Add trainings stuff

Plots/Layers/DecoderLayers.py

@@ -0,0 +1,31 @@
+import tensorflow as tf
+
+class DecoderLayers(tf.keras.Model):
+    def __init__(self):
+        super(DecoderLayers, self).__init__()
+
+        self.layer_list = [
+            tf.keras.layers.Conv2DTranspose(105, kernel_size=(3,3), strides=2, padding='same', name="Conv2D_Trans_0"),
+            tf.keras.layers.BatchNormalization(name="BatchNormalization_0"),
+            tf.keras.layers.Activation(tf.nn.tanh, name="tanh_0"),
+
+            tf.keras.layers.Conv2DTranspose(90, kernel_size=(3,3), strides=2, padding='same', name="Conv2D_Trans_1"),
+            tf.keras.layers.BatchNormalization(name="BatchNormalization_1"),
+            tf.keras.layers.Activation(tf.nn.tanh, name="tanh_1"),
+
+            tf.keras.layers.Conv2DTranspose(75, kernel_size=(3,3), strides=2, padding='same', name="Conv2D_Trans_2"),
+            tf.keras.layers.BatchNormalization(name="BatchNormalization_2"),
+            tf.keras.layers.Activation(tf.nn.tanh, name="tanh_2"),
+
+            # bottleneck to RGB
+
+            tf.keras.layers.Conv2DTranspose(2, kernel_size=(1,1), strides=1, padding='same', name="Conv2D_Trans_3"),
+            tf.keras.layers.BatchNormalization(name="BatchNormalization_3"),
+            tf.keras.layers.Activation(tf.nn.tanh, name="tanh_3"),
+        ]
+        
+    
+    def call(self, x):
+        for layer in self.layer_list:
+            x = layer(x)
+        return x