experiments/STT-tensorflow
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Documentation correction

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This commit is contained in:
Pariksheet 2019-03-21 16:31:02 +05:30
parent 61d004089c
commit aef739a85c
4 changed files with 4 additions and 4 deletions
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2
tensorflow/compiler/xla/tests/test_utils_test.cc
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@ -258,7 +258,7 @@ XLA_TEST_F(TestUtilsTest, MakeFakeArgumentsForGather) {
auto module = ParseHloString(R"(
HloModule Test
ENTRY %module(paramater.0: f32[200,100,300], parameter.1: s32[10,2]) ->
ENTRY %module(parameter.0: f32[200,100,300], parameter.1: s32[10,2]) ->
f32[10,300] {
%parameter.0 = f32[200,100,300] parameter(0)
%parameter.1 = s32[10,2] parameter(1)

2
tensorflow/contrib/distribute/python/parameter_server_strategy.py
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@ -46,7 +46,7 @@ class ParameterServerStrategy(distribute_lib.DistributionStrategy):
becomes local training where variables are assigned to local CPU or the only
GPU. When each worker has more than one GPU, operations will be replicated on
these GPUs. In both cases, operations are replicated but variables are not and
these workers share a common view for which paramater server a variable is
these workers share a common view for which parameter server a variable is
assigned to.
This class assumes between-graph replication will be used and works on a graph

2
tensorflow/lite/toco/dump_graphviz.cc
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@ -37,7 +37,7 @@ using toco::port::StringF;
namespace toco {
namespace {
// 'nslimit' is a graphviz (dot) paramater that limits the iterations during
// 'nslimit' is a graphviz (dot) parameter that limits the iterations during
// the layout phase. Omitting it allows infinite iterations, causing some
// complex graphs to never finish. A value of 125 produces good graphs
// while allowing complex graphs to finish.

2
tensorflow/python/keras/layers/kernelized.py
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@ -112,7 +112,7 @@ class RandomFourierFeatures(base_layer.Layer):
definitions above). When provided, it should be a positive float. If None,
the implementation chooses a default value (1.0 typically). Both the
approximation error of the kernel and the classification quality are
sensitive to this parameter. If trainable is set to True, this paramater
sensitive to this parameter. If trainable is set to True, this parameter
is learned end-to-end during training and the provided value serves as an
initialization value.
NOTE: When this layer is used to map the initial features and then the