Question

我正在尝试在len N的多个序列上使用GRU层训练RNN。如何设置GRU层的输入以及我的训练和验证数据？

我尝试了不同的input_shape（例如（None，N，2），（N，2），（None，2）...），我的数据有2列，并且序列的大小为N。

def get_batch(train, ind):
    X = np.array([train.loc[i * N: (i+1) * N - 1][['col_1', 'col_2']].values for i in ind])
    Y = np.array([train.loc[i * N: (i+1) * N - 1][['target']].values for i in ind])
    return X,Y

model = Sequential()
model.add(CuDNNGRU(20, input_shape=(2, 1)))
model.add(Dropout(.4))
model.add(Dense(20, activation='relu'))
model.add(Dropout(.5))
model.add(Dense(1))

model.compile(optimizer=adam(lr=0.005), loss="mae")

def cross_val(ind, train):
    fold_size = int(len(ind) / 5)
    for i in range(5):
        val_ind = ind[i * fold_size: (i+1) * fold_size]
        train_ind = [x for x in ind if x not in val_ind]
        X_train, Y_train = get_batch(train, train_ind)
        X_val, Y_val = get_batch(train, val_ind)
        model.fit(X_train, Y_train, validation_data=(X_val, Y_val))

cross_val(ind, train)

我最终遇到这种错误：

ValueError: Error when checking target: expected dense_54 to have 2 dimensions, but got array with shape (8, 4096, 1)

Answer 1

好吧，我想我明白了，您的模型必须是：

from keras.layers import TimeDistributed

model = Sequential()
model.add(CuDNNGRU(20, return_sequences=True, input_shape=(2, 1)))
model.add(TimeDistributed(Dropout(.4)))
model.add(TimeDistributed(Dense(20, activation='relu')))
model.add(TimeDistributed(Dropout(.5)))
model.add(TimeDistributed(Dense(1)))

由于每个时间步有一个输出，所以要返回一个序列

Answer 2

我尝试了一些事情，并且设法训练了我的模型，但是一次只能训练一个序列。

def create_model():
    model = Sequential()
    model.add(CuDNNGRU(20, input_shape=(2, 1)))
    model.add(Dense(20, activation='relu'))
    model.add(Dense(1))

    model.compile(optimizer=adam(lr=0.005), loss="mae")
    return model

def cross_val(ind, train):
    fold_size = int(len(ind) / 5)
    for i in range(5):
        model = create_model()
        val_ind = ind[i * fold_size: (i+1) * fold_size]
        train_ind = [x for x in ind if x not in val_ind]
        X_train, Y_train = get_batch(train, train_ind)
        X_val, Y_val = get_batch(train, val_ind)
        for j in range(len(X_train)):
            model.fit(X_train[j], Y_train[j], validation_data=(X_val[0], Y_val[0]))

cross_val(ind, train)

我的问题是，我可以删除for j in ...循环吗？

GRU输入和培训

2 个答案: