Searched defs:layers (Results 1 - 25 of 154) sorted by relevance

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/external/libopus/src/
H A Dmlp.h34 int layers; member in struct:__anon12273
H A Dmlp_train.h78 int layers; member in struct:__anon12274
/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/
H A D__init__.py20 from tensorflow.contrib.tensor_forest.hybrid.python import layers namespace
/external/deqp/external/vulkancts/modules/vulkan/image/
H A DvktImageTexture.cpp84 Texture::Texture (const ImageType imageType, const tcu::IVec3& imageLayerSize, const int layers, const int samples) argument
87 , m_numLayers (layers)
/external/drm_hwcomposer/
H A Ddrmcomposition.h38 std::vector<DrmHwcLayer> layers; member in struct:android::DrmCompositionDisplayLayersMap
H A Dplatform.cpp40 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
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/external/mesa3d/src/gallium/drivers/swr/
H A Dswr_clear.cpp38 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.
/external/tensorflow/tensorflow/contrib/bayesflow/
H A D__init__.py28 from tensorflow.contrib.bayesflow.python.ops import layers namespace
45 'layers',
/external/tensorflow/tensorflow/contrib/gan/python/features/python/
H A Dconditioning_utils_impl.py26 from tensorflow.contrib.layers.python.layers import layers namespace
68 mapped_conditioning = layers.linear(
69 layers.flatten(conditioning), num_features)
/external/tensorflow/tensorflow/contrib/model_pruning/python/layers/
H A Dlayers_test.py15 """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
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/external/tensorflow/tensorflow/contrib/slim/python/slim/nets/
H A Dalexnet.py26 "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
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H A Doverfeat.py35 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
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H A Dinception_v1.py21 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
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H A Dinception_v2.py21 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
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H A Dinception_v3.py21 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
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H A Dresnet_utils.py43 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
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H A Dresnet_v1.py61 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
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H A Dresnet_v2.py55 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
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H A Dvgg.py45 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
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/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/layers/
H A Dfully_connected.py20 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(
/external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/models/
H A Dhard_decisions_to_data_then_nn.py20 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(
/external/tensorflow/tensorflow/contrib/keras/api/keras/
H A D__init__.py32 from tensorflow.contrib.keras.api.keras import layers namespace
/external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/
H A Dsvm.py21 from tensorflow.contrib import layers namespace
209 return layers.parse_feature_columns_from_examples(
/external/tensorflow/tensorflow/contrib/quantize/python/
H A Dgraph_matcher_test.py22 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,
/external/skia/src/gpu/vk/
H A DGrVkExtensions.cpp56 // 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
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