def _build_graph(self, image_size):
self.image_size = image_size
self.images = tf.placeholder(tf.float32,
shape = (None, image_size, image_size, 3))
images_mini = tf.image.resize_images(self.images,
size = (int(image_size/4),
int(image_size/4)))
self.images_blur = tf.image.resize_images(images_mini,
size = (image_size, image_size))
self.net = U_Net(output_ch = 3, block_fn = 'origin')
self.images_reconst = self.net(self.images_blur, reuse = False)
# self.image_reconst can be [-inf +inf], so need to clip its value if visualize them as images.
self.loss = tf.reduce_mean((self.images_reconst - self.images)**2)
self.opt = tf.train.AdamOptimizer()\
.minimize(self.loss, var_list = self.net.vars)
self.saver = tf.train.Saver()
self.sess.run(tf.global_variables_initializer())
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