Update modelutil.py

modelutil.py

@@ -1,24 +1,38 @@
+# import tensorflow as tf
+
+# def create_model():
+
+#     LAYERS = [tf.keras.layers.Flatten(input_shape=[28,28], name="inputlayer"),
+#               tf.keras.layers.Dense(300, activation='relu', name="hiddenlayer1"),
+#               tf.keras.layers.Dense(100, activation='relu', name="hiddenlayer2"),
+#               tf.keras.layers.Dense(10, activation='softmax', name="outputlayer")]
+    
+#     model = tf.keras.models.Sequential(LAYERS)
+    
+#     model.load_weights('./checkpoint')
+    
+    
+#     # LOSS_FUNCTION = tf.keras.losses.SparseCategoricalCrossentropy() # HERE
+#     # OPTIMIZER = tf.keras.optimizers.legacy.Adam()
+#     # METRICS = ["accuracy"]
+#     # model.compile(loss=LOSS_FUNCTION,
+#     # optimizer=OPTIMIZER,
+#     # metrics=METRICS)
+
+#     return model
+
+
+
 import tensorflow as tf
 
 def create_model():
-
     LAYERS = [tf.keras.layers.Flatten(input_shape=[28,28], name="inputlayer"),
               tf.keras.layers.Dense(300, activation='relu', name="hiddenlayer1"),
               tf.keras.layers.Dense(100, activation='relu', name="hiddenlayer2"),
               tf.keras.layers.Dense(10, activation='softmax', name="outputlayer")]
     
     model = tf.keras.models.Sequential(LAYERS)
-    
-    model.load_weights('./checkpoint')
-    
-    
-    # LOSS_FUNCTION = tf.keras.losses.SparseCategoricalCrossentropy() # HERE
-    # OPTIMIZER = tf.keras.optimizers.legacy.Adam()
-    # METRICS = ["accuracy"]
-    # model.compile(loss=LOSS_FUNCTION,
-    # optimizer=OPTIMIZER,
-    # metrics=METRICS)
-
     return model
 
-
+def load_model_weights(model, checkpoint_path):
+    model.load_weights(checkpoint_path)