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/external/tensorflow/tensorflow/python/keras/_impl/keras/
Dmodels_test.py44 model = keras.models.Sequential()
45 model.add(keras.layers.Dense(2, input_shape=(3,)))
46 model.add(keras.layers.RepeatVector(3))
47 model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
48 model.compile(loss=keras.losses.MSE,
54 model.train_on_batch(x, y)
56 out = model.predict(x)
58 keras.models.save_model(model, fname)
70 model.train_on_batch(x, y)
72 out = model.predict(x)
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Dmodel_subclassing_test.py178 model = SimpleTestModel(num_classes=num_classes,
181 model.compile(loss='mse',
188 model.fit(x, y, epochs=2, batch_size=32, verbose=0)
189 _ = model.evaluate(x, y, verbose=0)
198 model = MultiIOTestModel(num_classes=num_classes,
201 model.compile(loss='mse',
210 model.fit([x1, x2], [y1, y2], epochs=2, batch_size=32, verbose=0)
211 _ = model.evaluate([x1, x2], [y1, y2], verbose=0)
220 model = SimpleTestModel(num_classes=num_classes,
223 model.compile(loss='mse', optimizer=RMSPropOptimizer(learning_rate=0.001))
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/external/tensorflow/tensorflow/contrib/lite/toco/graph_transformations/
Didentify_lstm.cc28 Model* model, const Operator& op) { in FindOperator() argument
29 auto it = model->operators.begin(); in FindOperator()
30 for (; it != model->operators.end(); ++it) { in FindOperator()
38 bool GetStateArrayForBackEdge(const Model& model, in GetStateArrayForBackEdge() argument
41 for (const auto& rnn_state : model.flags.rnn_states()) { in GetStateArrayForBackEdge()
57 bool MatchOperatorInputs(const Operator& op, const Model& model, in MatchOperatorInputs() argument
65 Operator* x = GetOpWithOutput(model, op.inputs[0]); in MatchOperatorInputs()
90 bool MatchOperatorInputs(const Operator& op, const Model& model, in MatchOperatorInputs() argument
99 Operator* x = GetOpWithOutput(model, op.inputs[0]); in MatchOperatorInputs()
113 Operator* y = GetOpWithOutput(model, op.inputs[1]); in MatchOperatorInputs()
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Dpropagate_fixed_sizes.cc105 int GetOutputDepthFromWeights(const Model& model, const Operator& op) { in GetOutputDepthFromWeights() argument
107 const auto& weights_shape = model.GetArray(weights_name).shape(); in GetOutputDepthFromWeights()
118 bool EnsureBiasVectorShape(Model* model, Operator* op) { in EnsureBiasVectorShape() argument
120 const auto& weights_array = model->GetArray(weights_name); in EnsureBiasVectorShape()
129 auto& bias_array = model->GetArray(op->inputs[2]); in EnsureBiasVectorShape()
134 const int output_depth = GetOutputDepthFromWeights(*model, *op); in EnsureBiasVectorShape()
143 void ProcessConvOperator(Model* model, ConvOperator* op) { in ProcessConvOperator() argument
144 if (!EnsureBiasVectorShape(model, op)) { in ProcessConvOperator()
148 const auto& input_array = model->GetArray(op->inputs[0]); in ProcessConvOperator()
156 const auto& weights_array = model->GetArray(op->inputs[1]); in ProcessConvOperator()
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Dgraph_transformations.cc32 void PrintModelStats(const string& label, const Model& model) { in PrintModelStats() argument
34 for (const auto& array : model.GetArrayMap()) { in PrintModelStats()
39 LOG(INFO) << label << ": " << model.operators.size() << " operators, " in PrintModelStats()
40 << model.GetArrayMap().size() << " arrays (" << quantized_arrays in PrintModelStats()
57 void DiscardUselessConnectedComponentsAndRNNBackEdges(Model* model) { in DiscardUselessConnectedComponentsAndRNNBackEdges() argument
61 for (const string& output_array : model->flags.output_arrays()) { in DiscardUselessConnectedComponentsAndRNNBackEdges()
67 for (const auto& op : model->operators) { in DiscardUselessConnectedComponentsAndRNNBackEdges()
83 for (const auto& rnn_state : model->flags.rnn_states()) { in DiscardUselessConnectedComponentsAndRNNBackEdges()
