      sentence = [ls[2]]\n",
        "    else:\n",
        "      sentence.append([ls[0], ls[1]])\n",
        "  except:\n",
        "    continue\n",
        "a.close()\n",
        "b.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TEPRqIUDEuJt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "hin_dict = set()\n",
        "s = \"\"\n",
        "translated_sentences = list(sentences)\n",
        "y_train = []\n",
        "x_train = []\n",
        "# translations = translator.translate([s], dest='en')\n",
        "for sen_num,sentence in enumerate(sentences):\n",
        "  # print(sentence[0])\n",
        "  # break\n",
        "  if sentence[0]==\"positive\":\n",
        "    y_train.append(0)\n",
        "  elif sentence[0]==\"negative\":\n",
        "    y_train.append(1)\n",
        "  elif sentence[0]==\"neutral\":\n",
        "    y_train.append(2)\n",
        "  new_sentence = \"\"\n",
        "  for i in (sentence[1:]):\n",
        "    if i[1]==\"Hin\":\n",
        "        new_sentence += i[0]+\" \"\n",
        "    else:  \n",
        "      new_sentence += i[0]+\" \"\n",
        "  x_train.append(new_sentence)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8puOxdf95JQc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "s = \"\"\n",
        "test_translated_sentences = list(test_sentences)\n",
        "y_test = []\n",
        "x_test = []\n",
        "for sen_num,sentence in enumerate(test_sentences):\n",
        "  if sentence[0]==\"positive\":\n",
        "    y_test.append(0)\n",
        "  elif sentence[0]==\"negative\":\n",
        "    y_test.append(1)\n",
        "  elif sentence[0]==\"neutral\":\n",
        "    y_test.append(2)\n",
        "  new_sentence = \"\"\n",
        "  for i in (sentence[1:]):\n",
        "    if i[1]==\"Hin\":\n",
        "        hin_dict.add(i[0])\n",
        "        new_sentence += i[0]+\" \"\n",
        "    else:  \n",
        "      new_sentence += i[0]+\" \"\n",
        "  x_test.append(new_sentence)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BoU6Y1YsN8eh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re\n",
        "def preprocess(sentences):\n",
        "  # for sentence in sentences:\n",
        "  new_sentences = list(sentences)\n",
        "  for ct,sentence in enumerate(sentences):\n",
        "    sentence = re.sub(r'[#,@,!,.,_,…,RT]*', \"\", sentence)\n",
        "    sentence = sentence.lower()\n",
        "    new_sentences[ct] = sentence\n",
        "    for letter in range(len(sentence)):\n",
        "      try:\n",
        "        if sentence[letter:letter+4]=='http':\n",
        "          print()\n",
        "          new_sentences[ct] = sentence[:letter]\n",
        "          break\n",
        "      except:\n",
        "        break\n",
        "  return new_sentences"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lz-rS547J66U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8af9a309-6e9b-4088-c03a-bbae201c75be"
      },
      "source": [
        "x_train = preprocess(x_train)\n",
        "x_test = preprocess(x_test)\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3mHuIOdn_Qy3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "max_words = 1000\n",
        "max_len = 100\n",
        "tokenizer_train = Tokenizer(num_words=5000)\n",
        "tokenizer_train.fit_on_texts(x_train)\n",
        "orig_y_test = y_test[:]\n",
        "y_train = to_categorical(y_train, 3)\n",
        "y_test = to_categorical(y_test, 3)\n",
        "x_train = tokenizer_train.texts_to_sequences(x_train)\n",
        "x_test = tokenizer_train.texts_to_sequences(x_test)\n",
        "# sequences = tok.texts_to_sequences(X_train)\n",
        "# sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDzHwUlX_tyA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a0fb7b67-3103-45c6-fdf7-167e5ae698d1"
      },
      "source": [
        "x_test[0]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[36, 1312, 46, 59, 3619, 3892, 428, 4733, 154, 2510, 72, 14, 3839, 2161, 69]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1iniyjeJnGk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vocab_size_train = len(tokenizer_train.word_index) + 1\n",
        "\n",
        "maxlen = 100\n",
        "\n",
        "x_train = pad_sequences(x_train, padding='post', maxlen=maxlen)\n",
        "x_test = pad_sequences(x_test, padding='post', maxlen=maxlen)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYuCD09hfWjE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "fe19051a-a345-4d34-92ed-0572b91b67fb"
      },
      "source": [
