我想迭代TF数据集,以便将获得的数据转换为numpy张量。作为tensorflow的新手,我的代码如下所示
def convert_dataset_to_pytorch(self, dataset):
sess = tf.Session(config=self.config)
iterator = dataset.make_one_shot_iterator()
exampleTF, labelsTF = iterator.get_next()
examples = torch.Tensor()
labels = torch.Tensor()
try:
while True:
examples = torch.cat((examples,torch.Tensor(exampleTF.eval(session=sess))),0)
labels = torch.cat((labels,torch.Tensor([labelsTF.eval(session=sess)])),0)
except tf.errors.OutOfRangeError:
pass
return examples, labels
显而易见的问题是,每次对eval()的调用都会遍历exampleTF和labelsTF,因此会跳过一半的条目。有什么帮助吗?我也试过像这样的东西
def convert_dataset_to_pytorch(self, dataset):
sess = tf.Session(config=self.config)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
examples = torch.Tensor()
labels = torch.Tensor()
try:
while True:
sess.run(next_element)
examples = torch.cat((examples,torch.Tensor(next_element[0])),0)
labels = torch.cat((labels,torch.Tensor([next_element[0]])),0)
except tf.errors.OutOfRangeError:
pass
return examples, labels
但是这只会导致表单的错误
examples = torch.cat((examples,torch.Tensor(next_element[0])),0)
TypeError: object of type 'Tensor' has no len()
https://stackoverflow.com/questions/56047379
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