Add trainings stuff

Plots/Layers/EncoderLayers.py

@@ -0,0 +1,29 @@
+import tensorflow as tf
+
+class EncoderLayers(tf.keras.Model):
+    def __init__(self):
+        super(EncoderLayers, self).__init__()
+
+        self.layer_list = [
+            tf.keras.layers.Conv2D(75, kernel_size=(3, 3), strides=2, padding='same', name="Conv2D_0"),
+            tf.keras.layers.BatchNormalization(name="BatchNormalization_0"),
+            tf.keras.layers.Activation(tf.nn.tanh, name="tanh_0"),
+
+            tf.keras.layers.Conv2D(90, kernel_size=(3, 3), strides=2, padding='same', name="Conv2D_1"),
+            tf.keras.layers.BatchNormalization(name="BatchNormalization_1"),
+            tf.keras.layers.Activation(tf.nn.tanh, name="tanh_1"),
+
+            tf.keras.layers.Conv2D(105, kernel_size=(3, 3), strides=2, padding='same',name="Conv2D_2"),
+            tf.keras.layers.BatchNormalization(name="BatchNormalization_2"),
+            tf.keras.layers.Activation(tf.nn.tanh, name="tanh_2"),
+
+            tf.keras.layers.Conv2D(3, kernel_size=(1, 1), strides=1, padding='same', name="Conv2D_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