| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| D | input_pipeline_test.py | 50 def _make_csv_time_series(num_features, num_samples, test_tmpdir): argument
54 for i in range(num_samples)],
59 def _make_tfexample_series(num_features, num_samples, test_tmpdir): argument
62 for i in range(num_samples):
74 def _make_numpy_time_series(num_features, num_samples): argument
75 times = numpy.arange(num_samples)
124 filename = _make_csv_time_series(num_features=1, num_samples=50,
131 num_features=1, num_samples=50,
143 data = _make_numpy_time_series(num_features=1, num_samples=50)
148 filename = _make_csv_time_series(num_features=1, num_samples=50,
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | lstm_test.py | 37 num_samples = 2
45 input_shape=(num_samples, timesteps, embedding_dim))
63 num_samples = 2
73 x = np.random.random((num_samples, timesteps, embedding_dim))
74 y = np.random.random((num_samples, units))
78 num_samples = 2
87 input_shape=(num_samples, timesteps, embedding_dim))
91 num_samples = 2
99 input_shape=(num_samples, timesteps, embedding_dim))
160 num_samples = 2
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| D | gru_test.py | 36 num_samples = 2
44 input_shape=(num_samples, timesteps, embedding_dim))
47 num_samples = 2
56 x = np.random.random((num_samples, timesteps, embedding_dim))
57 y = np.random.random((num_samples, units))
61 num_samples = 2
70 input_shape=(num_samples, timesteps, embedding_dim))
74 num_samples = 2
82 input_shape=(num_samples, timesteps, embedding_dim))
85 num_samples = 2
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| D | simplernn_test.py | 35 num_samples = 2
43 input_shape=(num_samples, timesteps, embedding_dim))
46 num_samples = 2
54 x = np.random.random((num_samples, timesteps, embedding_dim))
55 y = np.random.random((num_samples, units))
59 num_samples = 2
68 input_shape=(num_samples, timesteps, embedding_dim))
71 num_samples = 2
80 input_shape=(num_samples, timesteps, embedding_dim))
142 num_samples = 2
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| D | cudnn_recurrent_test.py | 48 num_samples = 32
53 input_shape=(num_samples, timesteps, input_size))
64 num_samples = 32
69 input_shape=(num_samples, timesteps, input_size))
80 num_samples = 32
83 inputs = keras.Input(batch_shape=(num_samples, timesteps, input_size))
91 inputs = np.random.random((num_samples, timesteps, input_size))
105 num_samples = 32
117 np.ones((num_samples, timesteps, input_size)),
118 np.ones((num_samples, timesteps, units)))
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| D | convolutional_test.py | 36 num_samples = 2
44 input_shape=(num_samples, length, stack_size))
100 num_samples = 2
109 input_shape=(num_samples, num_row, num_col, stack_size))
167 num_samples = 2
177 input_shape=(num_samples, depth, num_row, num_col, stack_size))
259 num_samples = 2
262 shape = (num_samples, num_steps, input_dim)
309 num_samples = 2
314 inputs = np.ones((num_samples, input_num_row, input_num_col, stack_size))
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| D | local_test.py | 38 num_samples = 2
67 input_shape=(num_samples, num_steps, input_dim))
70 num_samples = 2
96 layer.build((num_samples, num_steps, input_dim))
99 keras.backend.variable(np.ones((num_samples,
114 layer.build((num_samples, num_steps, input_dim))
127 num_samples = 8
158 input_shape=(num_samples, num_row, num_col, stack_size))
163 num_samples = 8
187 input_shape=(num_samples, num_row, num_col, stack_size))
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| D | lstm_v2_test.py | 96 num_samples = 2
104 x = np.random.random((num_samples, timesteps, embedding_dim))
105 y = np.random.random((num_samples, units))
132 num_samples = 2
149 inputs = np.random.random((num_samples, timesteps, embedding_dim))
151 np.random.random((num_samples, units)) for _ in range(num_states)
153 targets = np.random.random((num_samples, units))
161 num_samples = 2
