experiments/STT-tensorflow
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Fix API docs formatting issue.

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Change `string to `string` to avoid having html tags inlined in the generated
API docs files. For example, in the description of the padding argument of
https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose.

PiperOrigin-RevId: 350806817
Change-Id: If0bf2c028663bc3ba2c8e1cb40aa01d8fc2141c9
This commit is contained in:
Richard Uhler 2021-01-08 11:49:46 -08:00 committed by TensorFlower Gardener
parent 7b9bd381c8
commit 9a1ea4737b
1 changed files with 7 additions and 7 deletions

14
tensorflow/python/ops/nn_ops.py
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@ -2321,7 +2321,7 @@ def conv2d_backprop_filter( # pylint: disable=redefined-builtin,dangerous-defau
The stride of the sliding window for each dimension of the input
of the convolution. Must be in the same order as the dimension specified
with format.
padding: Either the `string `"SAME"` or `"VALID"` indicating the type of
padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
padding algorithm to use, or a list indicating the explicit paddings at
the start and end of each dimension. When explicit padding is used and
data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
@ -2383,7 +2383,7 @@ def conv2d_backprop_input( # pylint: disable=redefined-builtin,dangerous-defaul
The stride of the sliding window for each dimension of the input
of the convolution. Must be in the same order as the dimension specified
with format.
padding: Either the `string `"SAME"` or `"VALID"` indicating the type of
padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
padding algorithm to use, or a list indicating the explicit paddings at
the start and end of each dimension. When explicit padding is used and
data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
@ -2526,7 +2526,7 @@ def conv2d_transpose_v2(
value is given it is replicated in the `H` and `W` dimension. By default
the `N` and `C` dimensions are set to 0. The dimension order is determined
by the value of `data_format`, see below for details.
padding: Either the `string `"SAME"` or `"VALID"` indicating the type of
padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
padding algorithm to use, or a list indicating the explicit paddings at
the start and end of each dimension. When explicit padding is used and
data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
@ -4490,7 +4490,7 @@ def max_pool_v2(input, ksize, strides, padding, data_format=None, name=None):
of the window for each dimension of the input tensor.
strides: An int or list of `ints` that has length `1`, `N` or `N+2`. The
stride of the sliding window for each dimension of the input tensor.
padding: Either the `string `"SAME"` or `"VALID"` indicating the type of
padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
padding algorithm to use, or a list indicating the explicit paddings at
the start and end of each dimension. When explicit padding is used and
data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
@ -4569,7 +4569,7 @@ def max_pool(value,
The size of the window for each dimension of the input tensor.
strides: An int or list of `ints` that has length `1`, `2` or `4`.
The stride of the sliding window for each dimension of the input tensor.
padding: Either the `string `"SAME"` or `"VALID"` indicating the type of
padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
padding algorithm to use, or a list indicating the explicit paddings at
the start and end of each dimension. When explicit padding is used and
data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,
@ -4627,7 +4627,7 @@ def max_pool1d(input, ksize, strides, padding, data_format="NWC", name=None):
window for each dimension of the input tensor.
strides: An int or list of `ints` that has length `1` or `3`. The stride of
the sliding window for each dimension of the input tensor.
padding: Either the `string `"SAME"` or `"VALID"` indicating the type of
padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
padding algorithm to use, or a list indicating the explicit paddings at
the start and end of each dimension. When explicit padding is used and
data_format is `"NWC"`, this should be in the form `[[0, 0], [pad_left,
@ -4683,7 +4683,7 @@ def max_pool2d(input, ksize, strides, padding, data_format="NHWC", name=None):
the window for each dimension of the input tensor.
strides: An int or list of `ints` that has length `1`, `2` or `4`. The
stride of the sliding window for each dimension of the input tensor.
padding: Either the `string `"SAME"` or `"VALID"` indicating the type of
padding: Either the `string` `"SAME"` or `"VALID"` indicating the type of
padding algorithm to use, or a list indicating the explicit paddings at
the start and end of each dimension. When explicit padding is used and
data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,