bn_ops.py 文件源码

def deconv2d(input_, o_size, k_size, name='deconv2d'):
    print name, 'input', ten_sh(input_)
    print name, 'output', o_size
    assert np.sum(np.mod(o_size[1:3], ten_sh(input_)[1:3]) - [0,0]) == 0
    with tf.variable_scope(name):
        init = ly.xavier_initializer_conv2d()
        output = ly.convolution2d_transpose(input_, num_outputs=o_size[-1], \
                kernel_size=k_size, stride=np.divide(o_size[1:3], ten_sh(input_)[1:3]), \
                padding='SAME', weights_initializer=init, \
                activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm)
        return output