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author | Taehoon Lee <taehoonlee@snu.ac.kr> | 2017-06-11 14:23:54 +0900 |
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committer | Taehoon Lee <taehoonlee@snu.ac.kr> | 2017-06-11 14:23:54 +0900 |
commit | cc2b908fa6f2acf06ffccf341ad2af8cd24aa12f (patch) | |
tree | 02580d97f45e7edaa3cf9e0d3278bb687107d6d1 | |
parent | 504965336d1432ee96b4f0e9b78f65ee201e9be5 (diff) |
Fix typos
10 files changed, 25 insertions, 25 deletions
diff --git a/tensorflow/contrib/keras/python/keras/backend.py b/tensorflow/contrib/keras/python/keras/backend.py index b7adf9461a..7a00560308 100644 --- a/tensorflow/contrib/keras/python/keras/backend.py +++ b/tensorflow/contrib/keras/python/keras/backend.py @@ -3260,7 +3260,7 @@ def conv2d(x, padding: string, `"same"` or `"valid"`. data_format: `"channels_last"` or `"channels_first"`. Whether to use Theano or TensorFlow data format - for inputs/kernels/ouputs. + for inputs/kernels/outputs. dilation_rate: tuple of 2 integers. Returns: @@ -3308,7 +3308,7 @@ def conv2d_transpose(x, padding: string, `"same"` or `"valid"`. data_format: `"channels_last"` or `"channels_first"`. Whether to use Theano or TensorFlow data format - for inputs/kernels/ouputs. + for inputs/kernels/outputs. Returns: A tensor, result of transposed 2D convolution. @@ -3394,7 +3394,7 @@ def conv3d(x, padding: string, `"same"` or `"valid"`. data_format: `"channels_last"` or `"channels_first"`. Whether to use Theano or TensorFlow data format - for inputs/kernels/ouputs. + for inputs/kernels/outputs. dilation_rate: tuple of 3 integers. Returns: diff --git a/tensorflow/contrib/keras/python/keras/models_test.py b/tensorflow/contrib/keras/python/keras/models_test.py index 50aba43c24..99fd6e1cbe 100644 --- a/tensorflow/contrib/keras/python/keras/models_test.py +++ b/tensorflow/contrib/keras/python/keras/models_test.py @@ -105,7 +105,7 @@ class TestModelSaving(test.TestCase): out2 = model.predict(x) self.assertAllClose(out, out2, atol=1e-05) - def test_fuctional_model_saving(self): + def test_functional_model_saving(self): if h5py is None: return # Skip test if models cannot be saved. diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py index 145d5c40fa..d3bb0fda57 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/linear_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/linear_test.py @@ -729,7 +729,7 @@ class LinearClassifierTest(test.TestCase): self.assertLess(loss, 0.07) def testSdcaOptimizerRealValuedFeatures(self): - """Tests LinearClasssifier with SDCAOptimizer and real valued features.""" + """Tests LinearClassifier with SDCAOptimizer and real valued features.""" def input_fn(): return { @@ -776,7 +776,7 @@ class LinearClassifierTest(test.TestCase): self.assertLess(loss, 0.05) def testSdcaOptimizerBucketizedFeatures(self): - """Tests LinearClasssifier with SDCAOptimizer and bucketized features.""" + """Tests LinearClassifier with SDCAOptimizer and bucketized features.""" def input_fn(): return { @@ -802,7 +802,7 @@ class LinearClassifierTest(test.TestCase): self.assertGreater(scores['accuracy'], 0.9) def testSdcaOptimizerSparseFeatures(self): - """Tests LinearClasssifier with SDCAOptimizer and sparse features.""" + """Tests LinearClassifier with SDCAOptimizer and sparse features.""" def input_fn(): return { @@ -833,7 +833,7 @@ class LinearClassifierTest(test.TestCase): self.assertGreater(scores['accuracy'], 0.9) def testSdcaOptimizerWeightedSparseFeatures(self): - """LinearClasssifier with SDCAOptimizer and weighted sparse features.""" + """LinearClassifier with SDCAOptimizer and weighted sparse features.""" def input_fn(): return { @@ -864,7 +864,7 @@ class LinearClassifierTest(test.TestCase): self.assertGreater(scores['accuracy'], 0.9) def testSdcaOptimizerCrossedFeatures(self): - """Tests LinearClasssifier with SDCAOptimizer and crossed features.""" + """Tests LinearClassifier with SDCAOptimizer and crossed features.""" def input_fn(): return { @@ -897,7 +897,7 @@ class LinearClassifierTest(test.TestCase): self.assertGreater(scores['accuracy'], 0.9) def