我有两个 Tensorflow 数据集,我分别处理它们以获得不同的特征和目标窗口:
window_size_x = 3
window_size_y = 2
shift_size = 1
x = np.arange(10)
y = x * 10
x = x[:-window_size_y]
y = y[window_size_x:]
ds_x = tf.data.Dataset.from_tensor_slices(x).window(window_size_x, shift=shift_size, drop_remainder=True)
ds_y = tf.data.Dataset.from_tensor_slices(y).window(window_size_y, shift=shift_size, drop_remainder=True)
for i, j in zip(ds_x, ds_y):
print(list(i.as_numpy_iterator()), list(j.as_numpy_iterator()))
Output:
[0, 1, 2] [30, 40]
[1, 2, 3] [40, 50]
[2, 3, 4] [50, 60]
[3, 4, 5] [60, 70]
[4, 5, 6] [70, 80]
[5, 6, 7] [80, 90]
当我最终使用这两个数据集将其输入模型时model.fit(ds_x, ds_y)
我收到以下错误:
ValueError: `y` argument is not supported when using dataset as input.
当我尝试像这样组合两个数据集时answer https://stackoverflow.com/a/69605943/8973620,我收到另一个错误:
ds_all = tf.data.Dataset.from_tensor_slices((ds_x, ds_y))
Error:
ValueError: Slicing dataset elements is not supported for rank 0.
合并两个数据集的正确方法是什么?