34fd2wqc/MachineLearning

1import tensorflow as tf2from tensorflow import keras3from tensorflow.keras import layers4 5def build_dce_net():6    input_img = keras.Input(shape=[None, None, 3])7    conv1 = layers.Conv2D(32, 3, activation="relu", padding="same")(input_img)8    conv2 = layers.Conv2D(32, 3, activation="relu", padding="same")(conv1)9    conv3 = layers.Conv2D(32, 3, activation="relu", padding="same")(conv2)10    conv4 = layers.Conv2D(32, 3, activation="relu", padding="same")(conv3)11    int_con1 = layers.Concatenate()([conv4, conv3])12    conv5 = layers.Conv2D(32, 3, activation="relu", padding="same")(int_con1)13    int_con2 = layers.Concatenate()([conv5, conv2])14    conv6 = layers.Conv2D(32, 3, activation="relu", padding="same")(int_con2)15    int_con3 = layers.Concatenate()([conv6, conv1])16    x_r = layers.Conv2D(24, 3, activation="tanh", padding="same")(int_con3)17    return keras.Model(inputs=input_img, outputs=x_r)