        "x_test[1]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  16,   16,   16,   53,  979,  709, 1642, 1324,   59,   19, 2384,\n",
              "        453,   87,  453,   87,  453,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
              "          0], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "URXhdfoQJECY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embeddings_dictionary_train = dict()\n",
        "glove_file = open('glove.6B.100d.txt', encoding=\"utf8\")\n",
        "\n",
        "for line in glove_file:\n",
        "    records = line.split()\n",
        "    word = records[0]\n",
        "    vector_dimensions = asarray(records[1:], dtype='float32')\n",
        "    embeddings_dictionary_train [word] = vector_dimensions\n",
        "glove_file.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S9hks7NyJHP6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embedding_matrix_train = zeros((vocab_size_train, 100))\n",
        "for word, index in tokenizer_train.word_index.items():\n",
        "    embedding_vector = embeddings_dictionary_train.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix_train[index] = embedding_vector"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2fDp-ocJKIK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "afa322af-7369-43ef-b800-b653a4a31081"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(vocab_size_train, 100, weights=[embedding_matrix_train], input_length=maxlen , trainable=True))\n",
        "model.add(Conv1D(128, 5, activation='relu'))\n",
        "model.add(Conv1D(64, 10, activation='relu'))\n",
        "model.add(MaxPooling1D())\n",
        "model.add(Conv1D(32, 10, activation='relu'))\n",
        "model.add(GlobalMaxPooling1D())\n",
        "model.add(Dense(128, activation='relu',kernel_regularizer=regularizers.l2(0.05)))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(64, activation='relu',kernel_regularizer=regularizers.l2(0.05)))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(16, activation='relu',kernel_regularizer=regularizers.l2(0.05)))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(3, activation='softmax'))\n",
        "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['acc'])\n",
        "model.summary()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 100, 100)          4467300   \n",
            "_________________________________________________________________\n",
            "conv1d_1 (Conv1D)            (None, 96, 128)           64128     \n",
            "_________________________________________________________________\n",
            "conv1d_2 (Conv1D)            (None, 87, 64)            81984     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_1 (MaxPooling1 (None, 43, 64)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_3 (Conv1D)            (None, 34, 32)            20512     \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d_1 (Glob (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               4224      \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 16)                1040      \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 3)                 51        \n",
            "=================================================================\n",
            "Total params: 4,647,495\n",
            "Trainable params: 4,647,495\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "85pzFa_sJNhF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "outputId": "3f5e6967-7a66-43ff-a3dc-b8616650c6dd"
      },
      "source": [
        "history = model.fit(x_train ,y_train, batch_size=64, epochs=20, verbose=1, validation_data = (x_test, y_test))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Train on 15130 samples, validate on 1868 samples\n",
            "Epoch 1/20\n",
            "15130/15130 [==============================] - 7s 461us/step - loss: 2.6810 - acc: 0.4336 - val_loss: 1.0655 - val_acc: 0.4764\n",
            "Epoch 2/20\n",
            "15130/15130 [==============================] - 4s 258us/step - loss: 0.9882 - acc: 0.5253 - val_loss: 1.0120 - val_acc: 0.5080\n",
            "Epoch 3/20\n",
            "15130/15130 [==============================] - 4s 265us/step - loss: 0.9241 - acc: 0.5751 - val_loss: 0.9887 - val_acc: 0.5268\n",
            "Epoch 4/20\n",
            "15130/15130 [==============================] - 4s 257us/step - loss: 0.8822 - acc: 0.6085 - val_loss: 1.0583 - val_acc: 0.5294\n",
            "Epoch 5/20\n",
            "15130/15130 [==============================] - 4s 264us/step - loss: 0.8424 - acc: 0.6320 - val_loss: 1.0488 - val_acc: 0.5412\n",
