python之如何向现有 Tensorflow 数据集对象添加/更改组件名称

从 Tensorflow 数据集指南中说

It is often convenient to give names to each component of an element, for example if they represent different features of a training example. In addition to tuples, you can use collections.namedtuple or a dictionary mapping strings to tensors to represent a single element of a Dataset.

dataset = tf.data.Dataset.from_tensor_slices( 
   {"a": tf.random_uniform([4]), 
    "b": tf.random_uniform([4, 100], maxval=100, dtype=tf.int32)}) 
print(dataset.output_types)  # ==> "{'a': tf.float32, 'b': tf.int32}" 
print(dataset.output_shapes)  # ==> "{'a': (), 'b': (100,)}" 

https://www.tensorflow.org/guide/datasets

这在 Keras 中非常有用。如果将数据集对象传递给 model.fit，组件的名称可用于匹配 Keras 模型的输入。示例:

image_input = keras.Input(shape=(32, 32, 3), name='img_input') 
timeseries_input = keras.Input(shape=(None, 10), name='ts_input') 
 
x1 = layers.Conv2D(3, 3)(image_input) 
x1 = layers.GlobalMaxPooling2D()(x1) 
 
x2 = layers.Conv1D(3, 3)(timeseries_input) 
x2 = layers.GlobalMaxPooling1D()(x2) 
 
x = layers.concatenate([x1, x2]) 
 
score_output = layers.Dense(1, name='score_output')(x) 
class_output = layers.Dense(5, activation='softmax', name='class_output')(x) 
 
model = keras.Model(inputs=[image_input, timeseries_input], 
                    outputs=[score_output, class_output]) 
 
train_dataset = tf.data.Dataset.from_tensor_slices( 
    ({'img_input': img_data, 'ts_input': ts_data}, 
     {'score_output': score_targets, 'class_output': class_targets})) 
train_dataset = train_dataset.shuffle(buffer_size=1024).batch(64) 
 
model.fit(train_dataset, epochs=3) 

因此，它对于在 tf 数据集对象中查找、添加和更改组件名称很有用。完成这些任务的最佳方式是什么？

请您参考如下方法:

您可以使用 map 对您的数据集进行修改，如果您正在寻找的话。例如，要将普通 tuple 输出转换为具有有意义名称的 dict，

import tensorflow as tf 
 
# dummy example 
ds_ori = tf.data.Dataset.zip((tf.data.Dataset.range(0, 10), tf.data.Dataset.range(10, 20))) 
ds_renamed = ds_ori.map(lambda x, y: {'input': x, 'output': y}) 
 
batch_ori = ds_ori.make_one_shot_iterator().get_next() 
batch_renamed = ds_renamed.make_one_shot_iterator().get_next() 
 
with tf.Session() as sess: 
  print(sess.run(batch_ori)) 
  print(sess.run(batch_renamed)) 
  # (0, 10) 
  # {'input': 0, 'output': 10}