/external/swiftshader/third_party/llvm-7.0/llvm/include/llvm/ADT/ |
D | SparseMultiSet.h | 123 DenseT Dense; variable 147 return Dense[D.Prev].isTail(); in isHead() 155 return &Dense[N.Prev] == &N; in isSingleton() 162 Dense.push_back(SMSNode(V, Prev, Next)); in addValue() 163 return Dense.size() - 1; in addValue() 168 unsigned NextFree = Dense[Idx].Next; in addValue() 169 assert(Dense[Idx].isTombstone() && "Non-tombstone free?"); in addValue() 171 Dense[Idx] = SMSNode(V, Prev, Next); in addValue() 179 Dense[Idx].Prev = SMSNode::INVALID; in makeTombstone() 180 Dense[Idx].Next = FreelistIdx; in makeTombstone() [all …]
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D | SparseSet.h | 132 DenseT Dense; 175 const_iterator begin() const { return Dense.begin(); } 176 const_iterator end() const { return Dense.end(); } 177 iterator begin() { return Dense.begin(); } 178 iterator end() { return Dense.end(); } 184 bool empty() const { return Dense.empty(); } 191 size_type size() const { return Dense.size(); } 197 Dense.clear(); 209 const unsigned FoundIdx = ValIndexOf(Dense[i]); 256 Dense.push_back(Val); [all …]
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/external/llvm/include/llvm/ADT/ |
D | SparseMultiSet.h | 115 DenseT Dense; variable 144 return Dense[D.Prev].isTail(); in isHead() 152 return &Dense[N.Prev] == &N; in isSingleton() 159 Dense.push_back(SMSNode(V, Prev, Next)); in addValue() 160 return Dense.size() - 1; in addValue() 165 unsigned NextFree = Dense[Idx].Next; in addValue() 166 assert(Dense[Idx].isTombstone() && "Non-tombstone free?"); in addValue() 168 Dense[Idx] = SMSNode(V, Prev, Next); in addValue() 176 Dense[Idx].Prev = SMSNode::INVALID; in makeTombstone() 177 Dense[Idx].Next = FreelistIdx; in makeTombstone() [all …]
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D | SparseSet.h | 128 DenseT Dense; 174 const_iterator begin() const { return Dense.begin(); } 175 const_iterator end() const { return Dense.end(); } 176 iterator begin() { return Dense.begin(); } 177 iterator end() { return Dense.end(); } 183 bool empty() const { return Dense.empty(); } 190 size_type size() const { return Dense.size(); } 196 Dense.clear(); 208 const unsigned FoundIdx = ValIndexOf(Dense[i]); 255 Dense.push_back(Val); [all …]
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/external/tensorflow/tensorflow/python/keras/layers/ |
D | tensorflow_op_layer_test.py | 39 x = keras.layers.Dense(10)(inputs) 46 x = keras.layers.Dense(10)(inputs) 54 x = keras.layers.Dense(10)(inputs) 62 x = keras.layers.Dense(10)(inputs) 64 outputs = keras.layers.Dense(10)(x) 70 x = keras.layers.Dense(10)(inputs) 73 outputs = keras.layers.Dense(10)(x) 79 x = keras.layers.Dense(10)(inputs) 87 outputs = keras.layers.Dense(10)(x) 94 x1 = keras.layers.Dense(10)(x) [all …]
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/external/tensorflow/tensorflow/python/keras/saving/ |
D | hdf5_format_test.py | 57 x = keras.layers.Dense(3)(a) 58 b = keras.layers.Dense(1)(x) 112 (keras.layers.TimeDistributed(keras.layers.Dense(1))), 182 model = keras.models.Sequential([keras.layers.Dense(2, input_dim=2)]) 187 y = keras.layers.Dense(2)(x) 243 model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) 244 model.add(keras.layers.Dense(num_classes)) 252 model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) 253 model.add(keras.layers.Dense(num_classes)) 273 ref_model.add(keras.layers.Dense(num_hidden, input_dim=input_dim, [all …]
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D | saved_model_test.py | 57 model.add(keras.layers.Dense(2, input_shape=(3,))) 59 model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) 82 model.add(keras.layers.Dense(2, input_shape=(3,))) 84 model.add(keras.layers.TimeDistributed(keras.layers.Dense(3))) 99 x = keras.layers.Dense(2)(inputs) 100 output = keras.layers.Dense(3)(x) 124 x = keras.layers.Dense(2)(inputs) 125 output = keras.layers.Dense(3)(x) 144 model.add(keras.layers.Dense(2, input_shape=(3,))) 145 model.add(keras.layers.Dense(3)) [all …]
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/external/eigen/Eigen/src/Core/util/ |
D | XprHelper.h | 277 template<typename T> struct plain_matrix_type<T,Dense> 314 template<typename T> struct eval<T,Dense> 334 struct eval<Matrix<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense> 340 struct eval<Array<_Scalar, _Rows, _Cols, _Options, _MaxRows, _MaxCols>, Dense> 350 struct plain_object_eval<T,Dense> 484 struct generic_xpr_base<Derived, XprKind, Dense> 529 … struct cwise_promote_storage_type<Dense,Dense,Functor> { ty… 530 … struct cwise_promote_storage_type<A,Dense,Functor> { typ… 531 … struct cwise_promote_storage_type<Dense,B,Functor> { ty… 532 …r> struct cwise_promote_storage_type<Sparse,Dense,Functor> … [all …]
