Searched defs:batch_size (Results 1 - 25 of 139) sorted by relevance

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/external/tensorflow/tensorflow/core/kernels/
H A Dxent_op_test.cc24 static Graph* Xent(int batch_size, int num_classes) { argument
26 Tensor logits(DT_FLOAT, TensorShape({batch_size, num_classes}));
28 Tensor labels(DT_FLOAT, TensorShape({batch_size, num_classes}));
H A Dmultinomial_op_test.cc27 static Graph* Multinomial(int batch_size, int num_classes, int num_samples) { argument
29 Tensor logits_t(DT_FLOAT, TensorShape({batch_size, num_classes}));
H A Dsparse_xent_op_test.cc26 static Graph* SparseXent(int batch_size, int num_classes) { argument
28 Tensor logits(DT_FLOAT, TensorShape({batch_size, num_classes}));
30 Tensor labels(DT_INT64, TensorShape({batch_size}));
35 for (int i = 0; i < batch_size; ++i) {
H A Dattention_ops.cc40 // Expect input tensor of rank 4 with dimensions (batch_size, height, width,
49 "input must be 4-dimensional (batch_size, height, width, depth)",
52 const int64 batch_size = input_shape.dim_size(0); variable
72 OP_REQUIRES(context, offsets.shape().dim_size(0) == batch_size,
88 offset_vec.reserve(batch_size);
89 for (int i = 0; i < batch_size; ++i) {
H A Dcholesky_op.cc144 const int64 batch_size = input_reshaped.dimension(0); variable
146 dev_info.push_back(solver->GetDeviceLapackInfo(batch_size, "potrf"));
149 for (int batch = 0; batch < batch_size; ++batch) {
H A Dconv_grad_ops.h233 int64 batch_size; member in struct:tensorflow::ConvBackpropDimensions
H A Dsoftmax_op_functor.h31 // logits: dim: batch_size, num_classes.
32 // softmax: dims: batch_size, num_classes.
49 const int batch_size = logits.dimension(kBatchDim); local
56 Eigen::DSizes<int, 2> batch_by_one(batch_size, 1);
62 batch_by_one.set(0, batch_size);
H A Dxent_op.h31 // logits: batch_size, num_classes.
32 // labels: batch_size, num_classes.
33 // scratch: temporary tensor, dims: batch_size, 1
34 // loss: output tensor for the loss, dims: batch_size.
35 // backprop: output tensor for the backprop, dims: batch_size, num_classes.
60 const int batch_size = logits.dimension(kBatchDim); local
69 batch_only[0] = batch_size;
71 batch_by_one[0] = batch_size;
79 batch_by_one.set(0, batch_size);
81 batch_only.set(0, batch_size);
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/external/tensorflow/tensorflow/core/ops/
H A Dcandidate_sampling_ops.cc35 DimensionHandle batch_size = c->Dim(true_classes_shape, 0); local
39 c->set_output(1, c->Matrix(batch_size, num_true));
/external/tensorflow/tensorflow/compiler/tf2xla/kernels/
H A Dcategorical_op.cc56 const int64 batch_size = logits_shape.dim_size(0); variable
62 {batch_size, num_samples, num_classes}};
H A Dbatchtospace_op.cc50 const int64 batch_size = input_shape[0]; local
67 ctx, batch_size % block_num_elems == 0,
68 errors::InvalidArgument("Input batch dimension (", batch_size,
73 reshaped_shape[block_rank] = batch_size / block_num_elems;
107 reshaped_permuted_shape[0] = batch_size / block_num_elems;
H A Dspacetobatch_op.cc88 const int64 batch_size = input_shape[0]; local
90 reshaped_padded_shape[0] = batch_size;
138 output_shape[0] = batch_size * block_num_elems;
/external/tensorflow/tensorflow/contrib/boosted_trees/lib/learner/common/partitioners/
H A Dexample_partitioner.cc28 const int64 batch_size = features.batch_size(); local
29 if (batch_size <= 0) {
51 boosted_trees::utils::ParallelFor(batch_size, desired_parallelism,
61 const int64 batch_size = features.batch_size(); local
62 if (batch_size <= 0) {
84 boosted_trees::utils::ParallelFor(batch_size, desired_parallelism,
/external/tensorflow/tensorflow/contrib/boosted_trees/lib/models/
H A Dmultiple_additive_trees.cc34 const int64 batch_size = features.batch_size(); local
35 if (batch_size <= 0) {
76 boosted_trees::utils::ParallelFor(batch_size, worker_threads->NumThreads(),
/external/tensorflow/tensorflow/contrib/boosted_trees/lib/utils/
H A Dbatch_features.h33 explicit BatchFeatures(int64 batch_size) : batch_size_(batch_size) {} argument
75 int64 batch_size() const { return batch_size_; }
H A Dparallel_for.cc22 void ParallelFor(int64 batch_size, int64 desired_parallelism, argument
27 do_work(0, batch_size);
