modelutil.py

# 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)
    return model

def load_model_weights(model, checkpoint_path):
    model.load_weights(checkpoint_path)