166 keras.backend.random_normal_variable((num_samples, units), 0, 1)
177 inputs = np.random.random((num_samples, timesteps, embedding_dim))
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| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_estimator_signals_test.py | 31 def make_input_fn(num_samples): argument
32 a = np.linspace(0, 100.0, num=num_samples)
47 def make_input_fn_with_labels(num_samples): argument
48 a = np.linspace(0, 100.0, num=num_samples)
66 num_samples = 4
70 input_fn, (a, b) = make_input_fn(num_samples=num_samples)
86 self.assertAllEqual(a[batch_size:num_samples], result['a'])
87 self.assertAllEqual(b[batch_size:num_samples], result['b'])
94 num_samples = 4
98 input_fn, (a, b) = make_input_fn(num_samples=num_samples)
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| /external/webrtc/webrtc/common_audio/ |
| D | wav_file.cc | 45 << (1.f * num_samples()) / (num_channels() * sample_rate()) << " s"; in FormatAsString()
67 size_t WavReader::ReadSamples(size_t num_samples, int16_t* samples) { in ReadSamples() argument
72 num_samples = std::min(num_samples, num_samples_remaining_); in ReadSamples()
74 fread(samples, sizeof(*samples), num_samples, file_handle_); in ReadSamples()
76 RTC_CHECK(read == num_samples || feof(file_handle_)); in ReadSamples()
82 size_t WavReader::ReadSamples(size_t num_samples, float* samples) { in ReadSamples() argument
85 for (size_t i = 0; i < num_samples; i += kChunksize) { in ReadSamples()
87 size_t chunk = std::min(kChunksize, num_samples - i); in ReadSamples()
121 void WavWriter::WriteSamples(const int16_t* samples, size_t num_samples) { in WriteSamples() argument
126 fwrite(samples, sizeof(*samples), num_samples, file_handle_); in WriteSamples()
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| D | wav_header_unittest.cc | 98 size_t num_samples = 0; in TEST() local
123 &bytes_per_sample, &num_samples)); in TEST()
144 &bytes_per_sample, &num_samples)); in TEST()
165 &bytes_per_sample, &num_samples)); in TEST()
187 &bytes_per_sample, &num_samples)); in TEST()
210 &bytes_per_sample, &num_samples)); in TEST()
229 &bytes_per_sample, &num_samples)); in TEST()
241 &bytes_per_sample, &num_samples)); in TEST()
275 size_t num_samples = 0; in TEST() local
279 &bytes_per_sample, &num_samples)); in TEST()
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| D | wav_file.h | 31 virtual size_t num_samples() const = 0;
50 void WriteSamples(const float* samples, size_t num_samples);
51 void WriteSamples(const int16_t* samples, size_t num_samples);
55 size_t num_samples() const override { return num_samples_; } in num_samples() function
78 size_t ReadSamples(size_t num_samples, float* samples);
79 size_t ReadSamples(size_t num_samples, int16_t* samples);
83 size_t num_samples() const override { return num_samples_; } in num_samples() function
109 size_t num_samples);
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| D | wav_header.cc | 66 size_t num_samples) { in CheckWavParameters() argument
104 if (num_samples > max_samples) in CheckWavParameters()
108 if (num_samples % num_channels != 0) in CheckWavParameters()
153 size_t num_samples) { in WriteWavHeader() argument
155 bytes_per_sample, num_samples)); in WriteWavHeader()
158 const size_t bytes_in_payload = bytes_per_sample * num_samples; in WriteWavHeader()
188 size_t* num_samples) { in ReadWavHeader() argument
217 *num_samples = bytes_in_payload / *bytes_per_sample; in ReadWavHeader()
239 *bytes_per_sample, *num_samples); in ReadWavHeader()
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| /external/tensorflow/tensorflow/python/kernel_tests/random/ |
| D | multinomial_op_test.py | 40 def composed_sampler(logits, num_samples): argument
43 tensor_shape.TensorShape([num_samples])))
64 num_samples = 1000
66 logits, num_samples, output_dtype=output_dtype))
67 self.assertAllEqual([[1] * num_samples, [2] * num_samples], samples)
112 num_samples = 21000
122 composed_freqs = self._do_sampling(logits, num_samples, composed_sampler)
