Adding new files

Encoder.py

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+import tensorflow as tf
+
+class Encoder(tf.keras.Model): # <-- Needed to make parameters trainable and to be callable
+    def __init__(self):
+        super(Encoder, self).__init__()
+        self.layer_list = [
+            # input (243,243) 
+
+            tf.keras.layers.Conv2D(75, kernel_size=(3, 3), strides=2, padding='same'),
+            tf.keras.layers.BatchNormalization(),
+            tf.keras.layers.Activation(tf.nn.tanh),
+            # -> (81, 81, 32)   
+
+            tf.keras.layers.Conv2D(90, kernel_size=(3, 3), strides=2, padding='same'),
+            tf.keras.layers.BatchNormalization(),
+            tf.keras.layers.Activation(tf.nn.tanh),
+            # -> (27, 27, 64)
+
+            tf.keras.layers.Conv2D(105, kernel_size=(3, 3), strides=2, padding='same'),
+            tf.keras.layers.BatchNormalization(),
+            tf.keras.layers.Activation(tf.nn.tanh),
+
+
+            # bottleneck
+            tf.keras.layers.Conv2D(3, 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("encoder:")
+        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
+