f40a875355
Back-ticks are now converted to links in the api_docs generator. With the new docs repo we're moving to simplify the docs pipeline, and make everything more readable.
By doing this we no longer get test failures for symbols that don't exist (`tf.does_not_exist` will not get a link).
There is also no way, not to set custom link text. That's okay.
This is the result of the following regex replacement (+ a couple of manual edits.):
re: @\{([^$].*?)(\$.+?)?}
sub: `\1`
Which does the following replacements:
"@{tf.symbol}" --> "`tf.symbol`"
"@{tf.symbol$link_text}" --> "`tf.symbol`"
PiperOrigin-RevId: 208042358
73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Helpers constructing Datasets."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import tensor_shape
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def optional_param_to_tensor(argument_name,
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argument_value,
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argument_default=0,
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argument_dtype=dtypes.int64):
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if argument_value is not None:
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return ops.convert_to_tensor(
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argument_value, dtype=argument_dtype, name=argument_name)
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else:
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return constant_op.constant(
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argument_default, dtype=argument_dtype, name=argument_name)
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def partial_shape_to_tensor(shape_like):
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"""Returns a `tf.Tensor` that represents the given shape.
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Args:
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shape_like: A value that can be converted to a `tf.TensorShape` or a
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`tf.Tensor`.
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Returns:
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A 1-D `tf.Tensor` of `tf.int64` elements representing the given shape, where
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`-1` is substituted for any unknown dimensions.
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"""
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try:
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# First attempt to convert the input to a shape, and return the
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# "canonical" tensor representation, which uses `-1` in place of
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# `None`.
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shape_like = tensor_shape.as_shape(shape_like)
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return ops.convert_to_tensor(
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[dim if dim is not None else -1 for dim in shape_like.as_list()],
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dtype=dtypes.int64)
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except (TypeError, ValueError):
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# The argument was not trivially convertible to a
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# `tf.TensorShape`, so fall back on the conversion to tensor
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# machinery.
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ret = ops.convert_to_tensor(shape_like, preferred_dtype=dtypes.int64)
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if ret.shape.dims is not None and len(ret.shape.dims) != 1:
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raise ValueError("The given shape %s must be a 1-D tensor of tf.int64 "
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"values, but the shape was %s."
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% (shape_like, ret.shape))
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if ret.dtype != dtypes.int64:
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raise TypeError("The given shape %s must be a 1-D tensor of tf.int64 "
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"values, but the element type was %s."
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% (shape_like, ret.dtype.name))
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return ret
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