Add trainings stuff

Plots/Layers/CreatePlot_layerStructure.py

@@ -0,0 +1,33 @@
+import tensorflow as tf
+
+from EncoderLayers import *
+from DecoderLayers import *
+
+import sys
+sys.path.append("../..")
+
+from Colorful_Image_Colorization.model import *
+
+def main():
+
+    encoder_layers = EncoderLayers()
+    decoder_layers = DecoderLayers()
+
+    inputs = tf.keras.Input(shape=(256,256, 1), name="Grey image") 
+    encoder = tf.keras.Model(inputs=[inputs],outputs=encoder_layers.call(inputs))
+
+    embedding = tf.keras.Input(shape=(32,32, 3), name="Embedding") 
+    decoder = tf.keras.Model(inputs=[embedding],outputs=decoder_layers.call(embedding))
+
+    tf.keras.utils.plot_model(encoder,show_shapes=True, show_layer_names=True, to_file="EncoderLayer.png")
+    tf.keras.utils.plot_model(decoder,show_shapes=True, show_layer_names=True, to_file="DecoderLayer.png")
+    
+    ModelToCompare_layers = build_model()
+    modelToCompare = tf.keras.Model(inputs=[inputs],outputs=ModelToCompare_layers.call(inputs))
+    tf.keras.utils.plot_model(modelToCompare,show_shapes=True, show_layer_names=True, to_file="ModelToCompare.png")
+
+if __name__ == "__main__":
+    try:
+        main()
+    except KeyboardInterrupt:
+        print("KeyboardInterrupt received")

Plots/Layers/DecoderLayers.py

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+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

Plots/Layers/EncoderLayers.py

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+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