| import tensorflow as tf | |
| from data_splitting import num_classes, input_size | |
| model = tf.keras.models.Sequential([ | |
| tf.keras.layers.Dense(100, activation='relu', kernel_initializer='he_normal', kernel_regularizer=tf.keras.regularizers.l2(0.01), input_shape=(input_size,)), | |
| tf.keras.layers.BatchNormalization(), | |
| tf.keras.layers.Dense(80, activation='relu', kernel_initializer='he_normal', kernel_regularizer=tf.keras.regularizers.l2(0.01)), | |
| tf.keras.layers.BatchNormalization(), | |
| tf.keras.layers.Dense(50, activation='relu', kernel_initializer='he_normal', kernel_regularizer=tf.keras.regularizers.l2(0.01)), | |
| tf.keras.layers.BatchNormalization(), | |
| tf.keras.layers.Dense(num_classes, activation='softmax') | |
| ]) | |