@@ -78,9 +78,9 @@ The returned memory region can be accessed from many threads in parallel.
The ownership of the returned ReadOnlyMemoryRegion is passed to the caller and the object should be deleted when is not used. The memory region object shouldn't live longer than the Env object.
Returns true if the path matches the given pattern. The wildcards allowed in pattern are described below (GetMatchingPaths).
Returns true if the path matches the given pattern. The wildcards allowed in pattern are described in FileSystem::GetMatchingPaths.
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@@ -100,13 +100,7 @@ Returns true if the path matches the given pattern. The wildcards allowed in pat
Given a pattern, stores in *results the set of paths that matches that pattern. *results is cleared.
pattern must match all of a name, not just a substring. pattern: { term } term: '*': matches any sequence of non-'/' characters '?': matches a single non-'/' character '[' [ '^' ] { match-list } ']': matches any single character (not) on the list c: matches character c (c != '*', '?', '\', '[') '\' c: matches character c character-range: c: matches character c (c != '\', '-', ']') '\' c: matches character c lo '-' hi: matches character c for lo <= c <= hi
Typical return codes
OK - no errors
UNIMPLEMENTED - Some underlying functions (like GetChildren) are not implemented The default implementation uses a combination of GetChildren, MatchPath and IsDirectory.
More details about `pattern` in FileSystem::GetMatchingPaths.
A tensor's shape is denoted by its number of dimensions and a size for each dimension. For example, a Tensor represented by a 3 x 4 matrix would have a shape of 2-D, [3,4].
If you know the exact shape of your Tensor when you create the TensorShape object, you can specify it then, or you can create a TensorShape with zero dimensions and one element, and call AddDim() to add dimensions later.
@@ -11,8 +11,8 @@ each component is divided by the weighted, squared sum of inputs within
sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2)
output = input / (bias + alpha * sqr_sum) ** beta
For details, see
[Krizhevsky et al., ImageNet classification with deep convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
For details, see[Krizhevsky et al., ImageNet classification with deep