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/external/tensorflow/tensorflow/python/keras/utils/ |
D | multi_gpu_utils_test.py | 53 model.add(keras.layers.Dense(hidden_dim, 55 model.add(keras.layers.Dense(output_dim)) 84 a = keras.layers.Dense(hidden_dim)(input_a) 85 b = keras.layers.Dense(hidden_dim)(input_b) 87 output_a = keras.layers.Dense(output_dim_a)(c) 88 output_b = keras.layers.Dense(output_dim_b)(c) 111 model.add(keras.layers.Dense(10, 114 model.add(keras.layers.Dense(1, activation='sigmoid')) 167 model.add(keras.layers.Dense(3, 169 model.add(keras.layers.Dense(num_classes)) [all …]
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/external/eigen/unsupported/Eigen/CXX11/src/Tensor/ |
D | TensorTraits.h | 51 typedef Dense StorageKind; 69 typedef Dense StorageKind; 122 struct eval<Tensor<_Scalar, NumIndices_, Options, IndexType_>, Eigen::Dense> 128 struct eval<const Tensor<_Scalar, NumIndices_, Options, IndexType_>, Eigen::Dense> 134 struct eval<TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense> 140 struct eval<const TensorFixedSize<Scalar_, Dimensions, Options, IndexType_>, Eigen::Dense> 146 struct eval<TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense> 152 struct eval<const TensorMap<PlainObjectType, Options, MakePointer>, Eigen::Dense> 158 struct eval<TensorRef<PlainObjectType>, Eigen::Dense> 164 struct eval<const TensorRef<PlainObjectType>, Eigen::Dense>
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/external/tensorflow/tensorflow/python/keras/engine/ |
D | topology_test.py | 190 test_layer = keras.layers.Dense(16, name='test_layer') 198 dense = keras.layers.Dense(16, name='dense_1') 215 new_dense = keras.layers.Dense(16) 218 new_dense = keras.layers.Dense(16) 221 new_dense = keras.layers.Dense(16) 224 new_dense = keras.layers.Dense(16) 227 new_dense = keras.layers.Dense(16) 234 new_dense = keras.layers.Dense(16) 281 dense = keras.layers.Dense(2) 313 b = keras.layers.Dense(1)(a) [all …]
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D | training_test.py | 61 output_a = keras.layers.Dense(1, name='dense_1')(input_a) 62 output_b = keras.layers.Dense(1, name='dense_2')(input_a) 188 dense = keras.layers.Dense(4, name='dense') 373 y = keras.layers.Dense(4)(x) 399 dense = keras.layers.Dense(4, name='dense') 482 keras.layers.Dense( 485 keras.layers.Dense( 506 layer = keras.layers.Dense( 523 x = keras.layers.Dense( 526 outputs = keras.layers.Dense(1, activation='sigmoid')(x) [all …]
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D | sequential_test.py | 42 model.add(keras.layers.Dense(1, input_dim=2)) 44 model.add(keras.layers.Dense(2, kernel_regularizer='l2', 54 model.add(keras.layers.Dense(1)) 56 model.add(keras.layers.Dense(2, kernel_regularizer='l2', 90 model.add(keras.layers.Dense(num_hidden, input_dim=input_dim)) 220 model.add(keras.layers.Dense(1, input_dim=1)) 230 inner_model.add(keras.layers.Dense(num_units, input_shape=(input_dim,))) 234 model.add(keras.layers.Dense(num_classes)) 352 model = keras.models.Sequential([keras.layers.Dense(3)]) 353 model.add(keras.layers.Dense(2)) [all …]
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D | feature_columns_integration_test.py | 37 self._dense_layer = keras.layers.Dense(units, name='dense_layer') 55 keras.layers.Dense(64, activation='relu'), 56 keras.layers.Dense(20, activation='softmax') 77 keras.layers.Dense(64, activation='relu'), 78 keras.layers.Dense(20, activation='softmax') 149 dense = keras.layers.Dense(4) 178 dense = keras.layers.Dense(4)
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/external/tensorflow/tensorflow/python/layers/ |
D | core_test.py | 46 dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense') 55 dense = core_layers.Dense(2, activation=nn_ops.relu) 58 dense = core_layers.Dense(2, activation=nn_ops.relu) 67 x = core_layers.Dense(1)(v) 73 dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense') 92 core_layers.Dense(5)(inputs) 93 core_layers.Dense(2, activation=nn_ops.relu, name='my_dense')(inputs) 97 dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense') 104 dense = core_layers.Dense(2, use_bias=False, name='my_dense') 118 dense = core_layers.Dense(2, trainable=False, name='my_dense') [all …]
