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Searched refs:num_batches (Results 1 – 25 of 48) sorted by relevance

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/external/tensorflow/tensorflow/contrib/distribute/python/
Dmetrics_v1_test.py143 def _expected_fn(num_batches): argument
145 return num_batches * 2 - 0.5
156 def _expected_fn(num_batches): argument
157 return [3./4, 3./8, 3./12, 4./16][num_batches - 1]
172 def _expected_fn(num_batches): argument
177 mean([0.5, 1./3, 1./3, 0., 0.])][num_batches - 1]
191 def _expected_fn(num_batches): argument
196 mean([2./8, 1./7, 1./7, 0., 0.])][num_batches - 1]
209 def _expected_fn(num_batches): argument
214 first = 2. * num_batches - 2.
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/external/tensorflow/tensorflow/core/kernels/
Deigen_pooling_test.cc31 const int num_batches = 13; in TEST() local
37 Tensor<float, 4> input(depth, input_rows, input_cols, num_batches); in TEST()
38 Tensor<float, 4> result(depth, output_rows, output_cols, num_batches); in TEST()
51 EXPECT_EQ(result.dimension(3), num_batches); in TEST()
53 for (int b = 0; b < num_batches; ++b) { in TEST()
79 const int num_batches = 13; in TEST() local
85 Tensor<float, 4, RowMajor> input(num_batches, input_cols, input_rows, depth); in TEST()
86 Tensor<float, 4, RowMajor> result(num_batches, output_cols, output_rows, in TEST()
100 EXPECT_EQ(result.dimension(0), num_batches); in TEST()
102 for (int b = 0; b < num_batches; ++b) { in TEST()
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Dparameterized_truncated_normal_op_test.cc27 static Graph* PTruncatedNormal(int num_batches, int samples_per_batch) { in PTruncatedNormal() argument
30 shape_t.flat<int32>().setValues({num_batches, samples_per_batch}); in PTruncatedNormal()
33 Tensor means_t(DT_FLOAT, TensorShape({num_batches})); in PTruncatedNormal()
35 Tensor stdevs_t(DT_FLOAT, TensorShape({num_batches})); in PTruncatedNormal()
38 Tensor minvals_t(DT_FLOAT, TensorShape({num_batches})); in PTruncatedNormal()
40 Tensor maxvals_t(DT_FLOAT, TensorShape({num_batches})); in PTruncatedNormal()
56 static Graph* PTruncatedNormal2SD(int num_batches, int samples_per_batch) { in PTruncatedNormal2SD() argument
59 shape_t.flat<int32>().setValues({num_batches, samples_per_batch}); in PTruncatedNormal2SD()
61 Tensor means_t(DT_FLOAT, TensorShape({num_batches})); in PTruncatedNormal2SD()
63 Tensor stdevs_t(DT_FLOAT, TensorShape({num_batches})); in PTruncatedNormal2SD()
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Deigen_backward_spatial_convolutions_test.cc499 const int num_batches = 13; in TEST() local
510 num_batches); in TEST()
513 num_batches); in TEST()
525 EXPECT_EQ(input_backward.dimension(3), num_batches); in TEST()
527 for (int b = 0; b < num_batches; ++b) { in TEST()
554 const int num_batches = 13; in TEST() local
564 Tensor<float, 4, RowMajor> input_backward(num_batches, input_cols, input_rows, in TEST()
568 Tensor<float, 4, RowMajor> output_backward(num_batches, output_cols, in TEST()
578 EXPECT_EQ(input_backward.dimension(0), num_batches); in TEST()
583 for (int b = 0; b < num_batches; ++b) { in TEST()
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Dparameterized_truncated_normal_op.cc52 void operator()(OpKernelContext* ctx, const CPUDevice& d, int64 num_batches, in operator ()()
307 Shard(worker_threads.num_threads, worker_threads.workers, num_batches, in operator ()()
339 int32 num_batches = shape_tensor.flat<int32>()(0); in Compute() local
346 const int32 num_elements = num_batches * samples_per_batch; in Compute()
380 int32 size = num_batches * samples_per_batch; in Compute()
384 num_batches = adjusted_batches; in Compute()
392 means_tensor.dim_size(0) == num_batches, in Compute()
400 stddevs_tensor.dim_size(0) == num_batches, in Compute()
408 minvals_tensor.dim_size(0) == num_batches, in Compute()
416 maxvals_tensor.dim_size(0) == num_batches, in Compute()
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Deigen_spatial_convolutions_test.cc197 const int num_batches = 13; in TEST() local
204 Tensor<float, 4> input(input_depth, input_rows, input_cols, num_batches); in TEST()
