nnet_rnn_binary_add.py 文件源码

def addition_problem(ReccurentLayer):
    X_train, X_test, y_train, y_test = addition_dataset(8, 5000)

    print(X_train.shape, X_test.shape)
    model = NeuralNet(
        layers=[
            ReccurentLayer,
            TimeDistributedDense(1),
            Activation('sigmoid'),
        ],
        loss='mse',
        optimizer=Adam(),
        metric='mse',
        batch_size=64,
        max_epochs=15,
    )
    model.fit(X_train, y_train)
    predictions = np.round(model.predict(X_test))
    predictions = np.packbits(predictions.astype(np.uint8))
    y_test = np.packbits(y_test.astype(np.int))
    print(accuracy(y_test, predictions))


# RNN
# addition_problem(RNN(16, parameters=Parameters(constraints={'W': SmallNorm(), 'U': SmallNorm()})))
# LSTM