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D | core.py | 33 class Dense(keras_layers.Dense, base.Layer): class 98 super(Dense, self).__init__(units=units, 174 layer = Dense(units, 338 FullyConnected = Dense
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/external/tensorflow/tensorflow/contrib/distribute/python/ |
D | keras_dnn_correctness_test.py | 49 model.add(keras.layers.Dense(10, activation='relu', input_shape=(1,))) 50 model.add(keras.layers.Dense( 53 model.add(keras.layers.Dense(10, activation='relu')) 54 model.add(keras.layers.Dense(1)) 92 model.add(keras.layers.Dense(1, 131 keras.layers.Dense( 134 keras.layers.Dense(
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/external/tensorflow/tensorflow/contrib/eager/python/ |
D | network_test.py | 42 self.l1 = self.track_layer(core.Dense(1, use_bias=False)) 52 self.l1 = self.track_layer(core.Dense( 56 self.l2 = self.track_layer(core.Dense( 172 layer_one = core.Dense(1, use_bias=False) 174 layer_two = core.Dense(1, use_bias=False) 181 self.first = self.track_layer(core.Dense( 183 self.second = self.track_layer(core.Dense( 203 self.first = self.track_layer(core.Dense( 216 self.second = self.track_layer(core.Dense( 226 self.first = self.track_layer(core.Dense( [all …]
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/external/tensorflow/tensorflow/python/keras/ |
D | model_subclassing_test.py | 55 self.dense1 = keras.layers.Dense(32, activation='relu') 56 self.dense2 = keras.layers.Dense(num_classes, activation='softmax') 79 self.dense1 = keras.layers.Dense(num_classes, activation='softmax') 95 self.dense1 = keras.layers.Dense(32, activation='relu') 96 self.dense2 = keras.layers.Dense(num_classes[0], activation='softmax') 97 self.dense3 = keras.layers.Dense(num_classes[1], activation='softmax') 121 self.dense1 = keras.layers.Dense(32, activation='relu') 122 self.dense2 = keras.layers.Dense(num_classes, activation='relu') 138 x = keras.layers.Dense(32, activation='relu')(inputs) 140 outputs = keras.layers.Dense(num_classes)(x) [all …]
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D | models_test.py | 50 self.layer1 = keras.layers.Dense(n_outputs) 61 keras.layers.Dense(4)] 63 model_layers = [keras.layers.Dense(4, input_shape=input_shape)] 65 model_layers = [keras.layers.Dense(4)] 70 keras.layers.Dense(4)] 164 dense_1 = keras.layers.Dense(4,) 165 dense_2 = keras.layers.Dense(4,) 223 keras.layers.Dense(1, kernel_initializer='one'))(outputs) 236 seq_model.add(keras.layers.Dense(4, input_shape=(4,))) 239 y = keras.layers.Dense(4)(x) [all …]
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D | integration_test.py | 48 [keras.layers.Dense(16, activation='relu'), 50 keras.layers.Dense(y_train.shape[-1], activation='softmax')], 78 [keras.layers.Dense(16, 86 y = keras.layers.Dense(y_train.shape[-1], activation='softmax')(y) 189 keras.layers.Dense(y_train.shape[-1], activation='softmax') 228 keras.layers.Dense(10, activation=nn.relu), 230 keras.layers.Dense(y_train.shape[-1], activation=nn.softmax_v2),
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/external/tensorflow/tensorflow/python/util/ |
D | serialization_test.py | 37 dense = core.Dense(3) 53 model.add(core.Dense(4)) 54 model.add(core.Dense(5)) 64 y = core.Dense(10)(x)
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/external/tensorflow/tensorflow/python/keras/wrappers/ |
D | scikit_learn_test.py | 38 model.add(keras.layers.Dense(INPUT_DIM, input_shape=(INPUT_DIM,))) 40 model.add(keras.layers.Dense(hidden_dim)) 42 model.add(keras.layers.Dense(NUM_CLASSES)) 74 model.add(keras.layers.Dense(INPUT_DIM, input_shape=(INPUT_DIM,))) 76 model.add(keras.layers.Dense(hidden_dim)) 78 model.add(keras.layers.Dense(1))
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/external/tensorflow/tensorflow/python/training/tracking/ |
D | data_structures_test.py | 46 self.layer_list = data_structures.List([core.Dense(3)]) 47 self.layer_list.append(core.Dense(4)) 49 [core.Dense(5), 50 core.Dense(6, kernel_regularizer=math_ops.reduce_sum)]) 52 core.Dense(7, bias_regularizer=math_ops.reduce_sum), 53 core.Dense(8) 56 data_structures.List([core.Dense(9)]) + data_structures.List( 57 [core.Dense(10)])) 60 list([core.Dense(11)]) + [core.Dense(12)])) 184 inner.append(non_keras_core.Dense(1)) [all …]
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/external/tensorflow/tensorflow/examples/saved_model/integration_tests/ |
D | use_model_in_sequential_keras.py | 46 model.add(l.Dense(100, activation="relu")) 47 model.add(l.Dense(50, activation="relu")) 48 model.add(l.Dense(1, activation="sigmoid"))
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