experiments/STT-tensorflow
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Merge pull request #29193 from chosungmann:fix-typos

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PiperOrigin-RevId: 251835242
This commit is contained in:
TensorFlower Gardener 2019-06-06 05:59:19 -07:00
commit 05d496e412
5 changed files with 11 additions and 11 deletions
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12
tensorflow/c/eager/c_api_experimental_test.cc
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@ -267,10 +267,10 @@ TEST(CAPI, MonitoringMultipleSampler) {
TFE_MonitoringSamplerCellAdd(cell1, 2.0); TFE_MonitoringSamplerCellAdd(cell1, 2.0);
TF_Buffer* result1 = TF_NewBuffer(); TF_Buffer* result1 = TF_NewBuffer();
TFE_MonitoringSamplerCellValue(cell1, result1); TFE_MonitoringSamplerCellValue(cell1, result1);
tensorflow::HistogramProto hitogram1; tensorflow::HistogramProto histogram1;
EXPECT_TRUE(hitogram1.ParseFromString( EXPECT_TRUE(histogram1.ParseFromString(
{reinterpret_cast<const char*>(result1->data), result1->length})); {reinterpret_cast<const char*>(result1->data), result1->length}));
EXPECT_EQ(hitogram1.sum(), 3.0); EXPECT_EQ(histogram1.sum(), 3.0);
delete result1; delete result1;
auto* sampler2 = TFE_MonitoringNewSampler2("test/sampler2", buckets, status, auto* sampler2 = TFE_MonitoringNewSampler2("test/sampler2", buckets, status,
@ -281,10 +281,10 @@ TEST(CAPI, MonitoringMultipleSampler) {
TFE_MonitoringSamplerCellAdd(cell2, 3.0); TFE_MonitoringSamplerCellAdd(cell2, 3.0);
TF_Buffer* result2 = TF_NewBuffer(); TF_Buffer* result2 = TF_NewBuffer();
TFE_MonitoringSamplerCellValue(cell2, result2); TFE_MonitoringSamplerCellValue(cell2, result2);
tensorflow::HistogramProto hitogram2; tensorflow::HistogramProto histogram2;
EXPECT_TRUE(hitogram2.ParseFromString( EXPECT_TRUE(histogram2.ParseFromString(
{reinterpret_cast<const char*>(result2->data), result2->length})); {reinterpret_cast<const char*>(result2->data), result2->length}));
EXPECT_EQ(hitogram2.sum(), 5.0); EXPECT_EQ(histogram2.sum(), 5.0);
delete result2; delete result2;
TFE_MonitoringDeleteBuckets(buckets); TFE_MonitoringDeleteBuckets(buckets);

2
tensorflow/compiler/xla/client/client.h
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@ -42,7 +42,7 @@ class Client {
// Compile the computation with the given argument shapes and returns the // Compile the computation with the given argument shapes and returns the
// handle to the compiled executable. The compiled executable is cached on the // handle to the compiled executable. The compiled executable is cached on the
// service, and the returned handle can be used for exection without // service, and the returned handle can be used for execution without
// re-compile. // re-compile.
// * The shape and layout of the arguments being executed with will affect how // * The shape and layout of the arguments being executed with will affect how
// the computation is compiled. If argument_shapes is empty, the parameters' // the computation is compiled. If argument_shapes is empty, the parameters'

4
tensorflow/compiler/xla/service/cpu/runtime_matmul_mkl.cc
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@ -110,7 +110,7 @@ __xla_cpu_runtime_MKLSingleThreadedMatMulF32(const void* run_options_ptr,
int64 m, int64 n, int64 k, int64 m, int64 n, int64 k,
int32 transpose_lhs, int32 transpose_lhs,
int32 transpose_rhs) { int32 transpose_rhs) {
// Set the thread number to 1 for single threaded excution. // Set the thread number to 1 for single threaded execution.
int prev_num_threads = mkl_set_num_threads_local(1); int prev_num_threads = mkl_set_num_threads_local(1);
MatMulF32(nullptr, out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); MatMulF32(nullptr, out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs);
// Set thread number back to the previous number. // Set thread number back to the previous number.
@ -123,7 +123,7 @@ __xla_cpu_runtime_MKLSingleThreadedMatMulF64(const void* run_options_ptr,
double* rhs, int64 m, int64 n, double* rhs, int64 m, int64 n,
int64 k, int32 transpose_lhs, int64 k, int32 transpose_lhs,
int32 transpose_rhs) { int32 transpose_rhs) {
// Set the thread number to 1 for single threaded excution. // Set the thread number to 1 for single threaded execution.
int prev_num_threads = mkl_set_num_threads_local(1); int prev_num_threads = mkl_set_num_threads_local(1);
MatMulF64(nullptr, out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs); MatMulF64(nullptr, out, lhs, rhs, m, n, k, transpose_lhs, transpose_rhs);
// Set thread number back to the previous number. // Set thread number back to the previous number.

2
tensorflow/contrib/eager/python/examples/workshop/1_basic.ipynb
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@ -189,7 +189,7 @@
"\n", "\n",
"Implement a python function `bisecting_line_search(f, a, b, epsilon)` which returns a value such that `tf.abs(f(value)) < epsilon`.\n", "Implement a python function `bisecting_line_search(f, a, b, epsilon)` which returns a value such that `tf.abs(f(value)) < epsilon`.\n",
"\n", "\n",
"One thing to keep in mind: python's `==` opertor is not overloaded on Tensors, so you need to use `tf.equal` to compare for equality." "One thing to keep in mind: python's `==` operator is not overloaded on Tensors, so you need to use `tf.equal` to compare for equality."
] ]
}, },
{ {

2
tensorflow/python/keras/distribute/distributed_training_utils.py
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@ -893,7 +893,7 @@ def _make_execution_function_with_cloning(model, mode):
distributed_function = _make_graph_execution_function(model, mode) distributed_function = _make_graph_execution_function(model, mode)
# We cache the distributed execution function on the model since creating # We cache the distributed execution function on the model since creating
# distributed models and exection functions are expensive. # distributed models and execution functions are expensive.
distributed_model._distributed_function = distributed_function distributed_model._distributed_function = distributed_function
distributed_model._recompile_exec_function = False distributed_model._recompile_exec_function = False
return distributed_function return distributed_function