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
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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 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 of the convolution. Must be in the same order as the dimension specified
with format. 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 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 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, 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 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 of the convolution. Must be in the same order as the dimension specified
with format. 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 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 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, 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 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 the `N` and `C` dimensions are set to 0. The dimension order is determined
by the value of `data_format`, see below for details. 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 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 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, 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. 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 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. 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 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 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, 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. 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`. 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. 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 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 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, 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. window for each dimension of the input tensor.
strides: An int or list of `ints` that has length `1` or `3`. The stride of 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. 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 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 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, 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. the window for each dimension of the input tensor.
strides: An int or list of `ints` that has length `1`, `2` or `4`. The 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. 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 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 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, data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top,