| /external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
| losses_test.py | 39 weights = array_ops.ones([10, 1], dtypes.float32)
52 labels_positive, weights, predictions_tensor, eps=eps)
55 labels_negative, weights, predictions_tensor, eps=eps)
83 weights = array_ops.ones([5, 1], dtypes.float32)
88 loss_tensor, _ = losses.per_example_squared_loss(labels, weights,
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| /external/tensorflow/tensorflow/contrib/saved_model/python/saved_model/ |
| reader_test.py | 69 # - add with weights.
76 # - simply add the model (weights are not updated).
83 # - to add the model (weights are not updated).
90 # - to add the model (weights are not updated).
97 # - to add the model (weights are not updated).
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| /external/tensorflow/tensorflow/core/kernels/ |
| bincount_op.cc | 40 const typename TTypes<T, 1>::ConstTensor& weights,
70 if (weights.size()) {
71 partial_bins(worker_id, value) += weights(i);
105 const auto weights = weights_t.flat<T>(); variable
111 ctx, arr, weights, output));
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| bincount_op_gpu.cu.cc | 40 const typename TTypes<T, 1>::ConstTensor& weights,
42 if (weights.size() != 0) {
44 "Weights should not be passed as it should be "
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| /external/tensorflow/tensorflow/core/lib/random/ |
| weighted_picker.h | 79 // The sum of the weights should not exceed 2^31 - 2
81 void SetWeightsFromArray(int N, const int32* weights);
106 // the sum of the weights of its children.
114 // Rebuild the tree weights using the leaf weights
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| distribution_sampler.h | 18 // The values taken by the variable are [0, N) and relative weights for each
26 // The advantage of that implementation is that weights can be adjusted
48 explicit DistributionSampler(const gtl::ArraySlice<float>& weights);
|
| /external/tensorflow/tensorflow/docs_src/api_guides/python/ |
| contrib.losses.md | 84 Note that when using weights for the losses, the final average is computed
85 by rescaling the losses by the weights and then dividing by the total number of
86 non-zero samples. For an arbitrary set of weights, this may not necessarily
90 weights are an array [1, 0.5, 3, 9], then the average loss is:
96 However, with a single loss function and an arbitrary set of weights, one can
|
| contrib.layers.md | 45 `fn(weights)`. The loss is typically added to
64 Optimize weights given a loss.
79 of `summarize_collection` to `VARIABLES`, `WEIGHTS` and `BIASES`, respectively.
|
| /external/tensorflow/tensorflow/python/keras/_impl/keras/layers/ |
| recurrent_test.py | 149 weights = model.get_weights() variable in class:RNNTest.test_minimal_rnn_cell_layer.MinimalRNNCell
155 model.set_weights(weights)
172 weights = model.get_weights() variable in class:RNNTest.test_minimal_rnn_cell_layer.MinimalRNNCell
178 model.set_weights(weights)
244 weights = model.get_weights() variable in class:RNNTest.test_rnn_cell_with_constants_layer.RNNCellWithConstants
251 model.set_weights(weights)
261 model.set_weights(weights)
284 weights = model.get_weights() variable in class:RNNTest.test_rnn_cell_with_constants_layer.RNNCellWithConstants
290 model.set_weights(weights)
358 weights = model.get_weights( variable in class:RNNTest.test_rnn_cell_with_constants_layer_passing_initial_state.RNNCellWithConstants
[all...] |
| /external/tensorflow/tensorflow/contrib/cudnn_rnn/python/ops/ |
| cudnn_rnn_ops.py | 202 and is used to save/restore the weights and biases parameters in a
262 weights, biases = self._OpaqueParamsToCanonical()
263 (weights, weight_names), (biases, bias_names) = self._TransformCanonical(
264 weights, biases)
269 params = weights + biases
282 weights, biases = self._ReverseTransformCanonical(restored_tensors)
283 weights = [array_ops.reshape(w, [-1]) for w in weights]
284 opaque_params = self._CanonicalToOpaqueParams(weights, biases)
304 2 list for weights and biases respectively
[all...] |
| /external/tensorflow/tensorflow/python/keras/_impl/keras/applications/ |
| mobilenet.py | 31 all 16 models from the paper can be built, with ImageNet weights provided.
35 For each of these `alpha` values, weights for 4 different input image sizes
60 The weights for all 16 models are obtained and translated
315 weights='imagenet',
354 weights: one of `None` (random initialization),
356 or the path to the weights file to be loaded.
374 if no `weights` argument is specified.
380 ValueError: in case of invalid argument for `weights`,
391 if not (weights in {'imagenet', None} or os.path.exists(weights))
[all...] |
| /external/tensorflow/tensorflow/contrib/estimator/python/estimator/ |
| head_test.py | 657 weights = np.array([[1.], [2.]], dtype=np.float32)
669 'example_weights': weights
691 weights = np.array([[1.], [2.]], dtype=np.float32)
703 'example_weights': weights
825 # Average over classes, sum over weights.
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/analysis/interpolation/ |
| MicrosphereInterpolator.java | 42 * Default exponent used the weights calculation.
52 * Exponent used in the power law that computes the weights of the
70 * weights of the sample data.
|
| /external/dng_sdk/source/ |
| dng_resample.cpp | 177 // Round to each set to weights to a multiple of 8 entries.
