Convert a Tensorflow MapDataset to a tf.TensorArray

Question

假设我有以下代码:

import numpy as np
import tensorflow as tf
simple_data_samples = np.array([
[1, 1, 1, -1, -1],
[2, 2, 2, -2, -2],
[3, 3, 3, -3, -3],
[4, 4, 4, -4, -4],
[5, 5, 5, -5, -5],
[6, 6, 6, -6, -6],
[7, 7, 7, -7, -7],
[8, 8, 8, -8, -8],
[9, 9, 9, -9, -9],
[10, 10, 10, -10, -10],
[11, 11, 11, -11, -11],
[12, 12, 12, -12, -12],
])
def timeseries_dataset_multistep_combined(features, label_slice, input_sequence_length, output_sequence_length, batch_size):
feature_ds = tf.keras.preprocessing.timeseries_dataset_from_array(features, None, input_sequence_length + output_sequence_length, batch_size=batch_size)
def split_feature_label(x):
x=tf.strings.as_string(x)
return x[:, :input_sequence_length, :], x[:, input_sequence_length:, label_slice]
feature_ds = feature_ds.map(split_feature_label)
return feature_ds
ds = timeseries_dataset_multistep_combined(simple_data_samples, slice(None, None, None), input_sequence_length=4, output_sequence_length=2,
batch_size=1)
def print_dataset(ds):
for inputs, targets in ds:
print("---Batch---")
print("Feature:", inputs.numpy())
print("Label:", targets.numpy())
print("")

print_dataset(ds)

变量ds表示Tensorflow MapDataset。我想把这个变量ds转换成tf。tensorarray。最快、最有效的方法是什么?

Answer 1

假设您想要按原样输出迭代器，下面是代码:

list_array = list(sum(list(ds),()))
feature = tf.squeeze(tf.stack(list_array[::2]))
label = tf.squeeze(tf.stack(list_array[1::2]))