python之TensorFlow 类型错误 : 'BatchDataset' object is not iterable/TypeError: 'CacheDataset' object is not subscriptable

我正在关注 TensorFlow starter guide .它特别指出要在鸢尾花(花)分类的示例项目上启用即时执行。

Import the required Python modules, including TensorFlow, and enable eager execution for this program. Eager execution makes TensorFlow evaluate operations immediately, returning concrete values instead of creating a computational graph that is executed later. If you are used to a REPL or the python interactive console, you'll feel at home.

所以我按照说明启用eager execution，然后继续说明。然而，当我到达讨论如何将数据集准备成 tensorflow 数据集的部分时，我遇到了一个错误。

单元格代码

train_dataset = tf.data.TextLineDataset(train_dataset_fp) 
train_dataset = train_dataset.skip(1)             # skip the first header row 
train_dataset = train_dataset.map(parse_csv)      # parse each row 
train_dataset = train_dataset.shuffle(buffer_size=1000)  # randomize 
train_dataset = train_dataset.batch(32) 
 
# View a single example entry from a batch 
features, label = iter(train_dataset).next() 
print("example features:", features[0]) 
print("example label:", label[0]) 

错误

--------------------------------------------------------------------------- 
TypeError                                 Traceback (most recent call last) 
<ipython-input-5-61bfe99af85b> in <module>() 
 
      7  
      8 # View a single example entry from a batch 
----> 9 features, label = iter(train_dataset).next() 
     10 print("example features:", features[0]) 
     11 print("example label:", label[0]) 
 
TypeError: 'BatchDataset' object is not iterable 

我只想继续学习这些例子。我该怎么做才能将 BatchDataset 对象转换为可迭代对象？

请您参考如下方法:

事实证明，我实际上没有在项目中执行导致此问题的某些步骤。

将 TensorFlow 从 1.7 升级到 1.8:

!pip install --upgrade tensorflow

检查您的 TensorFlow 是否已更新

此代码单元格:

from __future__ import absolute_import, division, print_function 
 
import os 
import matplotlib.pyplot as plt 
 
import tensorflow as tf 
import tensorflow.contrib.eager as tfe 
 
tf.enable_eager_execution() 
 
print("TensorFlow version: {}".format(tf.VERSION)) 
print("Eager execution: {}".format(tf.executing_eagerly())) 

应该返回以下输出:

TensorFlow version: 1.8.0 
Eager execution: True