225 // Evaluate kernel function for 32 bit weights.
244 // Scale 32 bit weights so total of weights is 1.0.
257 // Round off 32 bit weights to 16 bit weights.
314 // Find radius of this kernel. Unlike with 1d resample weights (see
385 // Evaluate kernel function for 32 bit weights.
433 // Scale 32 bit weights so total of weights is 1.0
[all...] |
| /external/icu/icu4c/source/i18n/ |
| collationfastlatin.h | 54 // use at most about 150 primary weights,
55 // where about 94 primary weights are possibly-variable (space/punct/symbol/currency),
56 // at most 4 secondary before-common weights,
57 // at most 4 secondary after-common weights,
58 // at most 16 secondary high weights (in secondary CEs), and
59 // at most 4 tertiary after-common weights.
60 // The following ranges are designed to support slightly more weights than that.
137 * Lookup: Add this offset to secondary weights, except for completely ignorable CEs.
156 * Lookup: Add this offset to tertiary weights, except for completely ignorable CEs.
158 * Must be greater than case bits as well, so that with combined case+tertiary weights
[all...] |
| /external/llvm/test/CodeGen/X86/ |
| code_placement_ignore_succ_in_inner_loop.ll | 5 ; to a node in an outer loop, the weights on edges in the inner loop should be
47 ; node in its peer loop, the weights on edges in the first loop should be
77 ; node in its outer loop, the weights on edges in the outer loop should be
|
| /external/stressapptest/src/ |
| pattern.h | 41 // All weights are added up, a random number is
42 // chosen between 0-sum(weights), and the
104 int weightcount_; // Total count of pattern weights.
|
| /external/swiftshader/third_party/LLVM/include/llvm/Analysis/ |
| BranchProbabilityInfo.h | 31 // weight to an edge that may have siblings with non-zero weights. This can
38 DenseMap<Edge, uint32_t> Weights;
40 // Get sum of the block successors' weights.
|
| /external/tensorflow/tensorflow/contrib/lite/toco/graph_transformations/ |
| convert_pure_conv_to_depthwise.cc | 38 // Yield until the weights are resolved as a constant array.
51 "%s is purely convolutional (input/weights depth is 1), replacing it by "
77 // Shuffle the weights.
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/kernels/v4/ |
| grow_stats_test.cc | 78 std::vector<float> weights = {2.3, 20.3, 1.1}; local
80 new TestableInputTarget(labels, weights, 1));
121 std::vector<float> weights = {2.3, 20.3, 1.1}; local
123 new TestableInputTarget(labels, weights, 1));
177 std::vector<float> weights = {1, 1, 1}; local
179 new TestableInputTarget(labels, weights, 1));
223 std::vector<float> weights = {1, 1}; local
224 TestableInputTarget target(labels, weights, 1);
331 std::vector<float> weights = {2.3, 20.3, 1.1}; local
333 new TestableInputTarget(labels, weights, 1))
400 std::vector<float> weights = {2.3, 20.3, 1.1}; local
[all...] |
| /external/tensorflow/tensorflow/contrib/tensor_forest/proto/ |
| fertile_stats.proto | 18 // by storing the sum of input weights and the sum of the squares of the
19 // input weights. Weighted gini is then: 1 - (square / sum * sum).
31 // The sum of the weights of the training examples that we have seen.
|
| /frameworks/support/leanback/src/main/java/androidx/leanback/widget/ |
| ParallaxEffect.java | 70 * Weights are used when there are three or more marker values.
96 * Weights are used when there are three or more marker values.
98 * @param weights A list of Float objects that represents weight associated with each variable
103 public final void setWeights(float... weights) {
104 for (float weight : weights) {
112 for (float weight : weights) {
121 * Weights are used when there are three or more marker values.
123 * @param weights A list of Float objects that represents weight associated with each variable
129 public final ParallaxEffect weights(float... weights) { method in class:ParallaxEffect
[all...] |
| /packages/apps/SettingsIntelligence/src/com/android/settings/intelligence/suggestions/ranking/ |
| SuggestionRanker.java | 43 private static final Map<String, Double> WEIGHTS = new HashMap<String, Double>() {{
103 for (String feature : WEIGHTS.keySet()) {
104 sum += WEIGHTS.get(feature) * features.get(feature);
|
| /external/llvm/lib/IR/ |
| MDBuilder.cpp | 42 MDNode *MDBuilder::createBranchWeights(ArrayRef<uint32_t> Weights) {
43 assert(Weights.size() >= 1 && "Need at least one branch weights!");
45 SmallVector<Metadata *, 4> Vals(Weights.size() + 1);
49 for (unsigned i = 0, e = Weights.size(); i != e; ++i)
50 Vals[i + 1] = createConstant(ConstantInt::get(Int32Ty, Weights[i]));
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/estimator_batch/ |
| estimator.py | 54 weight_column_name: Name of the column for weights, or None if not
73 def loss_fn(labels, logits, weights=None):
75 labels=labels, logits=logits, weights=weights,
137 weight_column_name: Name of the column for weights, or None if not
202 weight_column_name: Name of the column for weights, or None if not
|
