/external/libopus/src/ |
H A D | mlp.h | 34 int layers; member in struct:__anon12273
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H A D | mlp_train.h | 78 int layers; member in struct:__anon12274
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/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/ |
H A D | __init__.py | 20 from tensorflow.contrib.tensor_forest.hybrid.python import layers namespace
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/external/deqp/external/vulkancts/modules/vulkan/image/ |
H A D | vktImageTexture.cpp | 84 Texture::Texture (const ImageType imageType, const tcu::IVec3& imageLayerSize, const int layers, const int samples) argument 87 , m_numLayers (layers)
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/external/drm_hwcomposer/ |
H A D | drmcomposition.h | 38 std::vector<DrmHwcLayer> layers; member in struct:android::DrmCompositionDisplayLayersMap
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H A D | platform.cpp | 40 std::map<size_t, DrmHwcLayer *> &layers, bool use_squash_fb, DrmCrtc *crtc, 64 if (layers.size() > planes.size()) { 77 int ret = i->ProvisionPlanes(&composition, layers, crtc, &planes); 93 std::map<size_t, DrmHwcLayer *> &layers, DrmCrtc *crtc, 97 for (auto i = layers.begin(); i != layers.end();) { 109 i = layers.erase(i); 115 // Add any layers below the protected content to the precomposition since we 117 for (auto i = layers.begin(); i != layers 39 ProvisionPlanes( std::map<size_t, DrmHwcLayer *> &layers, bool use_squash_fb, DrmCrtc *crtc, std::vector<DrmPlane *> *primary_planes, std::vector<DrmPlane *> *overlay_planes) argument 91 ProvisionPlanes( std::vector<DrmCompositionPlane> *composition, std::map<size_t, DrmHwcLayer *> &layers, DrmCrtc *crtc, std::vector<DrmPlane *> *planes) argument 143 ProvisionPlanes( std::vector<DrmCompositionPlane> *composition, std::map<size_t, DrmHwcLayer *> &layers, DrmCrtc *crtc, std::vector<DrmPlane *> *planes) argument 171 ProvisionPlanes( std::vector<DrmCompositionPlane> *composition, std::map<size_t, DrmHwcLayer *> &layers, DrmCrtc *crtc, std::vector<DrmPlane *> *planes) argument [all...] |
/external/mesa3d/src/gallium/drivers/swr/ |
H A D | swr_clear.cpp | 38 unsigned layers = 0; local 49 layers = std::max(layers, fb->cbufs[i]->u.tex.last_layer - 56 layers = std::max(layers, fb->zsbuf->u.tex.last_layer - 62 layers = std::max(layers, fb->zsbuf->u.tex.last_layer - 71 for (unsigned i = 0; i < layers; ++i) { 77 // Mask out the attachments that are out of layers.
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/external/tensorflow/tensorflow/contrib/bayesflow/ |
H A D | __init__.py | 28 from tensorflow.contrib.bayesflow.python.ops import layers namespace 45 'layers',
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/external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
H A D | conditioning_utils_impl.py | 26 from tensorflow.contrib.layers.python.layers import layers namespace 68 mapped_conditioning = layers.linear( 69 layers.flatten(conditioning), num_features)
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/external/tensorflow/tensorflow/contrib/model_pruning/python/layers/ |
H A D | layers_test.py | 15 """Tests for imagingvision.intelligence.tensorflow.model_pruning.layers.""" 21 from tensorflow.contrib.model_pruning.python.layers import core_layers 22 from tensorflow.contrib.model_pruning.python.layers import layers namespace 37 layers.masked_conv2d(input_tensor, 32, 3) 42 layers.masked_conv2d(input_tensor, 32, 3) 48 layers.masked_conv2d(input_tensor, output_depth, kernel_size) 72 top_layer = layers.masked_conv2d(top_layer, base_depth + 97 layers.masked_fully_connected(input_tensor, output_depth) 120 top_layer = layers [all...] |
/external/tensorflow/tensorflow/contrib/slim/python/slim/nets/ |
H A D | alexnet.py | 26 "ImageNet Classification", as per the paper, the LRN layers have been removed. 39 from tensorflow.contrib import layers namespace 41 from tensorflow.contrib.layers.python.layers import layers as layers_lib 42 from tensorflow.contrib.layers.python.layers import regularizers 43 from tensorflow.contrib.layers.python.layers import utils 54 [layers [all...] |
H A D | overfeat.py | 35 from tensorflow.contrib import layers namespace 37 from tensorflow.contrib.layers.python.layers import layers as layers_lib 38 from tensorflow.contrib.layers.python.layers import regularizers 39 from tensorflow.contrib.layers.python.layers import utils 50 [layers.conv2d, layers_lib.fully_connected], 54 with arg_scope([layers [all...] |
H A D | inception_v1.py | 21 from tensorflow.contrib import layers namespace 23 from tensorflow.contrib.layers.python.layers import initializers 24 from tensorflow.contrib.layers.python.layers import layers as layers_lib 25 from tensorflow.contrib.layers.python.layers import regularizers 62 [layers.conv2d, layers_lib.fully_connected], 65 [layers [all...] |
