nets.py 文件源码

def __call__(self, x, reuse=False):
        with tf.variable_scope(self.name) as scope:
            if reuse:
                scope.reuse_variables()
            # --- conv
            size = 64
            d = tcl.conv2d(x, num_outputs=size, kernel_size=3, # bzx64x64x3 -> bzx32x32x64
                        stride=2, activation_fn=lrelu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))
            d = tcl.conv2d(d, num_outputs=size * 2, kernel_size=3, # 16x16x128
                        stride=2, activation_fn=lrelu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))
            d = tcl.conv2d(d, num_outputs=size * 4, kernel_size=3, # 8x8x256
                        stride=2, activation_fn=lrelu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))
            d = tcl.conv2d(d, num_outputs=size * 8, kernel_size=3, # 4x4x512
                        stride=2, activation_fn=lrelu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))

            h = tcl.fully_connected(tcl.flatten(d), self.n_hidden, activation_fn=lrelu, weights_initializer=tf.random_normal_initializer(0, 0.02))

            # -- deconv
            d = tcl.fully_connected(h, 4 * 4 * 512, activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm)
            d = tf.reshape(d, (-1, 4, 4, 512))  # size
            d = tcl.conv2d_transpose(d, 256, 3, stride=2, # size*2
                                    activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))
            d = tcl.conv2d_transpose(d, 128, 3, stride=2, # size*4
                                    activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))
            d = tcl.conv2d_transpose(d, 64, 3, stride=2, # size*8
                                    activation_fn=tf.nn.relu, normalizer_fn=tcl.batch_norm, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))

            d = tcl.conv2d_transpose(d, 3, 3, stride=2, # size*16
                                    activation_fn=tf.nn.sigmoid, padding='SAME', weights_initializer=tf.random_normal_initializer(0, 0.02))
            return d