Upload model.py

model.py

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+import tensorflow as tf
+
+def residual_block(inputs, filters):
+    x = tf.keras.layers.Conv2D(filters, (3, 3), padding='same', activation='relu')(inputs)
+    x = tf.keras.layers.Conv2D(filters, (3, 3), padding='same')(x)
+    x = tf.keras.layers.add([inputs, x])
+    x = tf.keras.layers.Activation('relu')(x)
+    return x
+
+def get_model():
+    inputs = tf.keras.layers.Input(shape=(None, None, 3))
+    batch_size = tf.shape(inputs)[0]
+    
+    conv1 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu')(inputs)
+    conv1 = tf.keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu')(conv1)
+    
+    conv2 = tf.keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu')(conv1)
+    conv2 = tf.keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu')(conv2)
+    
+    conv3 = tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu')(conv2)
+    conv3 = tf.keras.layers.Conv2D(128, (3, 3), padding='same', activation='relu')(conv2)
+    
+    res1 = residual_block(conv3, 128)
+    res2 = residual_block(res1, 128)
+    res3 = residual_block(res2, 128)
+    res4 = residual_block(res3, 128)
+    res5 = residual_block(res4, 128)
+    
+    deconv1 = tf.keras.layers.Conv2DTranspose(64, (3, 3), padding='same', activation='relu')(res5)
+    deconv2 = tf.keras.layers.Conv2DTranspose(32, (3, 3), padding='same', activation='relu')(deconv1)
+    
+    outputs = tf.keras.layers.Conv2D(3, (3, 3), padding='same', activation='sigmoid')(deconv2)
+    outputs=tf.keras.layers.add([inputs, outputs])
+
+    model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
+    return model