nn/nn_utils.py
def upsample_conv_concat(input_a, input_b, filter_n, training, name, size=(2, 2)):
    """
    Upsample tensor a, do convolution and concatenate with tensor b
    :param input_a:
    :param input_b:
    :param filter_n: #filters in convolutional layers to precess upsampled tensor a
    :param training: indicates it is in training or not
    :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)
    upsample = conv_conv_pool(upsample, filter_n, training, 'upsample_'+name, kernel_size=(2, 2), pool=False)
    return tf.concat([input_b, upsample], axis=-1, name='concat_{}'.format(name))

    Upsample tensor a, do convolution and concatenate with tensor b
    :param input_a:
    :param input_b:
    :param filter_n: #filters in convolutional layers to precess upsampled tensor a
    :param training: indicates it is in training or not
    :param name: name for this variable scope
    :param size: how many times tensor a should be upsampled
    :return:
    
Upsample tensor a, do convolution and concatenate with tensor b
Upsample tensor a do convolution and concatenate with tensor b
def upsample_conv_concat(input_a, input_b, filter_n, training, name, size=(2, 2)):
    upsample = upsampling_2d(input_a, size=size, name=name)
    upsample = conv_conv_pool(upsample, filter_n, training, 'upsample_'+name, kernel_size=(2, 2), pool=False)
    return tf.concat([input_b, upsample], axis=-1, name='concat_{}'.format(name))
def upsample_conv_concat ( input_a , input_b , filter_n , training , name , size = ( 2 , 2 ) ) : upsample = upsampling_2d ( input_a , size = size , name = name ) upsample = conv_conv_pool ( upsample , filter_n , training , 'upsample_' + name , kernel_size = ( 2 , 2 ) , pool = False ) return tf . concat ( [ input_b , upsample ] , axis = - 1 , name = 'concat_{}' . format ( name ) )
Upsample tensor a, do convolution and concatenate with tensor b
Upsample tensor a do convolution and concatenate with tensor b
"""
    Upsample tensor a, do convolution and concatenate with tensor b
    :param input_a:
    :param input_b:
    :param filter_n: #filters in convolutional layers to precess upsampled tensor a
    :param training: indicates it is in training or not
    :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
filter_n
filters in convolutional layers to precess upsampled tensor a
filters in convolutional layers to precess upsampled tensor a
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
size
None