model = keras.models.Sequential()model.add(keras.layers.Flatten(input_shape=[28, 28]))for _ in range(20): model.add(keras.layers.Dense(100, activation="relu")) model.add(keras.layers.BatchNormalization()) """ model.add(keras.layers.Dense(100)) model.add(keras.layers.BatchNormalization()) model.add(keras.layers.Activation('relu')) """model.add(keras.layers.Dense(10, activation="softmax"))model.compile(loss="sparse_categorical_crossentropy", optimizer = "sgd", metrics = ["accuracy"])