/external/tensorflow/tensorflow/python/kernel_tests/v1_compat_tests/ |
D | scatter_nd_ops_test.py | 51 num_updates = indices.size // ixdim 57 flat_updates = updates.reshape((num_updates, slice_size)) 87 num_updates = indices_shape[0] 98 indices = np.array(all_indices[:num_updates]) 100 if num_updates > 1 and repeat_indices: 101 indices = indices[:num_updates // 2] 102 for _ in range(num_updates - num_updates // 2): 104 indices, [indices[np.random.randint(num_updates // 2)]], axis=0) 106 indices = _AsType(indices[:num_updates], itype) 108 updates_shape = (num_updates,)
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/external/webrtc/modules/audio_processing/agc/ |
D | loudness_histogram_unittest.cc | 48 EXPECT_EQ(hist_->num_updates(), 0); in TestClean() 65 int num_updates = 0; in RunTest() local 72 num_updates = 0; in RunTest() 79 num_updates++; in RunTest() 80 EXPECT_EQ(hist_->num_updates(), num_updates); in RunTest()
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D | loudness_histogram.h | 46 int num_updates() const { return num_updates_; } in num_updates() function
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D | agc.cc | 55 if (histogram_->num_updates() < kNumAnalysisFrames) { in GetRmsErrorDb()
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/external/tensorflow/tensorflow/compiler/tests/ |
D | scatter_nd_op_test.py | 52 num_updates = indices.size // ixdim 58 flat_updates = updates.reshape((num_updates, slice_size)) 84 num_updates = indices_shape[0] 95 indices = np.array(all_indices[:num_updates]) 97 if num_updates > 1 and repeat_indices: 98 indices = indices[:num_updates // 2] 99 for _ in range(num_updates - num_updates // 2): 101 indices, [indices[np.random.randint(num_updates // 2)]], axis=0) 103 indices = _AsType(indices[:num_updates], itype) 105 updates_shape = (num_updates,)
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D | adadelta_test.py | 34 num_updates = 4 # number of ADADELTA steps to perform 98 update = [None] * num_updates 100 for step in range(num_updates):
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/external/tensorflow/tensorflow/python/training/ |
D | moving_averages.py | 368 num_updates=None, argument 393 self._num_updates = num_updates 483 num_updates = math_ops.cast( 486 (1.0 + num_updates) / (10.0 + num_updates))
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D | adadelta_test.py | 38 num_updates = 4 # number of ADADELTA steps to perform 108 update = [None] * num_updates 110 for step in range(num_updates):
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D | moving_averages_test.py | 260 ema = moving_averages.ExponentialMovingAverage(0.25, num_updates=1) 267 0.25, num_updates=1, zero_debias=True) 273 ema = moving_averages.ExponentialMovingAverage(0.25, num_updates=1) 280 0.25, num_updates=1, zero_debias=True)
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/external/tensorflow/tensorflow/python/kernel_tests/array_ops/ |
D | scatter_nd_ops_test.py | 65 num_updates = indices.size // ixdim 71 flat_updates = updates.reshape((num_updates, slice_size)) 121 num_updates = indices_shape[0] 133 indices = np.array(all_indices[:num_updates]) 135 if num_updates > 1 and repeat_indices: 136 indices = indices[:num_updates // 2] 137 for _ in range(num_updates - num_updates // 2): 139 indices, [indices[np.random.randint(num_updates // 2)]], axis=0) 141 indices = _AsType(indices[:num_updates], itype) 143 updates_shape = (num_updates,) [all …]
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/external/tensorflow/tensorflow/python/keras/optimizer_v2/ |
D | adadelta_test.py | 45 num_updates = 4 # number of ADADELTA steps to perform 98 update = [None] * num_updates 100 for step in range(num_updates):
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/external/tensorflow/tensorflow/core/kernels/ |
D | scatter_nd_op.cc | 52 int64 num_updates) { in ValidEmptyOutputShape() argument 53 if (num_indices == 0 && num_updates == 0) { in ValidEmptyOutputShape() 57 return (num_inputs != 0 && num_indices != 0 && num_updates != 0); in ValidEmptyOutputShape() 820 int64* slice_dim, Index* num_updates, in PrepareAndValidateInputs() argument 885 *num_updates = indices_shape.num_elements() / safe_slice_dim; in PrepareAndValidateInputs() 906 Index num_updates; in DoScatterNd() local 909 shape, indices, updates, &slice_dim, &num_updates, &slice_size)); in DoScatterNd() 913 auto updates_flat = updates.shaped<T, 2>({num_updates, slice_size}); in DoScatterNd()
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D | range_sampler.cc | 194 int num_updates = std::min(static_cast<int>(values.size()), in Update() local 196 for (int i = 0; i < num_updates; i++) { in Update()
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D | resource_variable_ops.cc | 918 int64 num_updates = updates.NumElements(); in DoCompute() local 919 OP_REQUIRES(c, num_updates % N == 0, in DoCompute() 924 auto updates_flat = updates.shaped<T, 2>({N, num_updates / N}); in DoCompute()
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/external/tensorflow/tensorflow/tools/api/golden/v2/ |
D | tensorflow.train.-exponential-moving-average.pbtxt | 11 …argspec: "args=[\'self\', \'decay\', \'num_updates\', \'zero_debias\', \'name\'], varargs=None, ke…
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/external/tensorflow/tensorflow/tools/api/golden/v1/ |
D | tensorflow.train.-exponential-moving-average.pbtxt | 11 …argspec: "args=[\'self\', \'decay\', \'num_updates\', \'zero_debias\', \'name\'], varargs=None, ke…
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/external/libchrome/dbus/ |
D | property_unittest.cc | 128 void WaitForUpdates(size_t num_updates) { in WaitForUpdates() argument 129 while (updated_properties_.size() < num_updates) { in WaitForUpdates() 133 for (size_t i = 0; i < num_updates; ++i) in WaitForUpdates()
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/external/tensorflow/tensorflow/core/api_def/base_api/ |
D | api_def_TensorScatterUpdate.pbtxt | 48 * `indices` must have at least 2 axes: `(num_updates, index_depth)`.
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/external/tensorflow/tensorflow/compiler/mlir/tensorflow/ir/ |
D | tf_generated_ops.td | 16729 * `indices` must have at least 2 axes: `(num_updates, index_depth)`.
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