            "Epoch 6/20\n",
            "15130/15130 [==============================] - 4s 261us/step - loss: 0.8202 - acc: 0.6539 - val_loss: 1.1050 - val_acc: 0.5530\n",
            "Epoch 7/20\n",
            "15130/15130 [==============================] - 4s 254us/step - loss: 0.7826 - acc: 0.6816 - val_loss: 1.1000 - val_acc: 0.5605\n",
            "Epoch 8/20\n",
            "15130/15130 [==============================] - 4s 257us/step - loss: 0.7472 - acc: 0.7005 - val_loss: 1.3144 - val_acc: 0.5482\n",
            "Epoch 9/20\n",
            "15130/15130 [==============================] - 4s 259us/step - loss: 0.6970 - acc: 0.7348 - val_loss: 1.2304 - val_acc: 0.5385\n",
            "Epoch 10/20\n",
            "15130/15130 [==============================] - 4s 257us/step - loss: 0.6587 - acc: 0.7545 - val_loss: 1.2691 - val_acc: 0.5525\n",
            "Epoch 11/20\n",
            "15130/15130 [==============================] - 4s 254us/step - loss: 0.6078 - acc: 0.7924 - val_loss: 1.6087 - val_acc: 0.5428\n",
            "Epoch 12/20\n",
            "15130/15130 [==============================] - 4s 250us/step - loss: 0.5594 - acc: 0.8213 - val_loss: 1.5914 - val_acc: 0.5412\n",
            "Epoch 13/20\n",
            "15130/15130 [==============================] - 4s 255us/step - loss: 0.5171 - acc: 0.8379 - val_loss: 1.5934 - val_acc: 0.5332\n",
            "Epoch 14/20\n",
            "15130/15130 [==============================] - 4s 254us/step - loss: 0.4758 - acc: 0.8593 - val_loss: 1.6576 - val_acc: 0.5353\n",
            "Epoch 15/20\n",
            "15130/15130 [==============================] - 4s 252us/step - loss: 0.4413 - acc: 0.8749 - val_loss: 1.8851 - val_acc: 0.5070\n",
            "Epoch 16/20\n",
            "15130/15130 [==============================] - 4s 254us/step - loss: 0.4090 - acc: 0.8904 - val_loss: 2.1999 - val_acc: 0.5187\n",
            "Epoch 17/20\n",
            "15130/15130 [==============================] - 4s 256us/step - loss: 0.3736 - acc: 0.9040 - val_loss: 2.0656 - val_acc: 0.5080\n",
            "Epoch 18/20\n",
            "15130/15130 [==============================] - 4s 255us/step - loss: 0.3489 - acc: 0.9180 - val_loss: 1.9273 - val_acc: 0.5182\n",
            "Epoch 19/20\n",
            "15130/15130 [==============================] - 4s 257us/step - loss: 0.3311 - acc: 0.9247 - val_loss: 2.4474 - val_acc: 0.5284\n",
            "Epoch 20/20\n",
            "15130/15130 [==============================] - 4s 254us/step - loss: 0.3133 - acc: 0.9264 - val_loss: 2.1963 - val_acc: 0.5225\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wSNjQoLLoEL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "31296d33-d7d8-4489-a301-3fc05f130e3a"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "y_pred=np.argmax(model.predict(x_test),axis=1)\n",
        "print(classification_report(orig_y_test,y_pred))\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.58      0.48      0.52       582\n",
            "           1       0.55      0.48      0.51       532\n",
            "           2       0.48      0.59      0.53       754\n",
            "\n",
            "    accuracy                           0.52      1868\n",
            "   macro avg       0.54      0.51      0.52      1868\n",
            "weighted avg       0.53      0.52      0.52      1868\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_O-J9lkNjrF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a149ba5d-fa0a-40b7-e44a-5330f58a11d2"
      },
      "source": [
        "from IPython.display import SVG\n",
        "from keras.utils import model_to_dot\n",
        "\n",
        "SVG(model_to_dot(model).create(prog='dot', format='svg'))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2Xc0-8IZI5R",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "0d52060f-8cfa-46e4-bc04-b7993852f168"
      },
      "source": [
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot training & validation loss values\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
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+/cU2Xvx6Gy4X3DI4lV9f0IXYptoSqFQw82QewePAU8aYQud2LHCfMeZBbwen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\nESilVIjTRKCUUiFOE4FSSoW4/wfC99sjH8RWWwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pd+G+afDKd6BqR/hiaDkMe96H938Lr99lG4dnXh6+51dqCNASQQ/SkuJJ98Zr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\nUirGaSJQSqkYp4lAKaVinCYCpZSKcf8fPyZjzeOlMo4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}