text: import tensorflow as tf
  import numpy as np
  
  #slim = tf.contrib.slim
  import tf_slim as slim
  
  class URLCNN(object):
      '''
      A CNN for URL 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.compat.v1.placeholder(tf.float32, [None, sequence_length, embedding_size], name="input_x")
          self.input_y = tf.compat.v1.placeholder(tf.float32, [None, num_classes], name="input_y")
          self.dropout_keep_prob = tf.compat.v1.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.compat.v1.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)
          epsilon = 1e-3
          with tf.name_scope("BATCH-NORM"):
              batch_mean,batch_var = tf.nn.moments(self.h_pool_flat,[0])
              scale = tf.Variable(tf.ones([384]))
              beta = tf.Variable(tf.zeros([384]))
              self.BN = tf.nn.batch_normalization(self.h_pool_flat,batch_mean,batch_var,beta,scale,epsilon)
  
          # Add 2-layer-MLP
          h1_units=128
          h2_units=64
          with tf.name_scope("FC-Layer-1"):
              W = tf.Variable(tf.compat.v1.truncated_normal(shape=[384,h1_units], stddev=0.1), name="W")
              b = tf.Variable(tf.constant(0.1, shape=[h1_units]), name="b")
              self.hidden_1 = tf.nn.relu(tf.compat.v1.nn.xw_plus_b(self.BN,W,b,name="fc1"))
          with tf.name_scope("FC-Layer-2"):
              W = tf.Variable(tf.compat.v1.truncated_normal(shape=[h1_units,h2_units], stddev=0.1), name="W")
              b = tf.Variable(tf.constant(0.1, shape=[h2_units]), name="b")
              self.hidden_2 = tf.nn.relu(tf.compat.v1.nn.xw_plus_b(self.hidden_1,W,b,name="hidden"))
  
          # Final (unnomalized) scores and predictions
          with tf.name_scope("output"):
              W = tf.compat.v1.get_variable(
                      "W",
                      # shape=[num_filters_total, num_classes],
                      shape=[h2_units,num_classes],
                      initializer=tf.compat.v1.keras.initializers.glorot_normal)
                      #initializer = tf.contrib.layers.xavier_initializer())   #deprecated
  
              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.compat.v1.nn.xw_plus_b(self.hidden_2, 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")
  