text_cnn.py

import tensorflow as tf
import numpy as np


class TextCNN(object):
    """
    A CNN for text classification.
    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
    """
    def __init__(
      self, sequence_length, num_classes, multi_size, vocab_size,
      embedding_size, filter_sizes, num_filters, hidden_units, l2_reg_lambda, retrain_emb):

      # Placeholders for input, output and dropout
      self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
      self.input_y=[]
      for i in xrange(multi_size):
          self.input_y.append(tf.placeholder(tf.float32, [None, num_classes], name="input_y"+str(i)))
      self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")

      # Keeping track of l2 regularization loss (optional)
      l2_loss = []
      for i in xrange(multi_size):
          l2_loss.append(tf.constant(0.0, name="l2_loss"+str(i)))
      # Embedding layer
      with tf.device('/cpu:0'), tf.name_scope("embedding"):
          self.W = tf.Variable(
              tf.constant(0.0, shape=[vocab_size, embedding_size]),
              trainable=False,name="W")
          self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
          self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)

      # Create a convolution + maxpool layer for each filter size
      pooled_outputs = []
      for i, filter_size in enumerate(filter_sizes):
          with tf.name_scope("conv-maxpool-%s" % filter_size):
                # Convolution Layer
                filter_shape = [filter_size, embedding_size, 1, num_filters]
                W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
                b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
                conv = tf.nn.conv2d(
                    self.embedded_chars_expanded,
                    W,
                    strides=[1, 1, 1, 1],
                    padding="VALID",
                    name="conv")
                # Apply nonlinearity
                h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
                # Maxpooling over the outputs
                pooled = tf.nn.max_pool(
                    h,
                    ksize=[1, sequence_length - filter_size + 1, 1, 1],
                    strides=[1, 1, 1, 1],
                    padding='VALID',
                    name="pool")
                pooled_outputs.append(pooled)

      # Combine all the pooled features
      num_filters_total = num_filters * len(filter_sizes)
      self.h_pool = tf.concat(3, pooled_outputs)
      self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])

      # Add dropout
      with tf.name_scope("dropout"):
          self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)

        # Final (unnormalized) scores and predictions
      self.scores=[]
      self.predictions=[]
      self.loss=[]
      self.accuracy=[]
    
      for i in xrange(multi_size):
          with tf.name_scope("output"+str(i)):
                W = tf.get_variable(
                    "W"+str(i),
                    shape=[num_filters_total, hidden_units],
                    initializer=tf.contrib.layers.xavier_initializer())
                b = tf.Variable(tf.constant(0.1, shape=[hidden_units]), name="b"+str(i))
                l2_loss[i] += tf.nn.l2_loss(W)
                l2_loss[i] += tf.nn.l2_loss(b)
                inference =tf.nn.softmax(tf.nn.bias_add(tf.matmul(self.h_drop,W), b), name="softmax"+str(i))
                inference = tf.nn.dropout(inference, self.dropout_keep_prob)
                W2 = tf.get_variable(
                        "W2"+str(i),
                        shape=[hidden_units, num_classes],
                        initializer=tf.contrib.layers.xavier_initializer())
                b2 = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b2"+str(i))
                l2_loss[i] += tf.nn.l2_loss(W2)
                l2_loss[i] += tf.nn.l2_loss(b2)

                self.scores.append(tf.nn.xw_plus_b(inference, W2, b2, name="scores"+str(i)))
                self.predictions.append(tf.argmax(tf.nn.softmax(self.scores[i]), 1, name="predictions"+str(i)))

      for i in xrange(multi_size):
          with tf.name_scope("loss"+str(i)):
                losses = tf.nn.softmax_cross_entropy_with_logits(self.scores[i], self.input_y[i])
                self.loss.append(tf.reduce_mean(losses) + l2_reg_lambda * l2_loss[i])

        # Accuracy
      for i in xrange(multi_size):
          with tf.name_scope("accuracy"+str(i)):
                correct_predictions = tf.equal(self.predictions[i], tf.argmax(self.input_y[i], 1))
                self.accuracy.append(tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy"+str(i)))