model12 = MLP_auto([Layers[0], Layers[1]], ['sigmoid'])
print("pre-training autoencoder 2")
model12.train(out1, out1, alpha, 12, max_iter)
out2 = model12.output_hidden(out1)
model13 = MLP_auto([Layers[1], Layers[2]], ['sigmoid'])
print("pre-training autoencoder 3")
model13.train(out2, out2, alpha, 12, max_iter)
# finetuning the stacked autoencoder
print("fine tuning stacked autoencoder")
# deep neural network using stacke autoencoder
final_model = MLP([n, *Layers, 2],
['sigmoid', 'sigmoid', 'sigmoid', 'sigmoid'])
# final_model.W_list[0:3] = model.W_list[0:3]
final_model.W_list[0] = model11.W_list[0]
final_model.W_list[1] = model12.W_list[0]
final_model.W_list[2] = model13.W_list[0]
# training deep neural network
alpha = 0.5
batch_size = 12
max_iter = 200
final_model.train(X_train, y_train, X_test, y_test, alpha, batch_size,
max_iter)
print(final_model.accuracy(X_test, y_test))
print(final_model.conf_mat(X_test, y_test))
