英文:
'Tensor' vs 'tf.Tensor' tensorflow
问题
Tensor 和 tf.Tensor 是相同的，它们都表示一个张量对象。它们之间没有区别。宗旨，它们的显示方式略有不同，但它们都代表相同类型的张量。
英文:
I am exploring tensorflow internals, and sometimes when I print the value of a tensor, I will see data like the following: Tensor("x/PlaceholderWithDefault:0", shape=(), dtype=int32)
and other times I will see tf.Tensor(0, shape=(), dtype=int32).
What is the difference between these two expressions? Are Tensor and tf.Tensor different? And if not, why are they displayed differently (and seem to have different behavior)?
答案1
得分: 1
当你看到输出的Tensor("x/PlaceholderWithDefault:0", 形状=(), 数据类型=int32)时，它是TensorFlow的占位符对象。占位符是符号性张量，充当TensorFlow计算图的输入节点。当你创建一个占位符时，你定义了它的形状和数据类型，但没有提供任何值。
当你看到输出"tf.Tensor(0, 形状=(), 数据类型=int32)"时，它是一个TensorFlow张量对象。这个输出格式显示了张量的实际值，以及它的形状和数据类型。
英文:
When you see the output Tensor("x/PlaceholderWithDefault:0", shape=(), dtype=int32), it is the TensorFlow placeholder object. Placeholders are symbolic tensors that act as input nodes to the TensorFlow computational graph. When you create a placeholder, you define its shape and data type but do not provide any values for it.
When you see the output "tf.Tensor(0, shape=(), dtype=int32)", it is a TensorFlow tensor object. This output format shows the actual value of the tensor, along with its shape and data type.
Thank You.
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