Adding new files

Decoder.py

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+import tensorflow as tf
+
+class Decoder(tf.keras.Model): # <-- Needed to make parameters trainable and to be callable
+    def __init__(self):
+
+        super(Decoder, self).__init__()
+        self.layer_list = [
+            
+            tf.keras.layers.Conv2DTranspose(105, kernel_size=(3,3), strides=2, padding='same'),
+            tf.keras.layers.BatchNormalization(),
+            tf.keras.layers.Activation(tf.nn.tanh),
+
+            tf.keras.layers.Conv2DTranspose(90, kernel_size=(3,3), strides=2, padding='same'),
+            tf.keras.layers.BatchNormalization(),
+            tf.keras.layers.Activation(tf.nn.tanh),
+
+            tf.keras.layers.Conv2DTranspose(75, kernel_size=(3,3), strides=2, padding='same'),
+            tf.keras.layers.BatchNormalization(),
+            tf.keras.layers.Activation(tf.nn.tanh),
+
+            # bottleneck to RGB
+
+            tf.keras.layers.Conv2DTranspose(2, kernel_size=(1,1), strides=1, padding='same'),
+            tf.keras.layers.BatchNormalization(),
+            tf.keras.layers.Activation(tf.nn.tanh),
+            
+        ]
+    
+    @tf.function
+    def call(self, x, training):
+        
+        #print("decoder:")
+        for layer in self.layer_list:
+            #print(x.shape)
+            if isinstance(layer, tf.keras.layers.BatchNormalization):
+                x = layer(x,training)
+            else: 
+                x = layer(x)
+        # print(x.shape) 
+        # print("-------------")
+        # exit()
+        return x