| /external/tensorflow/tensorflow/contrib/boosted_trees/lib/utils/ |
| dropout_utils_test.cc | 44 // Fill an weights.
296 std::vector<float> GetWeightsByIndex(const std::vector<float>& weights,
301 res.push_back(weights[index]);
306 void MergeLastElements(const int32 last_n, std::vector<float>* weights) {
309 sum += weights->back();
310 weights->pop_back();
312 weights->push_back(sum);
320 std::vector<float> weights = {1.0, 1.0, 1.0, 1.0, 1.0}; local
329 std::vector<float> current_weights = weights;
342 std::vector<float> current_weights = weights;
365 std::vector<float> weights = {1.1, 2.1, 3.1, 4.1, 5.1}; local
410 std::vector<float> weights = {1.0, 1.0, 1.0, 1.0, 1.0}; local
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| metrics_test.py | 236 metrics.mean(values, weights=1.0),
237 metrics.mean(values, weights=np.ones((1, 1, 1))),
238 metrics.mean(values, weights=np.ones((1, 1, 1, 1))),
239 metrics.mean(values, weights=np.ones((1, 1, 1, 1, 1))),
240 metrics.mean(values, weights=np.ones((1, 1, 4))),
241 metrics.mean(values, weights=np.ones((1, 1, 4, 1))),
242 metrics.mean(values, weights=np.ones((1, 2, 1))),
243 metrics.mean(values, weights=np.ones((1, 2, 1, 1))),
244 metrics.mean(values, weights=np.ones((1, 2, 4))),
245 metrics.mean(values, weights=np.ones((1, 2, 4, 1)))
[all...] |
| /external/tensorflow/tensorflow/contrib/nn/python/ops/ |
| sampling_ops.py | 31 def _rank_resample(weights, biases, inputs, sampled_values, num_resampled,
57 weights: From `rank_sampled_softmax_loss`.
94 # the logsumexp computation with the partitioned weights, which yields
97 weights, sampled, partition_strategy, transform_fn=logsumexp_logit)
110 def rank_sampled_softmax_loss(weights,
159 weights=weights,
166 logits = tf.matmul(inputs, tf.transpose(weights))
175 weights: A `Tensor` or `PartitionedVariable` of shape `[num_classes, dim]`,
199 if `len(weights) > 1`. Currently `"div"` and `"mod"` are supported
[all...] |
| /external/tensorflow/tensorflow/python/ops/ |
| confusion_matrix.py | 99 name=None, weights=None):
113 If `weights` is not `None`, then each prediction contributes its
138 weights: An optional `Tensor` whose shape matches `predictions`.
147 mismatched shapes, or if `weights` is not `None` and its shape doesn't
151 (predictions, labels, num_classes, weights)) as name:
184 if weights is not None:
185 predictions.get_shape().assert_is_compatible_with(weights.get_shape())
186 weights = math_ops.cast(weights, dtype)
191 if weights is None else weights
[all...] |
| /external/tensorflow/tensorflow/contrib/metrics/python/ops/ |
| metric_ops_test.py | 236 weights = weights_queue.dequeue()
238 mean, update_op = metrics.streaming_mean(values, weights)
258 weights = weights_queue.dequeue()
260 mean, update_op = metrics.streaming_mean(values, weights)
285 weights = weights_queue.dequeue()
287 mean, update_op = metrics.streaming_mean(values, weights)
307 weights = weights_queue.dequeue()
309 mean, update_op = metrics.streaming_mean(values, weights)
409 # Create the queue that populates the weights.
416 weights = weights_queue.dequeue(
[all...] |
| /external/guava/guava/src/com/google/common/cache/ |
| Weigher.java | 21 * Calculates the weights of cache entries.
31 * Returns the weight of a cache entry. There is no unit for entry weights; rather they are simply
|
| /external/icu/icu4c/source/i18n/ |
| collationkeys.h | 121 // Secondary level: Compress up to 33 common weights as 05..25 or 25..45.
127 // Case level, lowerFirst: Compress up to 7 common weights as 1..7 or 7..13.
133 // Case level, upperFirst: Compress up to 13 common weights as 3..15.
138 // Tertiary level only (no case): Compress up to 97 common weights as 05..65 or 65..C5.
144 // Tertiary with case, lowerFirst: Compress up to 33 common weights as 05..25 or 25..45.
150 // Tertiary with case, upperFirst: Compress up to 33 common weights as 85..A5 or A5..C5.
156 // Quaternary level: Compress up to 113 common weights as 1C..8C or 8C..FC.
