text: import tensorflow as tf
  import numpy as np
  
  
  class TextCNN(object):
      '''
      A CNN for text classification
      Uses and embedding layer, followed by a convolutional, max-pooling and softmax layer.
      '''
      def __init__(
          self, sequence_length, num_classes,
          embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):
  
              # Placeholders for input, output, dropout
              self.input_x = tf.placeholder(tf.float32, [None, sequence_length, embedding_size], name = "input_x")
  	    self.input_y = tf.placeholder(tf.float32, [None, num_classes], name = "input_y")
  	    self.dropout_keep_prob = tf.placeholder(tf.float32, name = "dropout_keep_prob")
  	    
  	    # Keeping track of l2 regularization loss (optional)
  	    l2_loss = tf.constant(0.0)
  
  	    # Embedding layer
              # self.embedded_chars = [None(batch_size), sequence_size, embedding_size]
              # self.embedded_chars = [None(batch_size), sequence_size, embedding_size, 1(num_channels)]
              self.embedded_chars = self.input_x
              self.embedded_chars_expended = 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_expended,
  		        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(pooled_outputs, 3)
  	    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 (unnomalized) scores and predictions
  	    with tf.name_scope("output"):
  	        W = tf.get_variable(
  		    "W",
  		    shape = [num_filters_total, num_classes],
  		    initializer = tf.contrib.layers.xavier_initializer())
  		b = tf.Variable(tf.constant(0.1, shape=[num_classes], name = "b"))
  		l2_loss += tf.nn.l2_loss(W)
  		l2_loss += tf.nn.l2_loss(b)
  		self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name = "scores")
  		self.predictions = tf.argmax(self.scores, 1, name = "predictions")
  
  	    # Calculate Mean cross-entropy loss
  	    with tf.name_scope("loss"):
  	        losses = tf.nn.softmax_cross_entropy_with_logits(logits = self.scores, labels = self.input_y)
  		self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
  	    
  	    # Accuracy
  	    with tf.name_scope("accuracy"):
  	        correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
  		self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name = "accuracy")
  