31 1, std::min(static_cast<int64>(desired_parallelism), batch_size));
32 const int64 block_size = (batch_size + num_shards - 1) / num_shards;
34 const int num_shards_used = (batch_size + block_size - 1) / block_size;
36 for (int64 start = block_size; start < batch_size; start += block_size) {
37 auto end = std::min(start + block_size, batch_size);
45 do_work(0, std::min(block_size, batch_size));
/external/tensorflow/tensorflow/contrib/cudnn_rnn/ops/
H A Dcudnn_rnn_ops_test.cc44 int batch_size = 3; local
48 std::vector<int> input_shape = {seq_length, batch_size, num_units};
49 std::vector<int> input_h_shape = {num_layers * dir_count, batch_size,
51 std::vector<int> output_shape = {seq_length, batch_size,
/external/tensorflow/tensorflow/contrib/lite/kernels/internal/
H A Dkernel_utils.cc22 int input_size, int num_units, int batch_size,
26 tensor_utils::VectorBatchVectorAssign(bias_ptr, num_units, batch_size,
30 input_weights_ptr, num_units, input_size, input_ptr_batch, batch_size,
35 batch_size, output_ptr_batch, /*result_stride=*/1);
38 output_ptr_batch, num_units * batch_size, activation, output_ptr_batch);
39 tensor_utils::VectorBatchVectorAssign(output_ptr_batch, num_units, batch_size,
20 RnnBatchStep(const float* input_ptr_batch, const float* input_weights_ptr, const float* recurrent_weights_ptr, const float* bias_ptr, int input_size, int num_units, int batch_size, TfLiteFusedActivation activation, float* hidden_state_ptr_batch, float* output_ptr_batch) argument
/external/tensorflow/tensorflow/contrib/tpu/ops/
H A Dtpu_embedding_ops.cc247 int64 batch_size = config.batch_size(); local
252 c->set_output(table_id, c->Matrix(batch_size * num_features, width));
/external/tensorflow/tensorflow/contrib/nearest_neighbor/kernels/
H A Dhyperplane_lsh_probes.cc105 int batch_size = products_tensor.dim_size(0); variable
109 TensorShape output_shape({batch_size, num_probes});
125 batch_size, cost_per_unit, [&](int64 start, int64 end) {
/external/tensorflow/tensorflow/core/kernels/data/
H A Dbatch_dataset_op.cc34 int64 batch_size = 0; variable
36 ParseScalarArgument<int64>(ctx, "batch_size", &batch_size));
38 ctx, batch_size > 0,
41 *output = new Dataset(ctx, batch_size, input);
47 Dataset(OpKernelContext* ctx, int64 batch_size, const DatasetBase* input) argument
48 : GraphDatasetBase(ctx), batch_size_(batch_size), input_(input) {
53 // then we could always report `batch_size` as the 0th dimension.
87 Node* batch_size = nullptr; variable
88 TF_RETURN_IF_ERROR(b->AddScalar(batch_size_, &batch_size));
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/external/libdrm/intel/
H A Dtest_decode.c78 size_t batch_size; local
80 read_file(batch_filename, &batch_ptr, &batch_size);
83 batch_size / 4);
97 size_t ref_size, batch_size; local
105 read_file(batch_filename, &batch_ptr, &batch_size);
120 batch_size / 4);
/external/tensorflow/tensorflow/contrib/boosted_trees/lib/testutil/
H A Dbatch_features_testutil.cc30 const int64 batch_size = static_cast<int64>(batch_features->batch_size()); local
36 for (int64 j = 0; j < batch_size; ++j) {
39 auto dense_tensor = Tensor(tensorflow::DT_FLOAT, {batch_size, 1});
53 for (int64 k = 0; k < static_cast<int64>(density * batch_size) + 1; ++k) {
54 indices.insert(rng->Uniform64(batch_size));
75 tensorflow::test::FillValues<int64>(&shape_tensor, {batch_size, 1});
/external/tensorflow/tensorflow/contrib/lite/kernels/
H A Dsoftmax_test.cc80 const int batch_size = 2; local
88 SoftmaxOpModel m(batch_size, input_size, beta);
90 m.SetInput(0, input_buffer, input_buffer + input_size * batch_size);
94 std::unique_ptr<float[]> output_buffer(new float[input_size * batch_size]);
95 static tflite::Dims<4> input_dims = {{input_size, 1, 1, batch_size},
102 output_buffer.get() + input_size * batch_size);
108 const int batch_size = 2; local
116 SoftmaxOpModel m(batch_size, input_size, beta);
118 m.SetInput(0, input_buffer, input_buffer + input_size * batch_size);
122 std::unique_ptr<float[]> output_buffer(new float[input_size * batch_size]);
[all...]
/external/tensorflow/tensorflow/contrib/seq2seq/python/ops/
H A Dbasic_decoder.py76 def batch_size(self): member in class:BasicDecoder
77 return self._helper.batch_size

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