{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\";\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\";   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import ktrain\n",
    "from ktrain import text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word Counts: 88582\n",
      "Nrows: 25000\n",
      "25000 train sequences\n",
      "Average train sequence length: 231\n",
      "x_train shape: (25000,400)\n",
      "y_train shape: (25000,2)\n",
      "25000 test sequences\n",
      "Average test sequence length: 224\n",
      "x_test shape: (25000,400)\n",
      "y_test shape: (25000,2)\n"
     ]
    }
   ],
   "source": [
    "# load training and validation data from a folder\n",
    "# download and unzip IMDb dataset:  https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\n",
    "DATADIR = 'data/aclImdb'\n",
    "(x_train, y_train), (x_test, y_test), preproc = text.texts_from_folder(DATADIR, \n",
    "                                                                         max_features=20000, maxlen=400, \n",
    "                                                                         ngram_range=1, \n",
    "                                                                         classes=['pos', 'neg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Embedding, GlobalAveragePooling1D\n",
    "def get_model():\n",
    "    model = Sequential()\n",
    "    model.add(Embedding(20000+1, 50, input_length=400)) # add 1 for padding token\n",
    "    model.add(GlobalAveragePooling1D())\n",
    "    model.add(Dense(2, activation='softmax'))\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "model = get_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = ktrain.get_learner(model, train_data=(x_train, y_train), val_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulating training for different learning rates... this may take a few moments...\n",
      "Epoch 1/1024\n",
      "25000/25000 [==============================] - 5s 187us/step - loss: 0.6942 - acc: 0.4932\n",
      "Epoch 2/1024\n",
      "25000/25000 [==============================] - 4s 172us/step - loss: 0.5448 - acc: 0.7606\n",
      "Epoch 3/1024\n",
      "   32/25000 [..............................] - ETA: 4s - loss: 1.3311 - acc: 0.8750\n",
      "\n",
      "done.\n",
      "Please invoke the Learner.lr_plot() method to visually inspect the loss plot to help identify the maximal learning rate associated with falling loss.\n"
     ]
    },
    {
     "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": [
    "learner.lr_find()\n",
    "learner.lr_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "begin training using triangular learning rate policy with max lr of 0.005...\n",
      "Train on 25000 samples, validate on 25000 samples\n",
      "Epoch 1/2\n",
      "25000/25000 [==============================] - 6s 241us/step - loss: 0.5540 - acc: 0.7531 - val_loss: 0.4021 - val_acc: 0.8615\n",
      "Epoch 2/2\n",
      "25000/25000 [==============================] - 6s 240us/step - loss: 0.3062 - acc: 0.8922 - val_loss: 0.2988 - val_acc: 0.8853\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f7ad1b4a6d8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learner.autofit(0.005, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = ktrain.get_predictor(learner.model, preproc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [ 'This movie was horrible! The plot was boring. Acting was okay, though.',\n",
    "         'The film really sucked. I want my money back.',\n",
    "        'What a beautiful romantic comedy. 10/10 would see again!']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['neg', 'neg', 'pos']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor.save('/tmp/mypred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = ktrain.load_predictor('/tmp/mypred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['neg']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.predict(['The plot had lots of holes and did not make sense'])"
   ]
  }
 ],
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