| /external/u-boot/arch/arm/dts/ |
| at91sam9x25.dtsi | 16 model = "Atmel AT91SAM9X25 SoC";
|
| at91sam9x35.dtsi | 15 model = "Atmel AT91SAM9X35 SoC";
|
| at91sam9x35ek.dts | 15 model = "Atmel AT91SAM9X35-EK";
|
| sun8i-a23-inet86dz.dts | 48 model = "INet-86DZ Rev 01";
|
| sun8i-a83t-allwinner-h8homlet-v2.dts | 48 model = "Allwinner A83T H8Homlet Proto Dev Board v2.0";
|
| sun8i-a83t-bananapi-m3.dts | 48 model = "Allwinner A83T BananaPi M3 Board v1.2";
|
| sun8i-a83t-cubietruck-plus.dts | 49 model = "Cubietech Cubietruck Plus";
|
| sun8i-a83t-tbs-a711.dts | 48 model = "TBS A711 Tablet";
|
| sun8i-r40-bananapi-m2-ultra.dts | 47 model = "Banana Pi BPI-M2-Ultra";
|
| /external/u-boot/tools/ |
| microcode-tool.py | 17 """Holds information about the microcode for a particular model of CPU.
22 model: Model code string (this is cpuid(1).eax, e.g. '206a7')
35 # The model is in the 4rd hex word
36 self.model = '%x' % self.words[3]
121 def List(date, microcodes, model):
127 model: Model string to search for, or None
130 if model:
131 mcode_list, tried = FindMicrocode(microcodes, model.lower()
[all...] |
| /external/tensorflow/tensorflow/lite/toco/ |
| export_tensorflow.cc | 32 #include "tensorflow/lite/toco/model.h"
81 tensorflow::DataType GetTensorFlowDataType(const Model& model,
83 return GetTensorFlowDataType(model.GetArray(array_name).data_type,
178 void ConvertFloatTensorConst(const Model& model, const string& name,
185 CHECK(model.HasArray(name));
186 const auto& input_array = model.GetArray(name);
196 void ConvertFloatTensorConst(const Model& model, const string& name
[all...] |
| /external/tensorflow/tensorflow/python/training/tracking/ |
| data_structures_test.py | 42 class HasList(training.Model):
78 model = HasList()
79 output = model(array_ops.ones([32, 2]))
81 self.assertEqual(11, len(model.layers))
82 self.assertEqual(10, len(model.layer_list.layers))
85 model.layers,
86 model.layer_list.layers + model.layers_with_updates)
88 self.assertEqual(3 + index, model.layer_list.layers[index].units)
89 self.assertEqual(2, len(model._checkpoint_dependencies)
[all...] |
| /external/tensorflow/tensorflow/contrib/distribute/python/ |
| keras_test.py | 57 model = keras.models.Sequential()
58 model.add(keras.layers.Dense(16, activation='relu', input_shape=_INPUT_SIZE))
59 model.add(keras.layers.Dropout(0.1))
60 model.add(keras.layers.Dense(_NUM_CLASS, activation='softmax'))
61 return model
69 model = keras.models.Model(inputs=[a], outputs=[b])
70 return model
75 class _SimpleMLP(keras.Model):
94 model = keras.models.Model
[all...] |
| /external/androidplot/AndroidPlot-Core/src/test/java/com/androidplot/ui/ |
| DynamicTableModelTest.java | 40 TableModel model = new DynamicTableModel(5, 5, TableOrder.COLUMN_MAJOR);
local
48 DynamicTableModel model = new DynamicTableModel(5, 5);
local
50 RectF cellRect = model.getCellRect(tableRect, 10);
54 model = new DynamicTableModel(5, 0);
55 cellRect = model.getCellRect(tableRect, 10);
59 model = new DynamicTableModel(0, 5);
60 cellRect = model.getCellRect(tableRect, 10);
65 TableModel model = new DynamicTableModel(2, 2);
local
70 Iterator<RectF> it = model.getIterator(tableRect, 10);
80 model = new DynamicTableModel(2, 0);
96 TableModel model = new DynamicTableModel(2, 2); local
154 TableModel model = new DynamicTableModel(2, 2, TableOrder.COLUMN_MAJOR); local
201 TableModel model = new DynamicTableModel(0, 1); local
[all...] |
| /external/deqp-deps/SPIRV-Tools/source/val/ |
| validate_mode_setting.cpp | 66 << "Fragment execution model entry points can only specify "
74 << "Fragment execution model entry points require either an "
90 << "Fragment execution model entry points can specify at most "
110 << "Tessellation execution model entry points can specify at "
127 << "Tessellation execution model entry points can specify at "
142 << "Tessellation execution model entry points can specify at "
163 << "Geometry execution model entry points must specify "
181 << "Geometry execution model entry points must specify "
210 << "In the Vulkan environment, GLCompute execution model "
249 [](const SpvExecutionModel& model) {
[all...] |
| /external/swiftshader/third_party/SPIRV-Tools/source/val/ |
| validate_mode_setting.cpp | 66 << "Fragment execution model entry points can only specify "
74 << "Fragment execution model entry points require either an "
90 << "Fragment execution model entry points can specify at most "
110 << "Tessellation execution model entry points can specify at "
127 << "Tessellation execution model entry points can specify at "
142 << "Tessellation execution model entry points can specify at "
163 << "Geometry execution model entry points must specify "
181 << "Geometry execution model entry points must specify "
210 << "In the Vulkan environment, GLCompute execution model "
249 [](const SpvExecutionModel& model) {
[all...] |
| /external/tensorflow/tensorflow/lite/toco/tflite/ |
| import.cc | 18 #include "tensorflow/lite/model.h"
30 void LoadTensorsTable(const ::tflite::Model& input_model,
40 void LoadOperatorsTable(const ::tflite::Model& input_model,
55 void ImportTensors(const ::tflite::Model& input_model, Model* model) {
