components.py 文件源码

def semi_supervised_decoder_convolutional(input_tensor, batch_size, n_dimensions, network_scale=1.0, img_res=28, img_channels=1):
    f_multiplier = network_scale

    net = layers.fully_connected(input_tensor, 2*2*int(128*f_multiplier))
    net = tf.reshape(net, [-1, 2, 2, int(128*f_multiplier)])

    assert(img_res in [28, 32])

    if img_res==28:
        net = layers.conv2d_transpose(net, int(64*f_multiplier), 3, stride=2)
        net = layers.conv2d_transpose(net, int(64*f_multiplier), 3, stride=1)
        net = layers.conv2d_transpose(net, int(32*f_multiplier), 4, stride=1, padding='VALID')
        net = layers.conv2d_transpose(net, int(32*f_multiplier), 4, stride=1)
        net = layers.conv2d_transpose(net, int(16*f_multiplier), 3, stride=2)
        net = layers.conv2d_transpose(net, int(16*f_multiplier), 3, stride=1)
        net = layers.conv2d_transpose(net, int(8*f_multiplier), 3, stride=2)
        net = layers.conv2d_transpose(net, int(8*f_multiplier), 3, stride=1)
    else:
        net = layers.conv2d_transpose(net, int(64*f_multiplier), 3, stride=2)
        net = layers.conv2d_transpose(net, int(64*f_multiplier), 3, stride=1)
        net = layers.conv2d_transpose(net, int(32*f_multiplier), 3, stride=2)
        net = layers.conv2d_transpose(net, int(32*f_multiplier), 3, stride=1)
        net = layers.conv2d_transpose(net, int(16*f_multiplier), 3, stride=2)
        net = layers.conv2d_transpose(net, int(16*f_multiplier), 3, stride=1)
        net = layers.conv2d_transpose(net, int(8*f_multiplier), 3, stride=2)
        net = layers.conv2d_transpose(net, int(8*f_multiplier), 3, stride=1)

    net = layers.conv2d_transpose(net, img_channels, 5, stride=1, activation_fn=tf.nn.sigmoid)
    net = layers.flatten(net)

    return net