imdb_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import imdb\n",
    "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
    "num_words=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "word_index = imdb.get_word_index()\n",
    "reverse_word_index = dict(\n",
    "[(value, key) for (key, value) in word_index.items()])\n",
    "decoded_review = ' '.join(\n",
    "[reverse_word_index.get(i - 3, '?') for i in train_data[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\n"
     ]
    }
   ],
   "source": [
    "print(decoded_review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def vectorize_sequences(sequences, dimension=10000):\n",
    "    results = np.zeros((len(sequences), dimension))\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        results[i, sequence] = 1.\n",
    "        return results\n",
    "x_train = vectorize_sequences(train_data)\n",
    "x_test = vectorize_sequences(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 1., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.asarray(train_labels).astype('float32')\n",
    "y_test = np.asarray(test_labels).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(16, activation='relu', input_shape=(10000,)))\n",
    "model.add(layers.Dense(16, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_val = x_train[:10000]\n",
    "partial_x_train = x_train[10000:]\n",
    "y_val = y_train[:10000]\n",
    "partial_y_train = y_train[10000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 15000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "15000/15000 [==============================] - 5s 313us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n",
      "Epoch 2/20\n",
      "15000/15000 [==============================] - 4s 267us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n",
      "Epoch 3/20\n",
      "15000/15000 [==============================] - 4s 251us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n",
      "Epoch 4/20\n",
      "15000/15000 [==============================] - 4s 287us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n",
      "Epoch 5/20\n",
      "15000/15000 [==============================] - 5s 318us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6932 - val_accuracy: 0.4947\n",
      "Epoch 6/20\n",
      "15000/15000 [==============================] - 5s 307us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n",
      "Epoch 7/20\n",
      "15000/15000 [==============================] - 4s 242us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n",
      "Epoch 8/20\n",
      "15000/15000 [==============================] - 4s 242us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n",
      "Epoch 9/20\n",
      "15000/15000 [==============================] - 4s 241us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n",
      "Epoch 10/20\n",
      "15000/15000 [==============================] - 4s 243us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n",
      "Epoch 11/20\n",
      "15000/15000 [==============================] - 4s 242us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6932 - val_accuracy: 0.4947\n",
      "Epoch 12/20\n",
      "15000/15000 [==============================] - 4s 287us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6932 - val_accuracy: 0.4947\n",
      "Epoch 13/20\n",
      "15000/15000 [==============================] - 5s 330us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6932 - val_accuracy: 0.4947\n",
      "Epoch 14/20\n",
      "15000/15000 [==============================] - 4s 289us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6932 - val_accuracy: 0.4947\n",
      "Epoch 15/20\n",
      "15000/15000 [==============================] - 4s 271us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6932 - val_accuracy: 0.4947\n",
      "Epoch 16/20\n",
      "15000/15000 [==============================] - 4s 267us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6932 - val_accuracy: 0.4947\n",
      "Epoch 17/20\n",
      "15000/15000 [==============================] - 4s 243us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6932 - val_accuracy: 0.4947\n",
      "Epoch 18/20\n",
      "15000/15000 [==============================] - 4s 263us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n",
      "Epoch 19/20\n",
      "15000/15000 [==============================] - 4s 263us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n",
      "Epoch 20/20\n",
      "15000/15000 [==============================] - 4s 241us/step - loss: 0.6931 - accuracy: 0.5035 - val_loss: 0.6933 - val_accuracy: 0.4947\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer=optimizers.RMSprop(lr=0.001),\n",
    "loss='binary_crossentropy',\n",
    "metrics=['accuracy'])\n",
    "history = model.fit(partial_x_train,\n",
    "                    partial_y_train,\n",
    "                    epochs=20,\n",
    "                    batch_size=512,\n",
    "                    validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "history_dict = history.history\n",
    "history_dict.keys()\n",
    "\n",
    "acc = history_dict['acc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "history_dict = history.history\n",
    "loss_values = history_dict['loss']\n",
    "val_loss_values = history_dict['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, loss_values, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss_values, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "acc_values = history_dict['acc']\n",
    "val_acc_values = history_dict['val_acc']\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8],[9, 10, 11, 12]])\n",
    "\n",
    "np.argmax(arr, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}