Update main.py

main.py

@@ -5,26 +5,31 @@ import matplotlib.pyplot # for colormap
 import matplotlib.colors # for color conversion
 
 # Building the neural network
-model1 = keras.models.Sequential()
-model1.add(keras.layers.InputLayer(input_shape=(101, 636, 1)))
-model1.add(keras.layers.Conv2D(4, (9, 9), activation='relu', padding='same', strides=1))
-model1.add(keras.layers.Conv2D(4, (9, 9), activation='relu', padding='same'))
-model1.add(keras.layers.Conv2D(8, (7, 7), activation='relu', padding='same', strides=1))
-model1.add(keras.layers.Conv2D(8, (7, 7), activation='relu', padding='same'))
-model1.add(keras.layers.Conv2D(16, (5, 5), activation='relu', padding='same'))
-model1.add(keras.layers.Conv2D(16, (5, 5), activation='relu', padding='same', strides=1))
-model1.add(keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same'))
-model1.add(keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same', strides=1))
-model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same'))
-model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same', strides=1))
-model1.add(keras.layers.UpSampling2D((1, 1)))
-model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same'))
-model1.add(keras.layers.UpSampling2D((1, 1)))
-model1.add(keras.layers.Conv2D(8, (3, 3), activation='relu', padding='same'))
-model1.add(keras.layers.UpSampling2D((1, 1)))
-model1.add(keras.layers.Conv2D(4, (7, 7), activation='tanh', padding='same'))
-model1.add(keras.layers.UpSampling2D((1, 1)))
-model1.add(keras.layers.Conv2D(3, (9, 9), activation='tanh', padding='same'))
+# model1 = keras.models.Sequential()
+# model1.add(keras.layers.InputLayer(input_shape=(101, 636, 1)))
+# model1.add(keras.layers.Conv2D(4, (9, 9), activation='relu', padding='same', strides=1))
+# model1.add(keras.layers.Conv2D(4, (9, 9), activation='relu', padding='same'))
+# model1.add(keras.layers.Conv2D(8, (7, 7), activation='relu', padding='same', strides=1))
+# model1.add(keras.layers.Conv2D(8, (7, 7), activation='relu', padding='same'))
+# model1.add(keras.layers.Conv2D(16, (5, 5), activation='relu', padding='same'))
+# model1.add(keras.layers.Conv2D(16, (5, 5), activation='relu', padding='same', strides=1))
+# model1.add(keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same'))
+# model1.add(keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same', strides=1))
+# model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same'))
+# model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same', strides=1))
+# model1.add(keras.layers.UpSampling2D((1, 1)))
+# model1.add(keras.layers.Conv2D(16, (2, 2), activation='relu', padding='same'))
+# model1.add(keras.layers.UpSampling2D((1, 1)))
+# model1.add(keras.layers.Conv2D(8, (3, 3), activation='relu', padding='same'))
+# model1.add(keras.layers.UpSampling2D((1, 1)))
+# model1.add(keras.layers.Conv2D(4, (7, 7), activation='tanh', padding='same'))
+# model1.add(keras.layers.UpSampling2D((1, 1)))
+# model1.add(keras.layers.Conv2D(3, (9, 9), activation='tanh', padding='same'))
+
+from huggingface_hub import from_pretrained_keras
+
+model = from_pretrained_keras("cmudrc/microstructure-colorization")
+
 
 #Loading the weights in the architecture (The file should be stored in the same directory as the code)
 model1.load_weights('modelV13_500trained_1.h5')