dcgan_w_gp.py 文件源码

def _build_graph(self):
        with tf.variable_scope("generator") as scope:
            print("### Print Generator Intermediate Parameter")
            self.prior = tf.placeholder(dtype=tf.float32, shape=(None, 100), name="prior_gen")
            self.is_training = tf.placeholder(dtype=tf.bool, shape = (), name="training_flag")
            prior_proj = tf.contrib.layers.fully_connected(inputs=self.prior, num_outputs=4*4*1024, 
                                                           activation_fn=None, scope="prior_projection")
            prior_proj = tf.contrib.layers.batch_norm(inputs=prior_proj, center=True, scale=True, activation_fn=tf.nn.leaky_relu, 
                                                  is_training= self.is_training, scope="bn0")
            conv0 = tf.reshape(prior_proj, (-1, 4, 4, 1024))
            conv1 = tf.contrib.layers.convolution2d_transpose(inputs=conv0, num_outputs=512, activation_fn=None,
                                                          kernel_size=(5,5), stride=(2,2), padding="SAME",scope="deconv1")
            conv1 = tf.contrib.layers.batch_norm(inputs=conv1, center=True, scale=True, activation_fn=tf.nn.leaky_relu, 
                                             is_training= self.is_training, scope="bn1")
            print(conv1.shape)
            conv2 = tf.contrib.layers.convolution2d_transpose(inputs=conv1, num_outputs=256, activation_fn=None,
                                                          kernel_size=(5,5), stride=(2,2), padding="SAME",scope="deconv2")
            conv2 = tf.contrib.layers.batch_norm(inputs=conv2, center=True, scale=True, activation_fn=tf.nn.leaky_relu, 
                                             is_training= self.is_training, scope="bn2")
            print(conv2.shape)
            conv3 = tf.contrib.layers.convolution2d_transpose(inputs=conv2, num_outputs=128, activation_fn=None,
                                                          kernel_size=(5,5), stride=(2,2), padding="SAME",scope="deconv3")
            conv3 = tf.contrib.layers.batch_norm(inputs=conv3, center=True, scale=True, activation_fn=tf.nn.leaky_relu, 
                                             is_training= self.is_training, scope="bn3")
            print(conv3.shape)
            conv4 = tf.contrib.layers.convolution2d_transpose(inputs=conv3, num_outputs=3, activation_fn=None,
                                                          kernel_size=(5,5), stride=(2,2), padding="SAME",scope="deconv4")
            self.gen_img = tf.nn.tanh(conv4)
            self.gen_img_out = tf.cast(x= tf.floor(self.gen_img*128.0 + 128.0), dtype=tf.int32)
            print(conv4.shape)
            print("### End Print Generator Intermediate Parameter")

# tf.reset_default_graph()
# g = Generator()