61 Array& array = model->GetOrCreateArray(input_tensor->name()->c_str());
101 const ::tflite::Model& input_model,
104 const details::OperatorsTable& operators_table, Model* model) {
217 std::unique_ptr<Model> model; local
[all...] |
| /external/tensorflow/tensorflow/python/keras/layers/ |
| convolutional_recurrent_test.py | 70 model = keras.models.Model(x, states[0])
71 state = model.predict(inputs)
101 model = keras.models.Sequential()
111 model.add(layer)
112 model.compile(optimizer='sgd', loss='mse')
113 out1 = model.predict(np.ones_like(inputs))
116 model.train_on_batch(np.ones_like(inputs),
118 out2 = model.predict(np.ones_like(inputs))
124 # (even though the model itself didn't change
[all...] |
| gru_test.py | 52 model = keras.models.Sequential()
53 model.add(layer)
54 model.compile(
58 model.train_on_batch(x, y)
101 gru_model = keras.models.Model(inputs, output)
112 model = keras.models.Sequential()
113 model.add(keras.layers.Masking(input_shape=(3, 4)))
114 model.add(layer_class(units=5, return_sequences=True, unroll=False))
115 model.compile(
119 model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1
[all...] |
| simplernn_test.py | 51 model = keras.models.Sequential()
52 model.add(layer)
53 model.compile('rmsprop', 'mse')
56 model.train_on_batch(x, y)
106 model = keras.models.Sequential()
107 model.add(keras.layers.Masking(input_shape=(3, 4)))
108 model.add(layer_class(units=5, return_sequences=True, unroll=False))
109 model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
110 model.fit(inputs, targets, epochs=1, batch_size=2, verbose=1)
147 model = keras.models.Sequential(
[all...] |
| /external/tensorflow/tensorflow/python/keras/saving/ |
| saved_model_test.py | 16 """Tests for saving/loading function for keras Model."""
56 model = keras.models.Sequential()
57 model.add(keras.layers.Dense(2, input_shape=(3,)))
58 model.add(keras.layers.RepeatVector(3))
59 model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
60 model.compile(
67 model.train_on_batch(x, y)
69 ref_y = model.predict(x)
72 keras_saved_model.export_saved_model(model, saved_model_dir)
81 model = keras.models.Sequential(
[all...] |
| /external/glide/library/src/main/java/com/bumptech/glide/ |
| RequestManager.java | 11 import com.bumptech.glide.load.model.ModelLoader;
12 import com.bumptech.glide.load.model.file_descriptor.FileDescriptorModelLoader;
13 import com.bumptech.glide.load.model.stream.MediaStoreStreamLoader;
14 import com.bumptech.glide.load.model.stream.StreamByteArrayLoader;
15 import com.bumptech.glide.load.model.stream.StreamModelLoader;
90 * @param <T> The type of the model.
174 * Returns a request builder that uses the given {@link com.bumptech.glide.load.model.ModelLoader} to fetch a
181 * @param modelLoader The {@link ModelLoader} class to use to load the model.
183 * @param <A> The type of the model to be loaded.
191 * Returns a request builder that uses the given {@link com.bumptech.glide.load.model.stream.StreamModelLoader} t
693 private final A model; field in class:RequestManager.GenericModelRequest.GenericTypeRequest
[all...] |
| /external/tensorflow/tensorflow/contrib/eager/python/examples/revnet/ |
| revnet_test.py | 32 def train_one_iter(model, inputs, labels, optimizer, global_step=None):
34 logits, saved_hidden = model(inputs)
35 grads, loss = model.compute_gradients(
38 zip(grads, model.trainable_variables), global_step=global_step)
57 self.model = revnet.RevNet(config=config)
67 del self.model
76 y, _ = self.model(self.x, training=False)
104 _, saved_hidden = self.model(self.x) # Initialize model
105 grads, loss = self.model.compute_gradients
[all...] |
| /external/tensorflow/tensorflow/lite/ |
| model_test.cc | 23 #include "tensorflow/lite/model.h"
75 // Make sure a model with nothing in it loads properly.
77 auto model = FlatBufferModel::BuildFromFile( local
79 ASSERT_TRUE(model);
80 // Now try to build it into a model.
82 ASSERT_EQ(InterpreterBuilder(*model, TrivialResolver())(&interpreter),
85 ASSERT_NE(InterpreterBuilder(*model, TrivialResolver())(nullptr), kTfLiteOk);
109 auto model = FlatBufferModel::BuildFromFile( local
111 ASSERT_TRUE(model);
114 ASSERT_NE(InterpreterBuilder(*model, TrivialResolver(nullptr))(&interpreter)
121 auto model = FlatBufferModel::BuildFromFile( local
201 auto model = FlatBufferModel::BuildFromFile( local
271 auto model = FlatBufferModel::BuildFromFile( local
286 auto model = FlatBufferModel::BuildFromFile( local
308 auto model = FlatBufferModel::BuildFromModel(model_fb); local
[all...] |
| /external/tensorflow/tensorflow/python/keras/engine/ |
| training_eager.py | 43 def _eager_metrics_fn(model, outputs, targets, sample_weights=None, masks=None):
44 """Calculates the metrics for each output of the given model.
47 model: The model on which metrics are being calculated.
48 outputs: The outputs of the given model.
49 targets: The predictions or targets of the given model.
54 Returns the metric results for each output of the model.
59 metric_results = model._handle_metrics(
64 def _model_loss(model,
70 """Calculates the loss for a given model
[all...] |
