model_FMNIST.py

import tensorflow as tf
from tensorflow.keras import layers

def fmnist_model1(num_classes, use_regularizer=True):
    model = tf.keras.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(128, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001) if use_regularizer else None),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dropout(0.3),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])
    return model


def fmnist_model2(num_classes, use_regularizer=True):
    model = tf.keras.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(512, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.001) if use_regularizer else None),
        tf.keras.layers.Dropout(0.3),
        tf.keras.layers.Dense(256, activation='relu'),
        tf.keras.layers.Dropout(0.3),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dropout(0.3),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dropout(0.3),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])
    return model