test.py

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization, Activation
from tensorflow.keras import regularizers
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau

with tf.device('/gpu:0'):
    encoding_dim1 = 500
    encoding_dim2 = 200

    lambda_act = 0.0001
    lambda_weight = 0.001

    input_data = Input(shape=(num_in_neurons,))

    # Encoder
    encoded = Dense(encoding_dim1, activity_regularizer=regularizers.l1(lambda_act), kernel_regularizer=regularizers.l2(lambda_weight), name='encoder1')(input_data)
    encoded = BatchNormalization()(encoded)
    encoded = Activation('relu')(encoded)
    encoded = Dropout(0.5)(encoded)

    encoded = Dense(encoding_dim2, activity_regularizer=regularizers.l1(lambda_act), kernel_regularizer=regularizers.l2(lambda_weight), name='encoder2')(encoded)
    encoded = BatchNormalization()(encoded)
    encoded = Activation('relu')(encoded)
    encoded = Dropout(0.5)(encoded)

    # Decoder
    decoded = Dense(encoding_dim1, name='decoder1')(encoded)
    decoded = BatchNormalization()(decoded)
    decoded = Activation('relu')(decoded)

    decoded = Dense(num_in_neurons, name='decoder2')(decoded)
    decoded = Activation('sigmoid')(decoded)

    autoencoder = Model(inputs=input_data, outputs=decoded)
    autoencoder.compile(optimizer=Adam(learning_rate=0.001), loss='mse')

    # Callbacks
    callbacks = [
        EarlyStopping(monitor='val_loss', patience=10, verbose=1),
        ModelCheckpoint('best_model.h5', monitor='val_loss', save_best_only=True, verbose=1),
        ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, verbose=1)
    ]

    # Training
    print('Training the autoencoder')
    autoencoder.fit(x_train_noisy, x_train,
                    epochs=50,
                    batch_size=16,  # Adjusted batch size
                    shuffle=True,
                    validation_data=(x_test_noisy, x_test),
                    callbacks=callbacks)

    # Load best model
    autoencoder.load_weights('best_model.h5')

    # Freeze the weights for inference
    autoencoder.trainable = False