123 native_freqs = self._do_sampling(logits, num_samples, native_sampler)
138 def _make_ops(self, num_samples, seed=None): argument
142 sample_op1 = random_ops.multinomial(logits, num_samples, seed)
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| /external/webrtc/webrtc/modules/audio_coding/neteq/ |
| D | statistics_calculator.cc | 143 void StatisticsCalculator::ExpandedVoiceSamples(size_t num_samples) { in ExpandedVoiceSamples() argument
144 expanded_speech_samples_ += num_samples; in ExpandedVoiceSamples()
147 void StatisticsCalculator::ExpandedNoiseSamples(size_t num_samples) { in ExpandedNoiseSamples() argument
148 expanded_noise_samples_ += num_samples; in ExpandedNoiseSamples()
151 void StatisticsCalculator::PreemptiveExpandedSamples(size_t num_samples) { in PreemptiveExpandedSamples() argument
152 preemptive_samples_ += num_samples; in PreemptiveExpandedSamples()
155 void StatisticsCalculator::AcceleratedSamples(size_t num_samples) { in AcceleratedSamples() argument
156 accelerate_samples_ += num_samples; in AcceleratedSamples()
159 void StatisticsCalculator::AddZeros(size_t num_samples) { in AddZeros() argument
160 added_zero_samples_ += num_samples; in AddZeros()
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| D | statistics_calculator.h | 42 void ExpandedVoiceSamples(size_t num_samples);
46 void ExpandedNoiseSamples(size_t num_samples);
50 void PreemptiveExpandedSamples(size_t num_samples);
53 void AcceleratedSamples(size_t num_samples);
56 void AddZeros(size_t num_samples);
62 void LostSamples(size_t num_samples);
67 void IncreaseCounter(size_t num_samples, int fs_hz);
73 void SecondaryDecodedSamples(int num_samples);
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| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| D | statistical_testing_test.py | 98 num_samples = 5000
102 num_samples, 0., 1., false_fail_rate=1e-6, false_pass_rate=1e-6)
108 samples = rng.uniform(size=num_samples).astype(np.float32)
119 num_samples = 5000
123 samples = rng.uniform(size=num_samples).astype(np.float32)
141 num_samples = 5000
145 samples = rng.uniform(size=num_samples).astype(np.float32)
166 num_samples = 4000
171 num_samples, 0., 1., num_samples, 0., 1.,
178 samples1 = rng.uniform(size=num_samples).astype(np.float32)
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| /external/libxaac/decoder/ |
| D | ixheaacd_mps_hybrid_filt.c | 50 WORD32 num_samples, WORD32 *filt_coeff) in ixheaacd_mps_hyb_filt_type1() argument
63 for (i = 0; i < num_samples; i++) { in ixheaacd_mps_hyb_filt_type1()
110 WORD32 num_samples, WORD32 *filt_coeff) in ixheaacd_mps_hyb_filt_type2() argument
123 for (i = 0; i < num_samples; i++) { in ixheaacd_mps_hyb_filt_type2()
175 WORD32 num_bands, WORD32 num_samples, in ixheaacd_mps_qmf_hybrid_analysis() argument
184 lf_samples_shift = BUFFER_LEN_LF_MPS - num_samples; in ixheaacd_mps_qmf_hybrid_analysis()
185 hf_samples_shift = BUFFER_LEN_HF_MPS - num_samples; in ixheaacd_mps_qmf_hybrid_analysis()
191 handle->lf_buffer[k][n].re = handle->lf_buffer[k][n + num_samples].re; in ixheaacd_mps_qmf_hybrid_analysis()
192 handle->lf_buffer[k][n].im = handle->lf_buffer[k][n + num_samples].im; in ixheaacd_mps_qmf_hybrid_analysis()
197 for (n = 0; n < num_samples; n++) { in ixheaacd_mps_qmf_hybrid_analysis()
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| /external/tensorflow/tensorflow/python/keras/ |
| D | model_subclassing_test.py | 416 num_samples = 1000
428 x1 = array_ops.ones((num_samples, input_dim))
429 x2 = array_ops.ones((num_samples, input_dim))
586 num_samples = 100
598 x = np.ones((num_samples, input_dim))
599 y = np.zeros((num_samples, num_classes))
606 num_samples = 1000
618 x1 = np.ones((num_samples, input_dim))
619 x2 = np.ones((num_samples, input_dim))
620 y1 = np.zeros((num_samples, num_classes[0]))
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | random_poisson_op.cc | 73 int num_rate, int num_samples,