testSdcaOptimizerMixedFeatures(self): - """Tests LinearClasssifier with SDCAOptimizer and a mix of features.""" + """Tests LinearClassifier with SDCAOptimizer and a mix of features.""" def input_fn(): return { @@ -1509,7 +1509,7 @@ class LinearRegressorTest(test.TestCase): self.assertLess(loss, 0.05) def testSdcaOptimizerSparseFeaturesWithL1Reg(self): - """Tests LinearClasssifier with SDCAOptimizer and sparse features.""" + """Tests LinearClassifier with SDCAOptimizer and sparse features.""" def input_fn(): return { @@ -1581,7 +1581,7 @@ class LinearRegressorTest(test.TestCase): self.assertLess(l1_reg_weights_norm, no_l1_reg_weights_norm) def testSdcaOptimizerBiasOnly(self): - """Tests LinearClasssifier with SDCAOptimizer and validates bias weight.""" + """Tests LinearClassifier with SDCAOptimizer and validates bias weight.""" def input_fn(): """Testing the bias weight when it's the only feature present. @@ -1614,7 +1614,7 @@ class LinearRegressorTest(test.TestCase): regressor.get_variable_value('linear/bias_weight')[0], 0.25, err=0.1) def testSdcaOptimizerBiasAndOtherColumns(self): - """Tests LinearClasssifier with SDCAOptimizer and validates bias weight.""" + """Tests LinearClassifier with SDCAOptimizer and validates bias weight.""" def input_fn(): """Testing the bias weight when there are other features present. @@ -1676,7 +1676,7 @@ class LinearRegressorTest(test.TestCase): regressor.get_variable_value('linear/b/weight')[0], 0.0, err=0.05) def testSdcaOptimizerBiasAndOtherColumnsFabricatedCentered(self): - """Tests LinearClasssifier with SDCAOptimizer and validates bias weight.""" + """Tests LinearClassifier with SDCAOptimizer and validates bias weight.""" def input_fn(): """Testing the bias weight when there are other features present. diff --git a/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py b/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py index 6ebfeb0f16..284e2cfd7a 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py +++ b/tensorflow/contrib/learn/python/learn/estimators/model_fn_test.py @@ -123,7 +123,7 @@ class ModelFnopsTest(test.TestCase): self.assertAllEqual(predictions["probabilities"].eval(), regression_output.value.eval()) - def testEstimatorSpec_export_classsification(self): + def testEstimatorSpec_export_classification(self): predictions = self.create_predictions() output_alternatives = {"classification_head": ( constants.ProblemType.CLASSIFICATION, predictions)} @@ -143,7 +143,7 @@ class ModelFnopsTest(test.TestCase): self.assertAllEqual(predictions["classes"].eval(), classification_output.classes.eval()) - def testEstimatorSpec_export_classsification_with_missing_scores(self): + def testEstimatorSpec_export_classification_with_missing_scores(self): predictions = self.create_predictions() output_alternatives_predictions = predictions.copy() del output_alternatives_predictions["scores"] @@ -165,7 +165,7 @@ class ModelFnopsTest(test.TestCase): self.assertAllEqual(predictions["classes"].eval(), classification_output.classes.eval()) - def testEstimatorSpec_export_classsification_with_missing_scores_proba(self): + def testEstimatorSpec_export_classification_with_missing_scores_proba(self): predictions = self.create_predictions() output_alternatives_predictions = predictions.copy() del output_alternatives_predictions["scores"] @@ -187,7 +187,7 @@ class ModelFnopsTest(test.TestCase): self.assertAllEqual(predictions["classes"].eval(), classification_output.classes.eval()) - def testEstimatorSpec_export_classsification_with_missing_classes(self): + def testEstimatorSpec_export_classification_with_missing_classes(self): predictions = self.create_predictions() output_alternatives_predictions = predictions.copy() del output_alternatives_predictions["classes"] @@ -208,7 +208,7 @@ class ModelFnopsTest(test.TestCase): classification_output.scores.eval()) self.assertIsNone(classification_output.classes) - def testEstimatorSpec_export_classsification_with_nonstring_classes(self): + def testEstimatorSpec_export_classification_with_nonstring_classes(self): predictions = self.create_predictions() output_alternatives_predictions = predictions.copy() output_alternatives_predictions["classes"] = constant_op.constant( diff --git a/tensorflow/core/kernels/priority_queue.cc b/tensorflow/core/kernels/priority_queue.cc index 8884c0c4a0..9cbd832957 100644 --- a/tensorflow/core/kernels/priority_queue.cc +++ b/tensorflow/core/kernels/priority_queue.cc @@ -338,7 +338,7 @@ void PriorityQueue::TryDequeueMany(int num_elements, OpKernelContext* ctx, for (; s > 0; --s) { if (attempt->tuple.empty()) { // Only allocate tuple when we have something to dequeue - // so we don't use exceessive memory when there are many + // so we don't use excessive memory when there are many // blocked dequeue attempts waiting. attempt->tuple.reserve(num_components()); for (int i = 0; i < num_components(); ++i) { diff --git a/tensorflow/core/kernels/typed_conditional_accumulator_base.h b/tensorflow/core/kernels/typed_conditional_accumulator_base.h index dbd7de7ce0..1980f758fc 100644 --- a/tensorflow/core/kernels/typed_conditional_accumulator_base.h +++ b/tensorflow/core/kernels/typed_conditional_accumulator_base.h @@ -22,7 +22,7 @@ namespace tensorflow { /* * TypedConditionalAccumulatorBase is a templated companion of - * ConditionalAccumulatorBase which allows for subclassses to use different + * ConditionalAccumulatorBase which allows for subclasses to use different * types for the input gradients. (See ConditionalAccumulator and * SparseConditionalAccumulator.) * diff --git a/tensorflow/python/debug/wrappers/framework_test.py b/tensorflow/python/debug/wrappers/framework_test.py index 536365b692..2b2289d6a8 100644 --- a/tensorflow/python/debug/wrappers/framework_test.py +++ b/tensorflow/python/debug/wrappers/framework_test.py @@ -215,7 +215,7 @@ class DebugWrapperSessionTest(test_util.TensorFlowTestCase): wrapper_sess.partial_run_setup(self._p) def testInteractiveSessionInit(self): - """The wrapper should work also on other subclassses of session.Session.""" + """The wrapper should work also on other subclasses of session.Session.""" TestDebugWrapperSession( session.InteractiveSession(), self._dump_root, self._observer) diff --git a/tensorflow/python/estimator/canned/dnn_linear_combined.py b/tensorflow/python/estimator/canned/dnn_linear_combined.py index e991f6431c..30b64a32ff 100644 --- a/tensorflow/python/estimator/canned/dnn_linear_combined.py +++ b/tensorflow/python/estimator/canned/dnn_linear_combined.py @@ -374,7 +374,7 @@ class DNNLinearCombinedClassifier(estimator.Estimator): class DNNLinearCombinedRegressor(estimator.Estimator): - """An estimator for TensorFlow Linear and DNN joined models for regresssion. + """An estimator for TensorFlow Linear and DNN joined models for regression. Note: This estimator is also known as wide-n-deep. diff --git a/tensorflow/tensorboard/components/tf_graph_common/graph.ts b/tensorflow/tensorboard/components/tf_graph_common/graph.ts index 1b0abcfd85..cbd7b14539 100644 --- a/tensorflow/tensorboard/components/tf_graph_common/graph.ts +++ b/tensorflow/tensorboard/components/tf_graph_common/graph.ts @@ -723,7 +723,7 @@ export class MetaedgeImpl implements Metaedge { number { let opNode = <OpNode> h.node(edge.v); if (opNode.outputShapes == null) { - // No shape information. Asssume a single number. This gives + // No shape information. Assume a single number. This gives // a lower bound for the total size. return 1; } diff --git a/tensorflow/tools/tfprof/g3doc/command_line.md b/tensorflow/tools/tfprof/g3doc/command_line.md index 0d8d56809a..9f0de72e07 100644 --- a/tensorflow/tools/tfprof/g3doc/command_line.md +++ b/tensorflow/tools/tfprof/g3doc/command_line.md @@ -126,7 +126,7 @@ tfprof> -show_name_regexes .* -hide_name_regexes IsVariableInitialized_[0-9]+,save\/.*,^zeros[0-9_]* -account_displayed_op_only false -# supported select fileds. Availability depends on --[run_meta|checkpoint|op_log]_path. +# supported select fields. Availability depends on --[run_meta|checkpoint|op_log]_path. # [bytes|micros|params|float_ops|occurrence|tensor_value|device|op_types] -select params # format: output_type:key=value,key=value... |