nn/nn_utils.py
def concat(input_a, input_b, training, name):
    """
    Concatenate two tensors along the last dimension
    :param input_a:
    :param input_b:
    :param training: indicates it is in training or not
    :param name: name for this variable scope
    :return:
    """
    with tf.variable_scope('layer{}'.format(name)):
        input_a_norm = tf.layers.batch_normalization(input_a, training=training, name='bn')
        return tf.concat([input_a_norm, input_b], axis=-1, name='concat_{}'.format(name))

    Concatenate two tensors along the last dimension
    :param input_a:
    :param input_b:
    :param training: indicates it is in training or not
    :param name: name for this variable scope
    :return:
    
Concatenate two tensors along the last dimension
Concatenate two tensors along the last dimension
def concat(input_a, input_b, training, name):
    with tf.variable_scope('layer{}'.format(name)):
        input_a_norm = tf.layers.batch_normalization(input_a, training=training, name='bn')
        return tf.concat([input_a_norm, input_b], axis=-1, name='concat_{}'.format(name))
def concat ( input_a , input_b , training , name ) : with tf . variable_scope ( 'layer{}' . format ( name ) ) : input_a_norm = tf . layers . batch_normalization ( input_a , training = training , name = 'bn' ) return tf . concat ( [ input_a_norm , input_b ] , axis = - 1 , name = 'concat_{}' . format ( name ) )
Concatenate two tensors along the last dimension
Concatenate two tensors along the last dimension
"""
    Concatenate two tensors along the last dimension
    :param input_a:
    :param input_b:
    :param training: indicates it is in training or not
    :param name: name for this variable scope
    :return:
    """
None
input_a
None
input_b
None
training
indicates it is in training or not
indicates it is in training or not
name
name for this variable scope
name for this variable scope