content: 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, vocab_size,
        embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):
  
          # Placeholders for input, output and dropout
          self.input_x = tf.placeholder(tf.int32, [None, sequence_length], 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
          with tf.device('/cpu:0'), tf.name_scope("embedding"):
              self.W = tf.Variable(
                  tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
                  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(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 (unnormalized) 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")
  