Update model.py

model.py

@@ -1,39 +1,39 @@
-import os
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-import keras
-from keras.layers import Input, Dropout, Dense
-from keras.models import Model
-from keras_vggface.vggface import VGGFace
-
-def get_model(image_shape, num_classes, model_weights, unfreeze_layers=-3, drop_rate=0.5):
-    
-    input_layer = Input(shape=image_shape)
-    vgg_base_model = VGGFace(include_top = False, input_shape = image_shape, pooling='avg')
-    
-    # Freeze all the layers till unfreeze layers
-    for layer in vgg_base_model.layers[:unfreeze_layers]:
-        layer.trainable = False
-    
-    for layer in vgg_base_model.layers[unfreeze_layers:]:
-        layer.trainable = True
-    
-    x = vgg_base_model(input_layer)
-    
-    x = Dropout(drop_rate)(x)
-    output = Dense(num_classes, activation='softmax')(x)
-    
-    model = Model(inputs=[input_layer], outputs=[output], name="Expression_Classifier")
-    model.load_weights(model_weights)
-    return model
-
-
-if __name__ == "__main__":
-	model_path = "vgg_face_weights2.h5"
-	model = get_model(
-			image_shape = (224, 224, 3),
-			num_classes = 6,
-			model_weights = model_path
-		)
-
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+
+import keras
+from keras.layers import Input, Dropout, Dense
+from keras.models import Model
+from keras_vggface.vggface import VGGFace
+
+def get_model(image_shape, num_classes, model_weights, unfreeze_layers=-3, drop_rate=0.5):
+    
+    input_layer = Input(shape=image_shape)
+    vgg_base_model = VGGFace(include_top = False, input_shape = image_shape, pooling='avg')
+    
+    # Freeze all the layers till unfreeze layers
+    for layer in vgg_base_model.layers[:unfreeze_layers]:
+        layer.trainable = True
+    
+    # for layer in vgg_base_model.layers[unfreeze_layers:]:
+    #     layer.trainable = True
+    
+    x = vgg_base_model(input_layer)
+    
+    x = Dropout(drop_rate)(x)
+    output = Dense(num_classes, activation='softmax')(x)
+    
+    model = Model(inputs=[input_layer], outputs=[output], name="Expression_Classifier")
+    model.load_weights(model_weights)
+    return model
+
+
+if __name__ == "__main__":
+	model_path = "vgg_face_weights2.h5"
+	model = get_model(
+			image_shape = (224, 224, 3),
+			num_classes = 6,
+			model_weights = model_path
+		)
+
 	print(model.summary())