dnn_model_2 = DNN( input_size=hidden_size, output_size=user_list[0].n_labels, architecture={'h1': 128, 'h2': 128}, learning_rate=0.001 ) train_dnn(dnn_model_2, user_list, ae_list, user_id=1, batch_size=128, epochs=1000, display_step=10) ''' dnn_model = DNN(input_size=hidden_size, output_size=user_list[0].n_labels, architecture={ 'h1': 128, 'h2': 128 }, learning_rate=0.001) train_dnn(dnn_model, user_list, ae_list, batch_size=128, epochs=10000, display_step=100) batch_xs, batch_ys = user_list[0].next_batch(user_list[0].n_samples_test, is_train=False) for i in range(n_users): acc_test = cal_acc(dnn_model.predict(ae_list[i].transform(batch_xs)), batch_ys) print(i, acc_test)