如何获得1维到3维的整形？

Question

here is my code

def create_dataset(signal_data, look_back=1):
    dataX, dataY = [], []
    for i in range(len(signal_data) - look_back):
        dataX.append(signal_data[i:(i + look_back), 0])
        dataY.append(signal_data[i + look_back, 0])
    return np.array(dataX), np.array(dataY)

look_back = 20
...

train_size = int(len(data) * 0.80)

test_size = len(data) - train_size

train = data[0:train_size]

test = data[train_size:len(data)]

x_train, y_train = create_dataset(train, look_back)

x_test, y_test = create_dataset(test, look_back)

then x_train shape is (62796, 20) and y_train shape is(62796,)

I use this data to LSTM

so, reshape x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1)) is done

(now x_train.shape is (62796, 20, 1) )

but y_train shape is (62796,) So, i can't reshape 1D -> 3D

how can i y_train reshape 1D ->3D

i want y_train shape as (62796, 20, 1) because want to LSTM return_sequences=True parameter

Answer 1

Is that what you're looking for?

y_train = np.ones(100)
print(y_train.shape) #prints (100,)
y_train = y_train.reshape(-1,1,1)
print(y_train.shape) # prints (100,1,1)

EDIT: Final solution, after brief discussion in comments:

y_train=np.repeat(y_train.reshape(-1,1), 20, axis=1).reshape(-1,20,1)