nn/nn_utils.py
def upsample_concat(input_a, input_b, name, size=(2, 2)):
    """
    Upsample tensor a and concatenate with tensor b
    :param input_a:
    :param input_b:
    :param name: name for this variable scope
    :param size: how many times tensor a should be upsampled
    :return:
    """
    upsample = upsampling_2d(input_a, size=size, name=name)
    return tf.concat([upsample, input_b], axis=-1, name='concat_{}'.format(name))

    Upsample tensor a and concatenate with tensor b
    :param input_a:
    :param input_b:
    :param name: name for this variable scope
    :param size: how many times tensor a should be upsampled
    :return:
    
Upsample tensor a and concatenate with tensor b
Upsample tensor a and concatenate with tensor b
def upsample_concat(input_a, input_b, name, size=(2, 2)):
    upsample = upsampling_2d(input_a, size=size, name=name)
    return tf.concat([upsample, input_b], axis=-1, name='concat_{}'.format(name))
def upsample_concat ( input_a , input_b , name , size = ( 2 , 2 ) ) : upsample = upsampling_2d ( input_a , size = size , name = name ) return tf . concat ( [ upsample , input_b ] , axis = - 1 , name = 'concat_{}' . format ( name ) )
Upsample tensor a and concatenate with tensor b
Upsample tensor a and concatenate with tensor b
"""
    Upsample tensor a and concatenate with tensor b
    :param input_a:
    :param input_b:
    :param name: name for this variable scope
    :param size: how many times tensor a should be upsampled
    :return:
    """
None
input_a
None
input_b
None
name
name for this variable scope
name for this variable scope
size
None