161 // Primary weights shifted to quaternary level must be encoded with
|
| /external/tensorflow/tensorflow/contrib/slim/python/slim/nets/ |
| alexnet_test.py | 77 'alexnet_v2/conv1/weights',
79 'alexnet_v2/conv2/weights',
81 'alexnet_v2/conv3/weights',
83 'alexnet_v2/conv4/weights',
85 'alexnet_v2/conv5/weights',
87 'alexnet_v2/fc6/weights',
89 'alexnet_v2/fc7/weights',
91 'alexnet_v2/fc8/weights',
|
| overfeat_test.py | 77 'overfeat/conv1/weights',
79 'overfeat/conv2/weights',
81 'overfeat/conv3/weights',
83 'overfeat/conv4/weights',
85 'overfeat/conv5/weights',
87 'overfeat/fc6/weights',
89 'overfeat/fc7/weights',
91 'overfeat/fc8/weights',
|
| /frameworks/ml/nn/runtime/test/specs/V1_0/ |
| fully_connected_float.mod.py | 19 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 1}", [2]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
|
| fully_connected_float_3.mod.py | 19 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 2}", [2, 4]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
|
| fully_connected_float_large.mod.py | 19 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 5}", [2, 3, 4, 5, 6]) # num_units = 1, input_size = 5 variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
|
| fully_connected_quant8.mod.py | 19 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM", "{1, 1}, 0.5f, 0", [2]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
|
| fully_connected_quant8_2.mod.py | 19 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM", "{3, 10}, 0.5f, 127", variable
26 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act_relu).To(out0)
|
| fully_connected_quant8_large.mod.py | 19 weights = Parameter("op2", "TENSOR_QUANT8_ASYMM", "{1, 5}, 0.2, 0", [10, 20, 20, 20, 10]) # num_units = 1, input_size = 5 variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
|
| /frameworks/ml/nn/runtime/test/specs/V1_1/ |
| fully_connected_float_large_relaxed.mod.py | 19 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 5}", [2, 3, 4, 5, 6]) # num_units = 1, input_size = 5 variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
|
| fully_connected_float_relaxed.mod.py | 19 weights = Parameter("op2", "TENSOR_FLOAT32", "{1, 1}", [2]) variable
23 model = model.Operation("FULLY_CONNECTED", in0, weights, bias, act).To(out0)
|
| /external/libxcam/modules/ocl/ |
| cv_image_process_helper.cpp | 96 CVImageProcessHelper::normalize_weights (cv::Mat &weights)
98 weights.convertTo (weights, CV_32FC1);
99 float sum = cv::sum (weights)[0];
100 weights /= sum;
|
| /external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
| clip_weights_impl.py | 15 """Utilities to clip weights.
36 """Modifies an optimizer so it clips weights to a certain value.
41 weight_clip: Positive python float to clip discriminator weights. Used to
55 """Modifies an optimizer so it clips weights to a certain value.
60 weight_clip: Positive python float to clip discriminator weights. Used to
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/ops/ |
| losses_ops.py | 32 def mean_squared_error_regressor(tensor_in, labels, weights, biases, name=None):
36 predictions = nn.xw_plus_b(tensor_in, weights, biases)
46 weights,
61 weights: Tensor, [batch_size, feature_size], linear transformation
72 logits = nn.xw_plus_b(tensor_in, weights, biases)
|
| /external/tensorflow/tensorflow/contrib/metrics/python/metrics/ |
| classification.py | 29 def accuracy(predictions, labels, weights=None, name=None):
37 weights: None or `Tensor` of float values to reweight the accuracy.
59 if weights is not None:
60 is_correct = math_ops.multiply(is_correct, weights)
61 num_values = math_ops.multiply(weights, array_ops.ones_like(is_correct))
|
| /external/tensorflow/tensorflow/core/graph/ |
| tensor_id_test.cc | 30 EXPECT_EQ(ParseHelper("weights:0"), "weights:0");
67 name = "weights";
70 name = "weights:17";
73 name = "^weights";
|
| /external/tensorflow/tensorflow/examples/android/jni/object_tracking/ |
| frame_pair.h | 50 Point2f GetWeightedMedian(const float* const weights,
53 float GetWeightedMedianScale(const float* const weights,
56 // Weights points based on the query_point and cutoff_dist.
58 float* const weights) const;
71 float* const weights,
|
| /frameworks/ml/nn/runtime/test/ |
| TestMemory.cpp | 36 // Tests the various ways to pass weights and input/output data.
56 WrapperMemory weights(offsetForMatrix3 + sizeof(matrix3), PROT_READ, fd, 0);
57 ASSERT_TRUE(weights.isValid());
70 model.setOperandValueFromMemory(e, &weights, offsetForMatrix2, sizeof(Matrix3x4));
71 model.setOperandValueFromMemory(a, &weights, offsetForMatrix3, sizeof(Matrix3x4));
|
| /external/tensorflow/tensorflow/contrib/estimator/python/estimator/ |
| head.py | 60 If `weight_column` is specified, weights must be of shape
78 weights. It is used to down weight or boost examples during training. It
126 If `weight_column` is specified, weights must be of shape
142 weights. It is used to down weight or boost examples during training. It
194 If `weight_column` is specified, weights must be of shape
205 weights. It is used to down weight or boost examples during training. It
258 If `weight_column` is specified, weights must be of shape
272 weights. It is used to down weight or boost examples during training. It
410 weights = head_lib._get_weights_and_check_match_logits( # pylint:disable=protected-access,
413 unweighted_loss, weights=weights, reduction=self._loss_reduction
[all...] |
