commit 632b85a6b76c4af8e9b03f6eff3ddd616ec7534d
Author: spike <chakib.benz@gmail.com>
Date:   Wed Feb 22 00:17:04 2017 +0100

    initial commit

diff --git a/README.md b/README.md
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+
+tensorflow==1.0.0
+tflearn==0.3
diff --git a/Sentiment Analysis with TFlearn - LSTM - Word2Vec.ipynb b/Sentiment Analysis with TFlearn - LSTM - Word2Vec.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import division, print_function, absolute_import\n",
+    "\n",
+    "import tflearn\n",
+    "from tflearn.data_utils import to_categorical, pad_sequences\n",
+    "from tflearn.datasets import imdb\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "train, valid, test = imdb.load_data(path='imdb.pkl', n_words=10000, valid_portion=0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "trainX, trainY = train\n",
+    "validX, validY = valid\n",
+    "testX, testY = test\n",
+    "\n",
+    "# Test set: 25% of the full test set\n",
+    "test_len = int(0.25*len(testX))\n",
+    "testX = testX[:test_len]\n",
+    "testY = testY[:test_len]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Data preprocessing\n",
+    "# Sequence padding\n",
+    "trainX = pad_sequences(trainX, maxlen=100, value=0.)\n",
+    "validX = pad_sequences(validX, maxlen=100, value=0.)\n",
+    "testX = pad_sequences(testX, maxlen=100)\n",
+    "\n",
+    "#Convert labels to binary vectors\n",
+    "trainY = to_categorical(trainY, nb_classes=2)\n",
+    "validY = to_categorical(validY, nb_classes=2)\n",
+    "testY = to_categorical(testY, nb_classes=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Network building\n",
+    "net = tflearn.input_data([None, 100])\n",
+    "net = tflearn.embedding(net, input_dim=10000, output_dim=128)\n",
+    "net = tflearn.lstm(net, 128, dropout=0.8)\n",
+    "net = tflearn.fully_connected(net, 2, activation='softmax')\n",
+    "net = tflearn.regression(net, optimizer='adam', learning_rate=0.001, loss='categorical_crossentropy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Step: 7039  | total loss: \u001b[1m\u001b[32m0.03469\u001b[0m\u001b[0m | time: 47.844s\n",
+      "| Adam | epoch: 010 | loss: 0.03469 - acc: 0.9900 -- iter: 22496/22500\n",
+      "Training Step: 7040  | total loss: \u001b[1m\u001b[32m0.03141\u001b[0m\u001b[0m | time: 49.158s\n",
+      "| Adam | epoch: 010 | loss: 0.03141 - acc: 0.9910 | val_loss: 0.98614 - val_acc: 0.7904 -- iter: 22500/22500\n",
+      "--\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Training\n",
+    "model = tflearn.DNN(net, tensorboard_verbose=0)\n",
+    "model.fit(trainX, trainY, validation_set=(validX, validY), show_metric=True, batch_size=32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.84079999971389774]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Testing the model\n",
+    "model.evaluate(testX[:test_len], testY[:test_len])"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "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": "3.5.1"
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+ "nbformat": 4,
+ "nbformat_minor": 2
+}