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Merge pull request #36947 from jraman:fix-guide-link

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PiperOrigin-RevId: 302665212
Change-Id: I6e83e58eb9df95c16983a770dfa380890eeab501
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TensorFlower Gardener 2020-03-24 08:04:34 -07:00
commit b80f5e6214
1 changed files with 22 additions and 17 deletions
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tensorflow/python/framework/ops.py
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@ -291,22 +291,25 @@ def disable_tensor_equality():
@tf_export("Tensor")
class Tensor(_TensorLike):
"""A tensor represents a rectangular array of data.
"""A tensor is a multidimensional array of elements represented by a
When writing a TensorFlow program, the main object you manipulate and pass
around is the `tf.Tensor`. A `tf.Tensor` object represents a rectangular array
of arbitrary dimension, filled with data of a specific data type.
`tf.Tensor` object. All elements are of a single known data type.
When writing a TensorFlow program, the main object that is
manipulated and passed around is the `tf.Tensor`.
A `tf.Tensor` has the following properties:
* a data type (float32, int32, or string, for example)
* a single data type (float32, int32, or string, for example)
* a shape
Each element in the Tensor has the same data type, and the data type is always
known.
TensorFlow supports eager execution and graph execution. In eager
execution, operations are evaluated immediately. In graph
execution, a computational graph is constructed for later
evaluation.
In eager execution, which is the default mode in TensorFlow, results are
calculated immediately.
TensorFlow defaults to eager execution. In the example below, the
matrix multiplication results are calculated immediately.
>>> # Compute some values using a Tensor
>>> c = tf.constant([[1.0, 2.0], [3.0, 4.0]])
@ -317,7 +320,6 @@ class Tensor(_TensorLike):
[[1. 3.]
[3. 7.]], shape=(2, 2), dtype=float32)
Note that during eager execution, you may discover your `Tensors` are actually
of type `EagerTensor`. This is an internal detail, but it does give you
access to a useful function, `numpy`:
@ -328,19 +330,22 @@ class Tensor(_TensorLike):
[[1. 3.]
[3. 7.]]
TensorFlow can define computations without immediately executing them, most
commonly inside `tf.function`s, as well as in (legacy) Graph mode. In those
cases, the shape (that is, the rank of the Tensor and the size of
each dimension) might be only partially known.
In TensorFlow, `tf.function`s are a common way to define graph execution.
A Tensor's shape (that is, the rank of the Tensor and the size of
each dimension) may not always be fully known. In `tf.function`
definitions, the shape may only be partially known.
Most operations produce tensors of fully-known shapes if the shapes of their
inputs are also fully known, but in some cases it's only possible to find the
shape of a tensor at execution time.
There are specialized tensors; for these, see `tf.Variable`, `tf.constant`,
`tf.placeholder`, `tf.SparseTensor`, and `tf.RaggedTensor`.
A number of specialized tensors are available: see `tf.Variable`,
`tf.constant`, `tf.placeholder`, `tf.SparseTensor`, and
`tf.RaggedTensor`.
For more on Tensors, see the [guide](https://tensorflow.org/guide/tensor).
For more on Tensors, see the [guide](https://tensorflow.org/guide/tensor`).
"""
# List of Python operators that we allow to override.