Chapter 22 Predict sentiment from movie reviews.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "from keras.datasets import imdb\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/text-datasets/imdb.npz\n",
      "17465344/17464789 [==============================] - 20s 1us/step\n"
     ]
    }
   ],
   "source": [
    "(X_train,y_train),(X_test,y_test) = imdb.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = numpy.concatenate((X_train,X_test),axis=0)\n",
    "y = numpy.concatenate((y_train,y_test),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data: \n",
      "(50000,)\n",
      "(50000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data: \")\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes: \n",
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "print(\"Classes: \")\n",
    "print(numpy.unique(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of words: \n",
      "88585\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of words: \")\n",
    "print(len(numpy.unique(numpy.hstack(X))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Review length: \n",
      "Mean 234.76 words (172.911495)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Review length: \")\n",
    "result = map(len,X)\n",
    "h = list(result)\n",
    "print(\"Mean %.2f words (%f)\" %(numpy.mean(h),numpy.std(h)))\n",
    "pyplot.subplot(121)\n",
    "pyplot.boxplot(h)\n",
    "pyplot.subplot(122)\n",
    "pyplot.hist(h)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simple multilayer perceptron model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "from keras.datasets import imdb\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.preprocessing import sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 7\n",
    "numpy.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arvind/.local/lib/python3.6/site-packages/keras/datasets/imdb.py:49: UserWarning: The `nb_words` argument in `load_data` has been renamed `num_words`.\n",
      "  warnings.warn('The `nb_words` argument in `load_data` '\n"
     ]
    }
   ],
   "source": [
    "top_words = 5000\n",
    "test_split=0.33\n",
    "(X_train,y_train),(X_test,y_test) = imdb.load_data(nb_words=top_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_words = 500\n",
    "X_train = sequence.pad_sequences(X_train,maxlen=max_words)\n",
    "X_test = sequence.pad_sequences(X_test,maxlen=max_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0426 14:47:49.865401 140610536482624 deprecation.py:506] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1633: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "W0426 14:47:50.497166 140610536482624 deprecation.py:323] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/nn_impl.py:183: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 500, 32)           160000    \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 16000)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 250)               4000250   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 251       \n",
      "=================================================================\n",
      "Total params: 4,160,501\n",
      "Trainable params: 4,160,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(top_words,32,input_length=max_words))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(250,activation=\"relu\"))\n",
    "model.add(Dense(1,activation=\"sigmoid\"))\n",
    "model.compile(loss = \"binary_crossentropy\",optimizer = \"adam\",metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0426 14:49:21.887778 140610536482624 deprecation.py:323] From /home/arvind/.local/lib/python3.6/site-packages/keras/optimizers.py:550: BaseResourceVariable.constraint (from tensorflow.python.ops.resource_variable_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Apply a constraint manually following the optimizer update step.\n",
      "W0426 14:49:24.659198 140610536482624 module_wrapper.py:136] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py:163: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/2\n",
      "25000/25000 [==============================] - 20s 804us/step - loss: 0.4825 - accuracy: 0.7349 - val_loss: 0.2928 - val_accuracy: 0.8754\n",
      "Epoch 2/2\n",
      "25000/25000 [==============================] - 19s 777us/step - loss: 0.1727 - accuracy: 0.9341 - val_loss: 0.3192 - val_accuracy: 0.8660\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7fe0abc16c18>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train,validation_data = (X_test,y_test),epochs = 2,batch_size=128,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = model.evaluate(X_test,y_test,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 86.60%\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: %.2f%%\" %(scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1D Convolutional neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "from keras.datasets import imdb\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "from keras.layers.convolutional import Convolution1D\n",
    "from keras.layers.convolutional import MaxPooling1D\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.preprocessing import sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 7\n",
    "numpy.random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/arvind/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(padding=\"same\", activation=\"relu\", filters=32, kernel_size=3)`\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "W0426 15:02:30.929549 140610536482624 module_wrapper.py:136] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py:163: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 500, 32)           160000    \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 500, 32)           3104      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 250, 32)           0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 8000)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 250)               2000250   \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 251       \n",
      "=================================================================\n",
      "Total params: 2,163,605\n",
      "Trainable params: 2,163,605\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(top_words,32,input_length=max_words))\n",
    "model.add(Convolution1D(nb_filter=32,filter_length=3,padding='same',activation=\"relu\"))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(250,activation=\"relu\"))\n",
    "model.add(Dense(1,activation=\"sigmoid\"))\n",
    "model.compile(loss=\"binary_crossentropy\",optimizer = \"adam\",metrics=[\"accuracy\"])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/2\n",
      "25000/25000 [==============================] - 20s 818us/step - loss: 0.4346 - accuracy: 0.7725 - val_loss: 0.2778 - val_accuracy: 0.8846\n",
      "Epoch 2/2\n",
      "25000/25000 [==============================] - 20s 799us/step - loss: 0.2079 - accuracy: 0.9180 - val_loss: 0.2954 - val_accuracy: 0.8763\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7fe211ae1208>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train, validation_data = (X_test,y_test),epochs = 2,batch_size = 128,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = model.evaluate(X_test,y_test,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 87.63%\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: %.2f%%\"%(scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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