206 Tensor<float, 4> result(output_depth, output_rows, output_cols, num_batches); in TEST()
219 EXPECT_EQ(result.dimension(3), num_batches); in TEST()
221 for (int b = 0; b < num_batches; ++b) { in TEST()
249 const int num_batches = 13; in TEST() local
259 Tensor<float, 4> input(input_depth, input_rows, input_cols, num_batches); in TEST()
261 Tensor<float, 4> result(output_depth, output_rows, output_cols, num_batches); in TEST()
274 EXPECT_EQ(result.dimension(3), num_batches); in TEST()
277 for (int b = 0; b < num_batches; ++b) { in TEST()
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Dnon_max_suppression_op.cc233 const int num_batches = inp_boxes.dim_size(0); in BatchedNonMaxSuppressionOp() local
239 std::vector<std::vector<float>> nmsed_boxes(num_batches); in BatchedNonMaxSuppressionOp()
241 std::vector<std::vector<float>> nmsed_scores(num_batches); in BatchedNonMaxSuppressionOp()
243 std::vector<std::vector<float>> nmsed_classes(num_batches); in BatchedNonMaxSuppressionOp()
250 for (int batch = 0; batch < num_batches; ++batch) { in BatchedNonMaxSuppressionOp()
401 TensorShape boxes_shape({num_batches, per_batch_size, 4}); in BatchedNonMaxSuppressionOp()
407 TensorShape scores_shape({num_batches, per_batch_size}); in BatchedNonMaxSuppressionOp()
418 TensorShape valid_detections_shape({num_batches}); in BatchedNonMaxSuppressionOp()
423 for (int i = 0; i < num_batches; ++i) { in BatchedNonMaxSuppressionOp()
Dparameterized_truncated_normal_op_gpu.cu.cc54 TruncatedNormalKernel(random::PhiloxRandom gen, T* data, int64 num_batches, in TruncatedNormalKernel() argument
235 void operator()(OpKernelContext* ctx, const GPUDevice& d, int64 num_batches, in operator ()()
247 0, d.stream(), gen, output.data(), num_batches, samples_per_batch, in operator ()()
/external/tensorflow/tensorflow/contrib/eager/python/examples/linear_regression/
Dlinear_regression_test.py51 num_batches = 2
54 batch_size, num_batches)
72 true_w, true_b, noise_level=0., batch_size=64, num_batches=40)
87 num_batches = 200
94 num_batches=num_batches)
110 examples_per_sec = num_epochs * num_batches * batch_size / wall_time
114 iters=num_epochs * num_batches,
Dlinear_regression_graph_test.py30 num_batches = 200
38 num_batches=num_batches)
75 examples_per_sec = num_epochs * num_batches * batch_size / wall_time
79 iters=num_epochs * num_batches,
Dlinear_regression.py98 def synthetic_dataset(w, b, noise_level, batch_size, num_batches): argument
102 num_batches)
106 num_batches): argument
118 return tf.data.Dataset.range(num_batches).map(batch)
/external/tensorflow/tensorflow/contrib/slim/python/slim/data/
Dprefetch_queue_test.py43 num_batches = 5
48 counter = examples.count_up_to(num_batches * batch_size)
62 for i in range(num_batches):
80 num_batches = 5
85 counter = examples.count_up_to(num_batches * batch_size)
100 for _ in range(num_batches):
109 np.arange(0, num_batches * batch_size))
120 num_batches = 4
125 counter = examples.count_up_to(num_batches * batch_size)
141 for _ in range(int(num_batches / 2)):
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/external/tensorflow/tensorflow/python/kernel_tests/
Dfractional_max_pool_op_test.py200 num_batches = 5
206 tensor_shape = (num_batches, num_rows, num_cols, num_channels)
216 num_batches = 5
222 tensor_shape = (num_batches, num_rows, num_cols, num_channels)
235 for num_batches in [1, 3]:
239 tensor_shape = (num_batches, num_rows, num_cols, num_channels)
251 num_batches = 3
255 tensor_shape = (num_batches, num_rows, num_cols, num_channels)
272 num_batches = 3
276 tensor_shape = (num_batches, num_rows, num_cols, num_channels)
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Dfractional_avg_pool_op_test.py198 num_batches = 5
204 tensor_shape = (num_batches, num_rows, num_cols, num_channels)
238 for num_batches in [1, 3]:
242 tensor_shape = (num_batches, num_rows, num_cols, num_channels)
254 num_batches = 3
258 tensor_shape = (num_batches, num_rows, num_cols, num_channels)
275 num_batches = 3
279 tensor_shape = (num_batches, num_rows, num_cols, num_channels)
353 for num_batches in [1, 3]:
359 input_shape = (num_batches, num_rows, num_cols, num_channels)
