| /external/libopus/silk/float/ |
| residual_energy_FLP.c | 47 silk_float tmp, nrg = 0.0f, regularization; local
52 regularization = REGULARIZATION_FACTOR * ( wXX[ 0 ] + wXX[ D * D - 1 ] );
75 matrix_c_ptr( wXX, i, i, D ) += regularization;
78 regularization *= 2.0f;
|
| /external/tensorflow/tensorflow/python/keras/ |
| regularizers.py | 45 """Regularizer for L1 and L2 regularization.
48 l1: Float; L1 regularization factor.
49 l2: Float; L2 regularization factor.
59 regularization = 0.
61 regularization += math_ops.reduce_sum(self.l1 * math_ops.abs(x))
63 regularization += math_ops.reduce_sum(self.l2 * math_ops.square(x))
64 return regularization
|
| /external/tensorflow/tensorflow/core/kernels/ |
| sdca_internal.cc | 156 const Regularizations& regularization, const int num_weight_vectors) const {
160 squared_norm_ / regularization.symmetric_l2();
180 feature_value * regularization.Shrink(sparse_weight);
181 result.wx[l] += feature_value * regularization.Shrink(feature_weight);
200 regularization.EigenShrinkVector(
207 regularization.EigenShrinkVector(
217 regularization.EigenShrinkMatrix(dense_weights.nominals())
220 regularization.EigenShrinkMatrix(feature_weights)
236 const int num_loss_partitions, const Regularizations& regularization,
254 model_weights, regularization,
[all...] |
| sdca_internal.h | 86 // Proximal SDCA shrinking for L1 regularization.
135 const Regularizations& regularization,
329 const int num_loss_partitions, const Regularizations& regularization,
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/lib/learner/common/stats/ |
| split-stats.h | 37 // regularization as no new nodes are being added with this candidate.
44 // Feature binary split candidate, we apply tree complexity regularization
52 learner_config.regularization().tree_complexity()) {}
|
| node-stats.h | 52 : NodeStats(learner_config.regularization().l1(),
53 learner_config.regularization().l2(),
80 // Apply L1 regularization.
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/ |
| hybrid_layer_test.py | 41 regularization="",
|
| hybrid_model.py | 58 if params.regularization == "l1":
61 elif params.regularization == "l2":
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/training/functions/ |
| gbdt_batch_test.py | 222 learner_config.regularization.l1 = 0
223 learner_config.regularization.l2 = 0
325 learner_config.regularization.l1 = 0
326 learner_config.regularization.l2 = 0
482 learner_config.regularization.l1 = 0
483 learner_config.regularization.l2 = 0
589 learner_config.regularization.l1 = 0
590 learner_config.regularization.l2 = 0
694 learner_config.regularization.l1 = 0
695 learner_config.regularization.l2 =
[all...] |
| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| factorization_ops_test.py | 176 regularization=0.01,
199 (_, process_input_op, unregularized_loss, regularization,
202 factor_loss = unregularized_loss + regularization
271 (_, process_input_op, unregularized_loss, regularization,
274 factor_loss = unregularized_loss + regularization
345 regularization=0.01,
374 (_, process_input_op, unregularized_loss, regularization,
377 factor_loss = unregularized_loss + regularization
450 (_, process_input_op, unregularized_loss, regularization,
453 factor_loss = unregularized_loss + regularization
[all...] |
| factorization_ops_test_utils.py | 98 def calculate_loss(input_mat, row_factors, col_factors, regularization=None,
110 regularization: the regularization coefficient, a scalar.
122 reg = (regularization if regularization is not None
|
| factorization_ops.py | 62 \\(\lambda)\\: regularization.
83 _, _, unregularized_loss, regularization, sum_weights =
87 regularization is
192 regularization=None,
209 regularization: weight of L2 regularization term. If None, no
210 regularization is done.
252 self._regularization = regularization
254 regularization * linalg_ops.eye(self._n_components)
255 if regularization is not None else None
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/examples/ |
| binary_mnist.py | 80 learner_config.regularization.l1 = 0.0
81 learner_config.regularization.l2 = FLAGS.l2 / FLAGS.examples_per_layer
149 "--l2", type=float, default=1.0, help="l2 regularization per batch.")
|
| boston.py | 19 and are using l2 loss and l2 regularization.
58 learner_config.regularization.l1 = 0.0
59 learner_config.regularization.l2 = FLAGS.l2
156 "--l2", type=float, default=1.0, help="l2 regularization per batch.")
|
| boston_combined.py | 56 learner_config.regularization.l1 = 0.0
57 learner_config.regularization.l2 = FLAGS.tree_l2
150 "--tree_l2", type=float, default=1.0, help="l2 regularization per batch.")
|
| mnist.py | 78 learner_config.regularization.l1 = 0.0
79 learner_config.regularization.l2 = FLAGS.l2 / FLAGS.examples_per_layer
151 "--l2", type=float, default=1.0, help="l2 regularization per batch.")
|
| /external/tensorflow/tensorflow/contrib/image/python/kernel_tests/ |
| sparse_image_warp_test.py | 77 for regularization in (0, 0.01):
79 self.assertZeroShift(order, regularization, num_boundary_points)
81 def assertZeroShift(self, order, regularization, num_boundary_points):
107 regularization_weight=regularization,
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/layers/ |
| decisions_to_data_test.py | 45 regularization="",
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/kernel_tests/ |
| k_feature_routing_function_op_test.py | 46 regularization="",
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/models/ |
| forest_to_data_then_nn_test.py | 46 regularization="",
|
| k_feature_decisions_to_data_then_nn_test.py | 46 regularization="",
|
| decisions_to_data_then_nn_test.py | 47 regularization="",
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/estimator_batch/ |
| estimator_test.py | 405 learner_config.regularization.l2 = 1.0 / _QUANTILE_REGRESSION_SIZE
406 learner_config.regularization.l1 = 1.0 / _QUANTILE_REGRESSION_SIZE
407 learner_config.regularization.tree_complexity = (
438 learner_config.regularization.l2 = 1.0 / _QUANTILE_REGRESSION_SIZE
439 learner_config.regularization.l1 = 1.0 / _QUANTILE_REGRESSION_SIZE
440 learner_config.regularization.tree_complexity = (
675 learner_config.regularization.l2 = 1.0 / _QUANTILE_REGRESSION_SIZE
676 learner_config.regularization.l1 = 1.0 / _QUANTILE_REGRESSION_SIZE
677 learner_config.regularization.tree_complexity = (
|
| /external/tensorflow/tensorflow/contrib/eager/python/examples/densenet/ |
| densenet_graph_test.py | 134 regularization = tf.add_n(model.losses)
135 loss = cross_ent + regularization
|
| densenet_test.py | 145 regularization = tf.add_n(model.losses)
146 loss = cross_ent + regularization
|
