import tensorflow as tf

def create_model():

    LAYERS = [tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1), name="convlayer1"),
              tf.keras.layers.Conv2D(64, (3, 3), activation='relu', name="convlayer2"),
              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