如何在 TensorFlow 2.0 中使用 Dataset.window() 方法创建的窗口? [英] How to use windows created by the Dataset.window() method in TensorFlow 2.0?

问题描述

我正在尝试使用 TensorFlow 2.0 创建一个数据集，该数据集将从时间序列中返回随机窗口，以及作为目标的下一个值.

I'm trying to create a dataset that will return random windows from a time series, along with the next value as the target, using TensorFlow 2.0.

我正在使用 Dataset.window()，看起来很有希望:

I'm using Dataset.window(), which looks promising:

import tensorflow as tf

dataset = tf.data.Dataset.from_tensor_slices(tf.range(10))
dataset = dataset.window(5, shift=1, drop_remainder=True)
for window in dataset:
    print([elem.numpy() for elem in window])

输出:

[0, 1, 2, 3, 4]
[1, 2, 3, 4, 5]
[2, 3, 4, 5, 6]
[3, 4, 5, 6, 7]
[4, 5, 6, 7, 8]
[5, 6, 7, 8, 9]

但是，我想使用最后一个值作为目标.如果每个窗口都是一个张量，我会使用:

However, I would like to use the last value as the target. If each window was a tensor, I would use:

dataset = dataset.map(lambda window: (window[:-1], window[-1:]))

但是，如果我尝试这样做，则会出现异常:

However, if I try this, I get an exception:

TypeError: '_VariantDataset' object is not subscriptable

推荐答案

解决方案是这样调用flat_map():

dataset = dataset.flat_map(lambda window: window.batch(5))

现在数据集中的每一项都是一个窗口，所以你可以像这样拆分它:

Now each item in the dataset is a window, so you can split it like this:

dataset = dataset.map(lambda window: (window[:-1], window[-1:]))

所以完整的代码是:

import tensorflow as tf

dataset = tf.data.Dataset.from_tensor_slices(tf.range(10))
dataset = dataset.window(5, shift=1, drop_remainder=True)
dataset = dataset.flat_map(lambda window: window.batch(5))
dataset = dataset.map(lambda window: (window[:-1], window[-1:]))

for X, y in dataset:
    print("Input:", X.numpy(), "Target:", y.numpy())

输出:

Input: [0 1 2 3] Target: [4]
Input: [1 2 3 4] Target: [5]
Input: [2 3 4 5] Target: [6]
Input: [3 4 5 6] Target: [7]
Input: [4 5 6 7] Target: [8]
Input: [5 6 7 8] Target: [9]

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