diff options
10 files changed, 12 insertions, 12 deletions
diff --git a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py index d44e258bd2..42865ed404 100644 --- a/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py +++ b/tensorflow/contrib/distributions/python/ops/bijectors/affine_impl.py @@ -120,7 +120,7 @@ class _TriLPlusVDVTLightweightOperatorPD(object): Doesn't actually do the sqrt! Named as such to agree with API. - To compute (M + V D V.T), we use the the Woodbury matrix identity: + To compute (M + V D V.T), we use the Woodbury matrix identity: inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M) where, C = inv(D) + V.T inv(M) V. @@ -166,7 +166,7 @@ class _TriLPlusVDVTLightweightOperatorPD(object): def _woodbury_sandwiched_term(self): """Computes the sandwiched term in the Woodbury identity. - Computes the "`C`" in the the identity: + Computes the "`C`" in the identity: inv(M + V D V.T) = inv(M) - inv(M) V inv(C) V.T inv(M) where, C = inv(D) + V.T inv(M) V. diff --git a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py index 642c7f1b54..b659988a56 100644 --- a/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py +++ b/tensorflow/contrib/seq2seq/python/ops/attention_wrapper.py @@ -327,7 +327,7 @@ class LuongAttention(_BaseAttentionMechanism): raise ValueError( "Incompatible or unknown inner dimensions between query and keys. " "Query (%s) has units: %s. Keys (%s) have units: %s. " - "Perhaps you need to set num_units to the the keys' dimension (%s)?" + "Perhaps you need to set num_units to the keys' dimension (%s)?" % (query, depth, self.keys, key_units, key_units)) dtype = query.dtype diff --git a/tensorflow/core/framework/tensor.h b/tensorflow/core/framework/tensor.h index 49eecc0b08..a164fe61b5 100644 --- a/tensorflow/core/framework/tensor.h +++ b/tensorflow/core/framework/tensor.h @@ -307,7 +307,7 @@ class Tensor { /// Returns the data as an Eigen::Tensor with NDIMS dimensions, collapsing the /// first 'begin' Tensor dimensions into the first dimension of the result and /// the Tensor dimensions of the last dims() - 'begin' - NDIMS into the last - /// dimension of the result. If 'begin' < 0 then the the |'begin'| leading + /// dimension of the result. If 'begin' < 0 then the |'begin'| leading /// dimensions of size 1 will be added. If 'begin' + NDIMS > dims() then /// 'begin' + NDIMS - dims() trailing dimensions of size 1 will be added. template <typename T, size_t NDIMS = 3> diff --git a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc index 484a8860d8..9e48c411dc 100644 --- a/tensorflow/core/grappler/costs/virtual_scheduler_test.cc +++ b/tensorflow/core/grappler/costs/virtual_scheduler_test.cc @@ -441,7 +441,7 @@ TEST_F(VirtualSchedulerTest, ComplexDependency) { 1 /* control dependency */); EXPECT_EQ(expected_size, cpu_state.memory_usage); - // Nodes currrently in memory: bn's port -1, 0, and 2, and x's port 0. + // Nodes currently in memory: bn's port -1, 0, and 2, and x's port 0. std::set<std::pair<string, int>> nodes_in_memory; std::transform( cpu_state.nodes_in_memory.begin(), cpu_state.nodes_in_memory.end(), diff --git a/tensorflow/core/protobuf/worker.proto b/tensorflow/core/protobuf/worker.proto index cf05aece39..e476a84a13 100644 --- a/tensorflow/core/protobuf/worker.proto +++ b/tensorflow/core/protobuf/worker.proto @@ -171,7 +171,7 @@ message ExecutorOpts { }; message RunGraphRequest { - // session_handle is the the master-generated unique id for this session. + // session_handle is the master-generated unique id for this session. // If session_handle is non-empty, it must be the same as used when // registering the graph. If it is empty, a single global namespace is used to // search for the graph_handle. diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py index 4eead79531..e05b1ff557 100644 --- a/tensorflow/python/ops/data_flow_ops.py +++ b/tensorflow/python/ops/data_flow_ops.py @@ -1582,7 +1582,7 @@ class StagingArea(BaseStagingArea): This is mostly useful for limiting the number of tensors on devices such as GPUs. - All get() and peek() commands block if the the requested data + All get() and peek() commands block if the requested data is not present in the Staging Area. """ diff --git a/tensorflow/python/ops/distributions/special_math.py b/tensorflow/python/ops/distributions/special_math.py index f96eafed71..3a804c941a 100644 --- a/tensorflow/python/ops/distributions/special_math.py +++ b/tensorflow/python/ops/distributions/special_math.py @@ -324,7 +324,7 @@ def log_ndtr(x, series_order=3, name="log_ndtr"): def _log_ndtr_lower(x, series_order): - """Asymptotic expansion version of `Log[cdf(x)]`, apppropriate for `x<<-1`.""" + """Asymptotic expansion version of `Log[cdf(x)]`, appropriate for `x<<-1`.""" x_2 = math_ops.square(x) # Log of the term multiplying (1 + sum) log_scale = -0.5 * x_2 - math_ops.log(-x) - 0.5 * math.log(2. * math.pi) diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py index 49a4aba473..cc6528d1f5 100644 --- a/tensorflow/python/ops/rnn_cell_impl.py +++ b/tensorflow/python/ops/rnn_cell_impl.py @@ -606,7 +606,7 @@ class DropoutWrapper(RNNCell): """Create a cell with added input, state, and/or output dropout. If `variational_recurrent` is set to `True` (**NOT** the default behavior), - then the the same dropout mask is applied at every step, as described in: + then the same dropout mask is applied at every step, as described in: Y. Gal, Z Ghahramani. "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks". https://arxiv.org/abs/1512.05287 diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py index 7a6abc8e61..20ae082ee1 100644 --- a/tensorflow/python/ops/tensor_array_ops.py +++ b/tensorflow/python/ops/tensor_array_ops.py @@ -87,7 +87,7 @@ class TensorArray(object): the shape constraints of each of the elements of the TensorArray. Need not be fully defined. colocate_with_first_write_call: If `True`, the TensorArray will be - colocated on the same device as the the Tensor used on its first write + colocated on the same device as the Tensor used on its first write (write operations include `write`, `unstack`, and `split`). If `False`, the TensorArray will be placed on the device determined by the device context available during its initialization. diff --git a/tensorflow/tools/graph_transforms/quantize_weights_test.cc b/tensorflow/tools/graph_transforms/quantize_weights_test.cc index e1a105bdd3..63c5b5a64d 100644 --- a/tensorflow/tools/graph_transforms/quantize_weights_test.cc +++ b/tensorflow/tools/graph_transforms/quantize_weights_test.cc @@ -90,13 +90,13 @@ class QuantizeWeightsTest : public ::testing::Test { EXPECT_EQ("Const", q_weights_const->op()); EXPECT_EQ(DT_QUINT8, q_weights_const->attr().at("dtype").type()); - // Run the the original graph. + // Run the original graph. std::unique_ptr<Session> original_session(NewSession(SessionOptions())); TF_ASSERT_OK(original_session->Create(original_graph_def)); std::vector<Tensor> original_outputs; TF_ASSERT_OK(original_session->Run({}, {"output"}, {}, &original_outputs)); - // Run the the quantized graph. + // Run the quantized graph. std::unique_ptr<Session> quantized_session(NewSession(SessionOptions())); TF_ASSERT_OK(quantized_session->Create(quantized_graph_def)); std::vector<Tensor> quantized_outputs; |