In this example (based on the previous one), we are going to employ a very similar architecture, but as the goal is denoising the images, we will impose a code length equal to (width × height), setting all the strides to (1 × 1), and therefore we won't need to resize the images anymore:
import tensorflow as tfgraph = tf.Graph()with graph.as_default(): input_noisy_images = tf.placeholder(tf.float32, shape=(None, width, height, 1)) input_images = tf.placeholder(tf.float32, shape=(None, width, height, 1)) # Encoder conv_0 = tf.layers.conv2d(inputs=input_noisy_images, filters=32, kernel_size=(3, 3), activation=tf.nn.relu, padding='same') conv_1 = tf.layers.conv2d(inputs=conv_0, filters=64, ...
