Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from imp import reload\n",
    "\n",
    "import itertools\n",
    "import numpy as np\n",
    "\n",
    "from __future__ import print_function\n",
    "from sklearn.metrics import confusion_matrix, classification_report, f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Daman\\Python\\envs\\tensorflow-gpu\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "from keras.layers import Embedding, SpatialDropout1D\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Conv1D, GlobalMaxPooling1D\n",
    "from keras.layers import Flatten\n",
    "from keras.datasets import imdb\n",
    "from keras.utils import plot_model\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "from IPython.display import SVG\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Embedding\n",
    "embedding_size = 50\n",
    "max_features = 5000\n",
    "maxlen = 400\n",
    "\n",
    "# Convolution\n",
    "kernel_size = 3\n",
    "pool_size = 4\n",
    "filters = 250\n",
    "\n",
    "# Dense\n",
    "hidden_dims = 250\n",
    "\n",
    "# Training\n",
    "batch_size = 64\n",
    "epochs = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data size: (25000, 400)\n",
      "Test data size: (25000, 400)\n"
     ]
    }
   ],
   "source": [
    "# Pad sequences\n",
    "x_train = sequence.pad_sequences(x_train, maxlen=maxlen)\n",
    "x_test = sequence.pad_sequences(x_test, maxlen=maxlen)\n",
    "\n",
    "print('Train data size:', x_train.shape)\n",
    "print('Test data size:', x_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 400, 50)           250000    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 400, 50)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 398, 250)          37750     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_1 (Glob (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 250)               62750     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 250)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 251       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 350,751\n",
      "Trainable params: 350,751\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# we start off with an efficient embedding layer which maps\n",
    "# our vocab indices into embedding_dims dimensions\n",
    "model.add(Embedding(max_features, \n",
    "                    embedding_size, \n",
    "                    input_length=maxlen))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "\n",
    "model.add(Conv1D(filters,\n",
    "                kernel_size,\n",
    "                padding='valid',\n",
    "                activation='relu',\n",
    "                strides=1))\n",
    "model.add(GlobalMaxPooling1D())\n",
    "\n",
    "# We add a vanilla hidden layer:\n",
    "model.add(Dense(hidden_dims))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# We project onto a single unit output layer, and squash it with a sigmoid:\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "# plot_model(model, to_file='model.png', show_shapes=True)\n",
    "# Image(filename = 'model.png')\n",
    "# SVG(model_to_dot(model).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/4\n",
      "25000/25000 [==============================] - 155s 6ms/step - loss: 0.4405 - acc: 0.7730 - val_loss: 0.2900 - val_acc: 0.8789\n",
      "Epoch 2/4\n",
      "25000/25000 [==============================] - 7s 267us/step - loss: 0.2414 - acc: 0.9009 - val_loss: 0.2562 - val_acc: 0.8937\n",
      "Epoch 3/4\n",
      "25000/25000 [==============================] - 7s 261us/step - loss: 0.1674 - acc: 0.9366 - val_loss: 0.2755 - val_acc: 0.8895\n",
      "Epoch 4/4\n",
      "25000/25000 [==============================] - 7s 263us/step - loss: 0.1140 - acc: 0.9606 - val_loss: 0.2934 - val_acc: 0.8903\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x19466257ac8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "model.fit(x_train, y_train,\n",
    "         batch_size=batch_size,\n",
    "         epochs=epochs,\n",
    "         validation_data=(x_test, y_test),\n",
    "         verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000/25000 [==============================] - 1s 53us/step\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Evaluate model# Evalua \n",
    "score, acc = model.evaluate(x_test, y_test, batch_size=batch_size)\n",
    "preds = model.predict_classes(x_test, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "Unable to create file (unable to open file: name = 'data/imdb/models/cnn_model.h5', errno = 2, error message = 'No such file or directory', flags = 13, o_flags = 302)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-4fe21c94356f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Save the model weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mmodel_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'data/imdb/models/'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_path\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'cnn_model.h5'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_path\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'cnn_weights.h5'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Python\\envs\\tensorflow-gpu\\lib\\site-packages\\keras\\engine\\topology.py\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, filepath, overwrite, include_optimizer)\u001b[0m\n\u001b[0;32m   2574\u001b[0m         \"\"\"\n\u001b[0;32m   2575\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msave_model\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2576\u001b[1;33m         \u001b[0msave_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minclude_optimizer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2577\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2578\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0msave_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Python\\envs\\tensorflow-gpu\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36msave_model\u001b[1;34m(model, filepath, overwrite, include_optimizer)\u001b[0m\n\u001b[0;32m    104\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    105\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 106\u001b[1;33m     \u001b[1;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'w'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    107\u001b[0m         \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'keras_version'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeras_version\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'utf8'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m         \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'backend'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'utf8'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Python\\envs\\tensorflow-gpu\\lib\\site-packages\\h5py\\_hl\\files.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, name, mode, driver, libver, userblock_size, swmr, **kwds)\u001b[0m\n\u001b[0;32m    267\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mphil\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    268\u001b[0m                 \u001b[0mfapl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_fapl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdriver\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlibver\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 269\u001b[1;33m                 \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_fid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muserblock_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mswmr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mswmr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    270\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    271\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Python\\envs\\tensorflow-gpu\\lib\\site-packages\\h5py\\_hl\\files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[1;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[0;32m    103\u001b[0m         \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mACC_EXCL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfcpl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'w'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 105\u001b[1;33m         \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mACC_TRUNC\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfcpl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    106\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'a'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    107\u001b[0m         \u001b[1;31m# Open in append mode (read/write).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mh5py\\h5f.pyx\u001b[0m in \u001b[0;36mh5py.h5f.create\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mOSError\u001b[0m: Unable to create file (unable to open file: name = 'data/imdb/models/cnn_model.h5', errno = 2, error message = 'No such file or directory', flags = 13, o_flags = 302)"
     ]
    }
   ],
   "source": [
    "# Save the model weights\n",
    "model_path = 'data/imdb/models/'\n",
    "model.save(model_path + 'cnn_model.h5')\n",
    "model.save_weights(model_path + 'cnn_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11002,  1498],\n",
       "       [ 1244, 11256]], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Confusion Matrix# Confus \n",
    "cm = confusion_matrix(y_test, preds)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.8903200000381469\n",
      "Test score (loss): 0.29339576820373536\n"
     ]
    }
   ],
   "source": [
    "print('Test accuracy:', acc)\n",
    "print('Test score (loss):', score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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