experiments/STT-tensorflow
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Fix typos

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This commit is contained in:
Taehoon Lee 2017-06-16 11:31:49 +09:00
parent c007ee2c1f
commit dbde3987ca
7 changed files with 9 additions and 9 deletions
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2
tensorflow/compiler/xla/service/compile_only_service.h
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@ -55,7 +55,7 @@ class CompileOnlyService : public Service {
// Override Service methods that require or imply the existence of an // Override Service methods that require or imply the existence of an
// execute backend. Note that this does not include TransferToClient, as // execute backend. Note that this does not include TransferToClient, as
// computing contants produces global data that we may wish to transfer. // computing constants produces global data that we may wish to transfer.
tensorflow::Status Execute(const ExecuteRequest* arg, tensorflow::Status Execute(const ExecuteRequest* arg,
ExecuteResponse* result) override { ExecuteResponse* result) override {
return Unimplemented("CompileOnlyService does not support execution."); return Unimplemented("CompileOnlyService does not support execution.");

2
tensorflow/contrib/distributions/python/ops/vector_laplace_linear_operator.py
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@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
# ============================================================================== # ==============================================================================
"""Vectorized Laplace distribution class, directly using LinearOpeartor.""" """Vectorized Laplace distribution class, directly using LinearOperator."""
from __future__ import absolute_import from __future__ import absolute_import
from __future__ import division from __future__ import division

2
tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py
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@ -344,7 +344,7 @@ class LuongAttention(_BaseAttentionMechanism):
# [batch_size, 1, depth] . [batch_size, depth, max_time] # [batch_size, 1, depth] . [batch_size, depth, max_time]
# resulting in an output shape of: # resulting in an output shape of:
# [batch_time, 1, max_time]. # [batch_time, 1, max_time].
# we then squeee out the center singleton dimension. # we then squeeze out the center singleton dimension.
score = math_ops.matmul(query, self.keys, transpose_b=True) score = math_ops.matmul(query, self.keys, transpose_b=True)
score = array_ops.squeeze(score, [1]) score = array_ops.squeeze(score, [1])

2
tensorflow/core/framework/graph_def_util.h
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@ -62,7 +62,7 @@ Status AddDefaultAttrsToGraphDef(GraphDef* graph_def,
// attr with a default was added). Note that this will not affect // attr with a default was added). Note that this will not affect
// attrs with non-default values, so you must run a // attrs with non-default values, so you must run a
// ValidateGraphDef...() function to see if the result is in fact // ValidateGraphDef...() function to see if the result is in fact
// compatible. If not nulllptr, the op/attr pairs that were removed // compatible. If not nullptr, the op/attr pairs that were removed
// are added to '*op_attr_removed'. // are added to '*op_attr_removed'.
// //
// Expected usage, for a producer that wants to prepare a graph for // Expected usage, for a producer that wants to prepare a graph for

2
tensorflow/core/grappler/optimizers/constant_folding.h
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@ -28,7 +28,7 @@ namespace grappler {
const char kConstantFoldingConst[] = "ConstantFolding"; const char kConstantFoldingConst[] = "ConstantFolding";
const char kConstantFoldingCtrl[] = "ConstantFoldingCtrl"; const char kConstantFoldingCtrl[] = "ConstantFoldingCtrl";
// Contant folding optimization for a graph. // Constant folding optimization for a graph.
class ConstantFolding : public GraphOptimizer { class ConstantFolding : public GraphOptimizer {
public: public:
ConstantFolding(); ConstantFolding();

2
tensorflow/core/grappler/optimizers/layout_optimizer.cc
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@ -927,7 +927,7 @@ struct TuningConfig {
// Conv2DBackpropFilter will use a specialized GEMM implementation, which is // Conv2DBackpropFilter will use a specialized GEMM implementation, which is
// usually faster than the NCHW implementation. The downside is that this // usually faster than the NCHW implementation. The downside is that this
// might result in more non-cancellable layout conversion nodes (implemented // might result in more non-cancellable layout conversion nodes (implemented
// by the Tranpose op). // by the Transpose op).
bool no_gemm; bool no_gemm;
}; };

6
tensorflow/python/ops/lookup_ops.py
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@ -988,9 +988,9 @@ def index_table_from_tensor(vocabulary_list,
Sample Usages: Sample Usages:
```python ```python
vocabulary_list = t.constant(["emerson", "lake", "palmer") vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
table = tf.contrib.lookup.index_table_from_tensor( table = tf.contrib.lookup.index_table_from_tensor(
vocabulary_list=vocabulary_list, num_oov_buckets=1, default_value=-1) mapping=vocabulary_list, num_oov_buckets=1, default_value=-1)
features = tf.constant(["emerson", "lake", "and", "palmer"]) features = tf.constant(["emerson", "lake", "and", "palmer"])
ids = table.lookup(features) ids = table.lookup(features)
... ...
@ -1160,7 +1160,7 @@ def index_to_string_table_from_tensor(vocabulary_list,
Sample Usages: Sample Usages:
```python ```python
vocabulary_list = t.constant(["emerson", "lake", "palmer") vocabulary_list = tf.constant(["emerson", "lake", "palmer"])
indices = tf.constant([1, 5], tf.int64) indices = tf.constant([1, 5], tf.int64)
table = tf.contrib.lookup.index_to_string_table_from_tensor( table = tf.contrib.lookup.index_to_string_table_from_tensor(
vocabulary_list, default_value="UNKNOWN") vocabulary_list, default_value="UNKNOWN")