94 model->EraseArrays([&](const string& name) { in DiscardUselessConnectedComponentsAndRNNBackEdges()
95 return (!useful_arrays.count(name) && IsDiscardableArray(*model, name)); in DiscardUselessConnectedComponentsAndRNNBackEdges()
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Didentify_lstm_merge_inputs.cc28 bool MergeLstmCellInputs::Run(Model* model, std::size_t op_index) { in Run() argument
30 auto op_it = model->operators.begin() + op_index; in Run()
45 if (!GetMatchingRnnArray(model, src_op->outputs[kOutputTensor], in Run()
50 if (!GetMatchingRnnArray(model, src_op->outputs[kCellStateTensor], in Run()
56 int num_cell = model->GetArray(src_op->inputs[kInputToInputWeightsTensor]) in Run()
59 int num_input = model->GetArray(src_op->inputs[kInputToInputWeightsTensor]) in Run()
63 model->GetArray(src_op->inputs[kRecurrentToInputWeightsTensor]) in Run()
73 string merged_weights = AvailableArrayName(*model, base_name + "weights"); in Run()
74 auto& array = model->GetOrCreateArray(merged_weights); in Run()
86 model->GetArray(src_op->inputs[kInputToInputWeightsTensor]), 0, 0); in Run()
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Didentify_lstm_split_inputs.cc28 bool SplitLstmCellInputs::Run(Model* model, std::size_t op_index) { in Run() argument
30 auto op_it = model->operators.begin() + op_index; in Run()
45 *model, curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT]) || in Run()
47 *model, curr_op->inputs[LstmCellOperator::BIASES_INPUT])) { in Run()
52 if (!model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT]) in Run()
60 int num_input = model->GetArray(curr_op->inputs[LstmCellOperator::DATA_INPUT]) in Run()
66 model->GetArray(curr_op->outputs[LstmCellOperator::ACTIV_OUTPUT]) in Run()
77 model->GetArray(curr_op->inputs[LstmCellOperator::WEIGHTS_INPUT]); in Run()
84 model, &(lstm_cell_op->inputs[kInputToInputWeightsTensor]), in Run()
86 CopySubArrayToArray(model, &(lstm_cell_op->inputs[kInputToCellWeightsTensor]), in Run()
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Dunroll_batch_matmul.cc39 bool UnrollBatchMatMul::Run(Model* model, std::size_t op_index) { in Run() argument
40 auto batch_op_it = model->operators.begin() + op_index; in Run()
48 const auto& input_array_a = model->GetArray(batch_op->inputs[0]); in Run()
49 const auto& input_array_b = model->GetArray(batch_op->inputs[1]); in Run()
65 const auto matmul_op_it = model->operators.emplace(batch_op_it, matmul_op); in Run()
68 model->operators.erase(batch_op_it); in Run()
88 CreateInt32Array(model, batch_name + "/slice_a/slice/begin", in Run()
91 model, batch_name + "/slice_a/slice/size", in Run()
94 slice_a_op->outputs = {AvailableArrayName(*model, batch_name + "/slice_a")}; in Run()
95 auto& slice_a_op_output = model->GetOrCreateArray(slice_a_op->outputs[0]); in Run()
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Didentify_l2_normalization.cc31 Model* model, const Operator* op) { in FindOperator() argument
32 auto it = model->operators.begin(); in FindOperator()
33 for (; it != model->operators.end(); ++it) { in FindOperator()
42 bool IdentifyL2Normalization::Run(Model* model, std::size_t op_index) { in Run() argument
43 const auto div_it = model->operators.begin() + op_index; in Run()
56 GetOpWithOutput(*model, div_or_mul_op->inputs[0]), in Run()
57 GetOpWithOutput(*model, div_or_mul_op->inputs[1]), in Run()
67 GetOpWithOutput(*model, sqrt_or_rsqrt_op->inputs[0]); in Run()
85 model->GetArray(op_producing_sqrt_or_rsqrt_input->inputs[i]); in Run()
101 op_producing_add_input = GetOpWithOutput(*model, add_op->inputs[1 - i]); in Run()
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Dhardcode_min_max.cc29 bool HardcodeMinMaxForIm2colArray(Model* model, Operator* op) { in HardcodeMinMaxForIm2colArray() argument
33 auto& im2col_array = model->GetArray(op->outputs[1]); in HardcodeMinMaxForIm2colArray()
37 const auto& input_array = model->GetArray(op->inputs[0]); in HardcodeMinMaxForIm2colArray()