H A D | inception_v2.py | 21 from tensorflow.contrib import layers namespace 23 from tensorflow.contrib.layers.python.layers import initializers 24 from tensorflow.contrib.layers.python.layers import layers as layers_lib 25 from tensorflow.contrib.layers.python.layers import regularizers 84 layers.conv2d, layers_lib.max_pool2d, layers_lib.avg_pool2d, 85 layers [all...] |
H A D | inception_v3.py | 21 from tensorflow.contrib import layers namespace 23 from tensorflow.contrib.layers.python.layers import initializers 24 from tensorflow.contrib.layers.python.layers import layers as layers_lib 25 from tensorflow.contrib.layers.python.layers import regularizers 46 Note that the names of the layers in the paper do not correspond to the names 107 [layers [all...] |
H A D | resnet_utils.py | 43 from tensorflow.contrib import layers as layers_lib 46 from tensorflow.contrib.layers.python.layers import initializers 47 from tensorflow.contrib.layers.python.layers import layers namespace 48 from tensorflow.contrib.layers.python.layers import regularizers 49 from tensorflow.contrib.layers.python.layers impor [all...] |
H A D | resnet_v1.py | 61 from tensorflow.contrib import layers namespace 64 from tensorflow.contrib.layers.python.layers import layers as layers_lib 65 from tensorflow.contrib.layers.python.layers import utils 94 depth_bottleneck: The depth of the bottleneck layers. 109 shortcut = layers.conv2d( 116 residual = layers.conv2d( 120 residual = layers [all...] |
H A D | resnet_v2.py | 55 from tensorflow.contrib import layers as layers_lib 58 from tensorflow.contrib.layers.python.layers import layers namespace 59 from tensorflow.contrib.layers.python.layers import utils 88 depth_bottleneck: The depth of the bottleneck layers. 100 preact = layers.batch_norm( 168 is_training: whether batch_norm layers are in training mode. 202 with arg_scope([layers [all...] |
H A D | vgg.py | 45 from tensorflow.contrib import layers namespace 47 from tensorflow.contrib.layers.python.layers import layers as layers_lib 48 from tensorflow.contrib.layers.python.layers import regularizers 49 from tensorflow.contrib.layers.python.layers import utils 66 [layers.conv2d, layers_lib.fully_connected], 70 with arg_scope([layers [all...] |
/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/layers/ |
H A D | fully_connected.py | 20 from tensorflow.contrib import layers namespace 37 nn_activations = layers.fully_connected(data, self.params.layer_size) 41 nn_activations = layers.fully_connected(nn_activations, 54 nn_activations = layers.fully_connected(data, 1) 70 nn_activations = [layers.fully_connected(data, self.params.layer_size)] 75 layers.fully_connected(
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/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/models/ |
H A D | hard_decisions_to_data_then_nn.py | 20 from tensorflow.contrib import layers namespace 22 from tensorflow.contrib.tensor_forest.hybrid.python.layers import decisions_to_data 23 from tensorflow.contrib.tensor_forest.hybrid.python.layers import fully_connected 43 self.layers = [decisions_to_data.HardDecisionsToDataLayer( 50 inference_result = self.layers[0].soft_inference_graph(data) 52 inference_result = self._do_layer_inference(self.layers[0], data) 54 for layer in self.layers[1:]: 58 output = layers.fully_connected(
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/external/tensorflow/tensorflow/contrib/keras/api/keras/ |
H A D | __init__.py | 32 from tensorflow.contrib.keras.api.keras import layers namespace
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/external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
H A D | svm.py | 21 from tensorflow.contrib import layers namespace 209 return layers.parse_feature_columns_from_examples(
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/external/tensorflow/tensorflow/contrib/quantize/python/ |
H A D | graph_matcher_test.py | 22 from tensorflow.contrib.layers.python.layers import initializers 23 from tensorflow.contrib.layers.python.layers import layers namespace 43 [layers.batch_norm], fused=True, is_training=True, trainable=True): 44 return layers.convolution( 51 normalizer_fn=layers.batch_norm,
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/external/skia/src/gpu/vk/ |
H A D | GrVkExtensions.cpp | 56 // instance layers 62 VkLayerProperties* layers = new VkLayerProperties[layerCount]; local 63 res = EnumerateInstanceLayerProperties(&layerCount, layers); 65 delete[] layers; 69 if (nonPatchVersion >= remove_patch_version(layers[i].specVersion)) { 70 fInstanceLayerStrings->push_back() = layers[i].layerName; 73 delete[] layers; 79 // via Vulkan implementation and implicitly enabled layers 101 // via explicitly enabled layers 149 // device layers 155 VkLayerProperties* layers = new VkLayerProperties[layerCount]; local [all...] |