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Dself_adjoint_eig_op_test.py129 num_batches = int(np.prod(x_e.shape[:-1]))
131 x_e = np.reshape(x_e, [num_batches] + [n])
132 x_v = np.reshape(x_v, [num_batches] + [n, n])
133 y_e = np.reshape(y_e, [num_batches] + [n])
134 y_v = np.reshape(y_v, [num_batches] + [n, n])
135 for i in range(num_batches):
/external/tensorflow/tensorflow/python/training/
Dinput_test.py467 num_batches = 3
470 counter = examples.count_up_to(num_batches * batch_size)
494 for i in range(num_batches):
552 num_batches = 3
555 counter = examples.count_up_to(num_batches * batch_size)
565 for i in range(num_batches):
584 num_batches = 3
587 counter = examples.count_up_to(num_batches * batch_size)
598 for i in range(num_batches):
620 num_batches = 3
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/external/tensorflow/tensorflow/contrib/data/python/kernel_tests/
Dslide_dataset_op_test.py90 num_batches = (count * 7 - (
92 for i in range(num_batches):
154 num_batches = (count * 7 - (
156 for i in range(num_batches):
217 num_batches = (10 - 5) // 3 + 1
218 for i in range(num_batches):
246 num_batches = (10 - 5) // 3 + 1
247 for i in range(num_batches):
/external/tensorflow/tensorflow/lite/kernels/
Dbasic_rnn_test.cc218 int num_batches() { return batches_; } in num_batches() function in tflite::__anon35c533a00111::RNNOpModel
265 (rnn.input_size() * rnn.num_batches()); in TEST()
292 (rnn.input_size() * rnn.num_batches()); in TEST()
320 (rnn.input_size() * rnn.num_batches()); in TEST()
Dunidirectional_sequence_lstm_test.cc200 int num_batches() { return n_batch_; } in num_batches() function in tflite::__anon0e31a6420111::UnidirectionalLSTMOpModel
345 const int num_batches = input.size(); in VerifyGoldens() local
346 EXPECT_GT(num_batches, 0); in VerifyGoldens()
354 for (int b = 0; b < num_batches; ++b) { in VerifyGoldens()
358 lstm->SetInput(((i * num_batches) + b) * num_inputs, batch_start, in VerifyGoldens()
363 for (int b = 0; b < num_batches; ++b) { in VerifyGoldens()
380 for (int b = 0; b < num_batches; ++b) { in VerifyGoldens()
388 for (int b = 0; b < num_batches; ++b) { in VerifyGoldens()
Dlstm_test.cc226 int num_batches() { return n_batch_; } in num_batches() function in tflite::__anon5af325910111::LSTMOpModel
371 const int num_batches = input.size(); in VerifyGoldens() local
372 EXPECT_GT(num_batches, 0); in VerifyGoldens()
378 for (int b = 0; b < num_batches; ++b) { in VerifyGoldens()
389 for (int b = 0; b < num_batches; ++b) { in VerifyGoldens()
1769 const int num_batches = input.size(); in VerifyGoldens() local
1770 EXPECT_GT(num_batches, 0); in VerifyGoldens()
1776 for (int b = 0; b < num_batches; ++b) { in VerifyGoldens()
1788 for (int b = 0; b < num_batches; ++b) { in VerifyGoldens()
/external/tensorflow/tensorflow/contrib/eager/python/examples/spinn/
Ddata_test.py254 self.assertEqual(4, train_data.num_batches(1))
255 self.assertEqual(2, train_data.num_batches(2))
256 self.assertEqual(2, train_data.num_batches(3))
257 self.assertEqual(1, train_data.num_batches(4))
/external/tensorflow/tensorflow/python/data/kernel_tests/
Dwindow_test.py135 num_batches = (10 - 5) // 3 + 1
140 dense_shape=[5, 1]) for i in range(num_batches)
158 num_batches = (10 - 5) // 3 + 1
159 for i in range(num_batches):
/external/tensorflow/tensorflow/contrib/gan/python/eval/python/
Dclassifier_metrics_impl.py316 def classifier_score(images, classifier_fn, num_batches=1): argument
346 images, num_or_size_splits=num_batches)
456 num_batches=1): argument
499 real_images, num_or_size_splits=num_batches)
501 generated_images, num_or_size_splits=num_batches)
/external/tensorflow/tensorflow/python/data/experimental/kernel_tests/
Dmap_and_batch_test.py90 num_batches = (28 * 7) // 14
91 for i in range(num_batches):
105 num_batches = int(math.ceil((14 * 7) / 8))
106 for i in range(num_batches - 1):
116 self.assertAllEqual(component[((num_batches - 1) * 8 + j) % 7]**2,
/external/libxkbcommon/xkbcommon/src/x11/
Dutil.c162 const size_t num_batches = ROUNDUP(count, SIZE) / SIZE; in adopt_atoms() local
165 for (size_t batch = 0; batch < num_batches; batch++) { in adopt_atoms()

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