seq_labeling.py 文件源码

def multilayer_perceptron(_X, input_size, n_hidden, n_class, forward_only=False):
    with variable_scope.variable_scope("DNN"):
      bias_start = 0.0
      weight_hidden = variable_scope.get_variable("Weight_Hidden", [input_size, n_hidden])         
      bias_hidden = variable_scope.get_variable("Bias_Hidden", [n_hidden],
                                                  initializer=init_ops.constant_initializer(bias_start))
      #Hidden layer with RELU activation
      layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, weight_hidden), bias_hidden))

      if not forward_only:
          layer_1 = tf.nn.dropout(layer_1, 0.5)

      weight_out = variable_scope.get_variable("Weight_Out", [n_hidden, n_class])
      bias_out = variable_scope.get_variable("Bias_Out", [n_class],
                                                  initializer=init_ops.constant_initializer(bias_start))  
      output = tf.matmul(layer_1, weight_out) + bias_out
      #regularizers = tf.nn.l2_loss(weight_hidden) + tf.nn.l2_loss(bias_hidden) + tf.nn.l2_loss(weight_out) + tf.nn.l2_loss(bias_out) 
    return output