components.py 文件源码

def semi_supervised_encoder_convolutional(input_tensor, z_dim, y_dim, batch_size, network_scale=1.0, img_res=28, img_channels=1):
    f_multiplier = network_scale

    net = tf.reshape(input_tensor, [-1, img_res, img_res, img_channels])

    net = layers.conv2d(net, int(16*f_multiplier), 3, stride=2)
    net = layers.conv2d(net, int(16*f_multiplier), 3, stride=1)
    net = layers.conv2d(net, int(32*f_multiplier), 3, stride=2)
    net = layers.conv2d(net, int(32*f_multiplier), 3, stride=1)
    net = layers.conv2d(net, int(64*f_multiplier), 3, stride=2)
    net = layers.conv2d(net, int(64*f_multiplier), 3, stride=1)
    net = layers.conv2d(net, int(128*f_multiplier), 3, stride=2)

    net = tf.reshape(net, [batch_size, -1])
    net = layers.fully_connected(net, 1000)

    y = layers.fully_connected(net, y_dim, activation_fn=None, normalizer_fn=None)

    z = layers.fully_connected(net, z_dim, activation_fn=None)

    return y, z