dcgan_.py 文件源码

def generator(self, z, Cc=128, f_h=5, f_w=5):
        with tf.variable_scope("g_deconv0",reuse=None):
            deconv0 = deconv2d(z, [self.batch_size, 4, 4, 8*Cc], 4, 4, 1, 1, bias=not self.Bn, padding='VALID')
            deconv0 = tf.nn.relu(tcl.batch_norm(deconv0)) if self.Bn else tf.nn.relu(deconv0)
        with tf.variable_scope("g_deconv1",reuse=None):
            deconv1 = deconv2d(deconv0, [self.batch_size, 8, 8, 4*Cc], f_h, f_w, 2, 2, bias=not self.Bn, padding='SAME')
            deconv1 = tf.nn.relu(tcl.batch_norm(deconv1)) if self.Bn else tf.nn.relu(deconv1)
        with tf.variable_scope("g_deconv2",reuse=None):
            deconv2 = deconv2d(deconv1, [self.batch_size, 16, 16, 2*Cc], f_h, f_w, 2, 2, bias=not self.Bn, padding='SAME')
            deconv2 = tf.nn.relu(tcl.batch_norm(deconv2)) if self.Bn else tf.nn.relu(deconv2)
        with tf.variable_scope("g_deconv3",reuse=None):
            deconv3 = deconv2d(deconv2, [self.batch_size, 32, 32, Cc], f_h, f_w, 2, 2, bias=not self.Bn, padding='SAME')
            deconv3 = tf.nn.relu(tcl.batch_norm(deconv3)) if self.Bn else tf.nn.relu(deconv3)
        with tf.variable_scope("g_deconv4",reuse=None):
            deconv4 = deconv2d(deconv3, [self.batch_size, 64, 64, self.C], f_h, f_w, 2, 2, bias=not self.Bn, padding='SAME')
        return tf.tanh(deconv4)