80 int num_rate, int num_samples, in operator ()()
105 auto DoWork = [num_samples, num_rate, &rng, samples_flat, rate_flat]( in operator ()()
115 const int64 rate_idx = output_idx / num_samples; in operator ()()
134 for (int64 sample_idx = output_idx % num_samples; in operator ()()
135 sample_idx < num_samples && output_idx < limit_output; in operator ()()
196 for (int64 sample_idx = output_idx % num_samples; in operator ()()
197 sample_idx < num_samples && output_idx < limit_output; in operator ()()
269 num_rate * num_samples, kElementCost, DoWork); in operator ()()
294 const int64 num_samples = samples_shape.num_elements(); in Compute() local
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| D | multinomial_op_gpu.cu.cc | 43 const int32 num_samples, const float* scores, in MultinomialKernel() argument
62 int num_classes, int num_samples, in operator ()()
73 bsc.set(1, num_samples); in operator ()()
81 oso.set(1, num_samples); in operator ()()
83 Eigen::array<int, 3> bsc{batch_size, num_samples, num_classes}; in operator ()()
85 Eigen::array<int, 3> oso{1, num_samples, 1}; in operator ()()
105 /*in_dim0=*/batch_size * num_samples, in operator ()()
112 const int32 work_items = batch_size * num_samples * num_classes; in operator ()()
116 num_samples, scores.data(), maxima.data(), in operator ()()
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| D | multinomial_op.cc | 51 int num_classes, int num_samples,
77 int num_classes, int num_samples, in operator ()()
86 auto DoWork = [ctx, num_samples, num_classes, &gen, &output, &logits]( in operator ()()
94 gen_copy.Skip(start_row * (num_samples + 3) / 4); in operator ()()
128 for (int64 j = 0; j < num_samples; ++j) { in operator ()()
137 50 * (num_samples * std::log(num_classes) / std::log(2) + num_classes); in operator ()()
163 const int num_samples = num_samples_t.scalar<int>()(); in DoCompute() local
164 OP_REQUIRES(ctx, num_samples >= 0, in DoCompute()
166 "num_samples should be nonnegative, got ", num_samples)); in DoCompute()
183 ctx, ctx->allocate_output(0, TensorShape({batch_size, num_samples}), in DoCompute()
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| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| D | test_util.py | 43 num_samples=int(1e5), num_threshold=int(1e3), seed=42, argument
85 y = dist.sample(num_samples, seed=seed)
86 y = array_ops.reshape(y, shape=[num_samples, -1])
101 self.assertAllClose(probs_, counts_ / num_samples,
106 num_samples=int(1e5), seed=24, argument
129 x = math_ops.cast(dist.sample(num_samples, seed=seed), dtypes.float32)
201 num_samples=int(1e5), argument
291 def monte_carlo_hypersphere_volume(dist, num_samples, radius, center): argument
293 x = dist.sample(num_samples, seed=seed)
302 … values=[num_samples, radius, center] + dist._graph_parents): # pylint: disable=protected-access
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| /external/webrtc/webrtc/modules/video_coding/utility/ |
| D | moving_average.h | 24 bool GetAverage(size_t num_samples, T* average);
44 bool MovingAverage<T>::GetAverage(size_t num_samples, T* avg) { in GetAverage() argument
45 if (num_samples > samples_.size()) in GetAverage()
49 while (num_samples < samples_.size()) { in GetAverage()
54 *avg = sum_ / static_cast<T>(num_samples); in GetAverage()
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| /external/tensorflow/tensorflow/python/data/experimental/kernel_tests/ |
| D | directed_interleave_dataset_test.py | 59 def _testSampleFromDatasetsHelper(self, weights, num_datasets, num_samples): argument
66 dataset = dataset.take(num_samples)
70 for _ in range(num_samples):
79 num_samples = 5000
88 freqs = self._testSampleFromDatasetsHelper(probs, classes, num_samples)
89 self.assertLess(self._chi2(probs, freqs / num_samples), 1e-2)
93 freqs = self._testSampleFromDatasetsHelper(probs_ds, classes, num_samples)
94 self.assertLess(self._chi2(probs, freqs / num_samples), 1e-2)
|