Tensorflow bow.ipynb

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     "text": [
      "# words: 519\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "import numpy as np\n",
    "import random\n",
    "import pickle\n",
    "from collections import Counter\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.stem.lancaster import LancasterStemmer\n",
    "stemmer = LancasterStemmer()\n",
    "\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "hm_lines = 100000\n",
    "\n",
    "def create_lexicon(pos,neg):\n",
    "\n",
    "    lexicon = []\n",
    "    with open(pos,'r') as f:\n",
    "        contents = f.readlines()\n",
    "        for l in contents[:hm_lines]:\n",
    "            all_words = word_tokenize(l)\n",
    "            lexicon += list(all_words)\n",
    "\n",
    "    with open(neg,'r') as f:\n",
    "        contents = f.readlines()\n",
    "        for l in contents[:hm_lines]:\n",
    "            all_words = word_tokenize(l)\n",
    "            lexicon += list(all_words)\n",
    "\n",
    "    #lexicon = [lemmatizer.lemmatize(i) for i in lexicon]\n",
    "    lexicon = [stemmer.stem(i.lower()) for i in lexicon]\n",
    "    w_counts = Counter(lexicon)\n",
    "    l2 = []\n",
    "    for w in w_counts:\n",
    "        #print(w_counts[w])\n",
    "        if 1000 > w_counts[w] > 50:\n",
    "            l2.append(w)\n",
    "    print('# words:', len(l2))\n",
    "    return l2\n",
    "\n",
    "\n",
    "def sample_handling(sample,lexicon,classification):\n",
    "\n",
    "    featureset = []\n",
    "\n",
    "    with open(sample,'r') as f:\n",
    "        contents = f.readlines()\n",
    "        for l in contents[:hm_lines]:\n",
    "            current_words = word_tokenize(l.lower())\n",
    "            #current_words = [lemmatizer.lemmatize(i) for i in current_words]\n",
    "            current_words = [stemmer.stem(i.lower()) for i in current_words]\n",
    "            features = np.zeros(len(lexicon))\n",
    "            for word in current_words:\n",
    "                if word.lower() in lexicon:\n",
    "                    index_value = lexicon.index(word.lower())\n",
    "                    features[index_value] += 1\n",
    "\n",
    "            features = list(features)\n",
    "            featureset.append([features,classification])\n",
    "\n",
    "    return featureset\n",
    "\n",
    "\n",
    "\n",
    "def create_feature_sets_and_labels(pos,neg,test_size = 0.15):\n",
    "    lexicon = create_lexicon(pos,neg)\n",
    "    features = []\n",
    "    features += sample_handling('pos.txt',lexicon,[1,0])\n",
    "    features += sample_handling('neg.txt',lexicon,[0,1])\n",
    "    random.shuffle(features)\n",
    "    features = np.array(features)\n",
    "\n",
    "    testing_size = int(test_size*len(features))\n",
    "\n",
    "    train_x = list(features[:,0][:-testing_size])\n",
    "    train_y = list(features[:,1][:-testing_size])\n",
    "    test_x = list(features[:,0][-testing_size:])\n",
    "    test_y = list(features[:,1][-testing_size:])\n",
    "\n",
    "    return train_x,train_y,test_x,test_y\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt')\n"
   ]
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     ]
    }
   ],
   "source": [
    "print(train_x[0])"
   ]
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     "text": [
      "# words: 519\n",
      "{'_op': <tensorflow.python.framework.ops.Operation object at 0x7f89a4592198>, '_handle_dtype': 0, '_shape': TensorShape(None), '_value_index': 0, '_consumers': [], '_dtype': tf.float32, '_handle_shape': unknown_rank: true\n",
      "}\n",
      "Epoch 3 completed out of 10 cost: 40.0176\n",
      "Epoch 5 completed out of 10 cost: 33.8899\n",
      "Epoch 7 completed out of 10 cost: 29.5996\n",
      "Epoch 9 completed out of 10 cost: 26.1718\n",
      "Accuracy: 0.508443\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import pickle\n",
    "import numpy as np\n",
    "\n",
    "train_x,train_y,test_x,test_y = create_feature_sets_and_labels('pos.txt','neg.txt')\n",
    "\n",
    "n_nodes_hl1 = 250\n",
    "n_nodes_hl2 = 250\n",
    "\n",
    "n_classes = 2\n",
    "hm_epochs = 10\n",
    "\n",
    "x = tf.placeholder('float')\n",
    "y = tf.placeholder('float')\n",
    "\n",
    "hidden_1_layer = {'f_fum':n_nodes_hl1,\n",
    "                  'weight':tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])),\n",
    "                  'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))}\n",
    "\n",
    "hidden_2_layer = {'f_fum':n_nodes_hl2,\n",
    "                  'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),\n",
    "                  'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))}\n",
    "\n",
    "output_layer = {'f_fum':None,\n",
    "                'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_classes])),\n",
    "                'bias':tf.Variable(tf.random_normal([n_classes])),}\n",
    "\n",
    "\n",
    "# Nothing changes\n",
    "def neural_network_model(data):\n",
    "\n",
    "    l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias'])\n",
    "    l1 = tf.sigmoid(l1)\n",
    "\n",
    "    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias'])\n",
    "    l2 = tf.sigmoid(l2)\n",
    "\n",
    "    output = tf.matmul(l2,output_layer['weight']) + output_layer['bias']\n",
    "\n",
    "    return output\n",
    "\n",
    "def train_neural_network(x):\n",
    "    prediction = neural_network_model(x)\n",
    "    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )\n",
    "    #optimizer = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.9, beta2=0.999).minimize(cost)\n",
    "    optimizer = tf.train.GradientDescentOptimizer(1).minimize(cost)\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "  \n",
    "        for epoch in range(hm_epochs):\n",
    "            epoch_loss = 0\n",
    "            last_cost = 0\n",
    "\n",
    "            batch_x = np.array(train_x[0:len(train_x)])\n",
    "            batch_y = np.array(train_y[0:len(train_x)])\n",
    "\n",
    "            _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})\n",
    "            epoch_loss += c\n",
    "            last_cost = c\n",
    "\n",
    "            if (epoch% 2) == 0 and epoch > 1:\n",
    "                print('Epoch', epoch+1, 'completed out of',hm_epochs,'cost:', last_cost)\n",
    "\n",
    "        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))\n",
    "\n",
    "        print('Accuracy:',accuracy.eval({x:test_x, y:test_y}))\n",
    "\n",
    "print(x.__dict__)\n",
    "train_neural_network(x)"
   ]
  }
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