| /external/tensorflow/tensorflow/contrib/model_pruning/python/ |
| D | pruning_test.py | 48 self.sparsity = variables.VariableV1(0.5, name="sparsity")
65 p = pruning.Pruning(spec=self.pruning_hparams, sparsity=self.sparsity)
67 sparsity = p._sparsity.eval()
68 self.assertAlmostEqual(sparsity, 0.5)
72 p = pruning.Pruning(spec=self.pruning_hparams, sparsity=self.sparsity)
74 spec=self.pruning_hparams, sparsity=self.sparsity)
76 sparsity = p._sparsity.eval()
77 self.assertAlmostEqual(sparsity, 0.5)
105 sparsity = variables.VariableV1(0.95, name="sparsity")
106 p = pruning.Pruning(sparsity=sparsity)
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| D | pruning.py | 223 def __init__(self, spec=None, global_step=None, sparsity=None): argument
250 self._sparsity = (sparsity
251 if sparsity is not None else self._setup_sparsity())
319 sparsity = math_ops.add(
325 return sparsity
348 weight_name, sparsity = val.split(':')
349 if float(sparsity) >= 1.0:
351 weight_sparsity_map[weight_name] = float(sparsity)
358 sparsity for name, sparsity in self._weight_sparsity_map.items()
399 sparsity = self._get_sparsity(weights.op.name)
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| D | strip_pruning_vars_test.py | 69 self.sparsity = variables.Variable(0.5, name="sparsity")
127 p = pruning.Pruning(pruning_hparams, sparsity=self.sparsity)
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| /external/tensorflow/tensorflow/contrib/model_pruning/ |
| D | README.md | 10 - [Block sparsity](#block-sparsity)
53 … name (or layer name):target sparsity pairs. Eg. [conv1:0.9,conv2/kernel:0.8]. For layers/weights …
59 | initial_sparsity | float | 0.0 | Initial sparsity value |
60 | target_sparsity | float | 0.5 | Target sparsity value |
61 | sparsity_function_begin_step | integer | 0 | The global step at this which the gradual sparsity f…
62 …nd_step | integer | 100 | The global step used as the end point for the gradual sparsity function |
63 | sparsity_function_exponent | float | 3.0 | exponent = 1 is linearly varying sparsity between init…
66 The sparsity $$s_t$$ at global step $$t$$ is given by:
76 #### Block Sparsity <a name="block-sparsity"></a>
78 …sparsity. To train models in which the weight tensors have block sparse structure, set *block_heig…
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| /external/webrtc/webrtc/common_audio/ |
| D | sparse_fir_filter.cc | 19 size_t sparsity, in SparseFIRFilter() argument
21 : sparsity_(sparsity), in SparseFIRFilter()
26 RTC_CHECK_GE(sparsity, 1u); in SparseFIRFilter()
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| D | sparse_fir_filter.h | 34 size_t sparsity,
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | sparse_add_op_test.py | 217 def _s2d_add_vs_sparse_add(sparsity, n, m, num_iters=50): argument
222 sp_t, unused_nnz = _sparsify(sp_vals, thresh=sparsity, index_dtype=np.int32)
246 for sparsity in [0.99, 0.5, 0.01]:
249 s2d_dt, sa_dt = _s2d_add_vs_sparse_add(sparsity, n, m)
250 print("%.2f \t %d \t %d \t %.4f \t %.4f \t %.2f" % (sparsity, n, m,
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| /external/tensorflow/tensorflow/contrib/boosted_trees/lib/testutil/ |
| D | batch_features_testutil.cc | 50 const double sparsity = in RandomlyInitializeBatchFeatures() local
52 const double density = 1 - sparsity; in RandomlyInitializeBatchFeatures()
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | sparse_matmul_op_test.cc | 33 void Sparsify(Tensor* t, float sparsity) { in Sparsify() argument
35 CHECK_LE(sparsity, 1); in Sparsify()
37 if (sparsity == 1) { in Sparsify()
43 if (rnd.Uniform(K) < sparsity * K) { in Sparsify()
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_SparseMatMul.pbtxt | 12 The gradient computation of this operation will only take advantage of sparsity
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| /external/eigen/Eigen/ |
| D | OrderingMethods | 54 * \note Some of these methods (like AMD or METIS), need the sparsity pattern
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| /external/tensorflow/tensorflow/contrib/factorization/g3doc/ |
| D | wals.md | 46 we decompose the norm into two terms, corresponding to the sparsity pattern of
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| /external/tensorflow/tensorflow/python/ops/ |
| D | nn_test.py | 76 sparsity = nn_impl.zero_fraction(
78 self.assertAllClose(1.0, self.evaluate(sparsity))
82 sparsity = nn_impl.zero_fraction(
84 self.assertAllClose(0.0, self.evaluate(sparsity))
89 sparsity = nn_impl.zero_fraction(value)
93 sess.run(sparsity, {value: [[0., 1.], [0.3, 2.]]}))
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| /external/swiftshader/third_party/subzero/docs/ |
| D | REGALLOC.rst | 64 sparsity of the data, resulting in stable performance as function size scales
95 by fitting well into the sparsity optimizations of their data structures.
160 As before, we need to take advantage of sparsity of variable uses across basic
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| D | DESIGN.rst | 596 algorithm if implemented naively. To improve things based on sparsity, we note
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| /external/eigen/doc/ |
| D | SparseLinearSystems.dox | 123 In the case where multiple problems with the same sparsity pattern have to be solved, then the "com…
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| /external/swiftshader/third_party/subzero/ |
| D | DESIGN.rst | 596 algorithm if implemented naively. To improve things based on sparsity, we note
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| /external/cldr/tools/java/org/unicode/cldr/util/data/transforms/ |
| D | internal_raw_IPA-old.txt | 183176 sparsity %10817
|