49 bool HardcodeMinMaxForL2Normalization(Model* model, Operator* op) { in HardcodeMinMaxForL2Normalization() argument
50 auto& output_array = model->GetArray(op->outputs[0]); in HardcodeMinMaxForL2Normalization()
54 const auto& input_array = model->GetArray(op->inputs[0]); in HardcodeMinMaxForL2Normalization()
66 bool HardcodeMinMaxForConcatenation(Model* model, Operator* op) { in HardcodeMinMaxForConcatenation() argument
73 if (model->GetArray(input).minmax) { in HardcodeMinMaxForConcatenation()
75 const auto* minmax = model->GetArray(input).minmax.get(); in HardcodeMinMaxForConcatenation()
82 auto& output = model->GetArray(op->outputs[0]); in HardcodeMinMaxForConcatenation()
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/external/tensorflow/tensorflow/python/keras/_impl/keras/engine/
Dtraining_test.py50 model = keras.models.Model([a, b], [d, e])
56 model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights)
65 model.fit(
70 model.fit(
75 model.fit(
80 model.train_on_batch([input_a_np, input_b_np], [output_d_np, output_e_np])
83 model.fit(
91 model.fit(
98 model.fit(
105 model.fit(
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Dtraining_eager_test.py43 model = keras.models.Model([a, b], [d, e])
49 model.compile(optimizer, loss, metrics=metrics, loss_weights=loss_weights)
58 model.fit(
63 model.fit(
68 model.fit(
75 model.fit(
82 model.fit(
89 model.fit(
96 model.train_on_batch([input_a_np, input_b_np], [output_d_np, output_e_np])
99 model.fit(
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Dtraining_eager.py62 def _eager_metrics_fn(model, outputs, targets): argument
81 for i in range(len(model.outputs)):
82 output_metrics = model.nested_metrics[i]
85 nested_output_metric, K.int_shape(model.outputs[i]),
86 model.loss_functions[i])
88 if len(model.output_names) > 1:
89 metric_name = model.output_names[i] + '_' + metric_name
90 if metric_name not in model.metrics_names:
91 model.metrics_names.append(metric_name)
101 def _model_loss(model, inputs, targets, training=False): argument
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/external/ImageMagick/PerlMagick/demo/
Ddemo.pl16 $model=Image::Magick->new();
17 $x=$model->ReadImage('model.gif');
19 $model->Label('Magick');
20 $model->Set(background=>'white');
34 $example=$model->Clone();
40 $example=$model->Clone();
46 $example=$model->Clone();
52 $example=$model->Clone();
58 $example=$model->Clone();
64 $example=$model->Clone();
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/external/ImageMagick/www/source/
Dexamples.pl16 $model=Image::Magick->new();
17 $x=$model->ReadImage('model.gif');
19 $model->Label('Magick');
20 $model->Set(background=>'white');
34 $example=$model->Clone();
40 $example=$model->Clone();
46 $example=$model->Clone();
52 $example=$model->Clone();
58 $example=$model->Clone();
64 $example=$model->Clone();
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/external/javaparser/javaparser-symbol-solver-testing/src/test/resources/javasymbolsolver_0_6_0/expected_output/java-symbol-solver-core/
Dcom_github_javaparser_symbolsolver_javaparsermodel_contexts_MethodCallExprContext.txt4 …r.javaparsermodel.JavaParserFacade.get(com.github.javaparser.symbolsolver.model.resolution.TypeSol…
7 …().getGenericParameterByName(name) ==> com.github.javaparser.symbolsolver.model.typesystem.Referen…
8 …Line 66) typeOfScope.asReferenceType() ==> com.github.javaparser.symbolsolver.model.typesystem.Typ…
16 …on.Context.solveType(java.lang.String, com.github.javaparser.symbolsolver.model.resolution.TypeSol…
17 …Line 86) ref.isSolved() ==> com.github.javaparser.symbolsolver.model.resolution.SymbolReference.is…
18 …model.declarations.TypeDeclaration, java.lang.String, java.util.List<com.github.javaparser.symbols…
19 …Line 87) ref.getCorrespondingDeclaration() ==> com.github.javaparser.symbolsolver.model.resolution…
20 …Line 88) m.isSolved() ==> com.github.javaparser.symbolsolver.model.resolution.SymbolReference.isSo…
21 …Line 89) m.getCorrespondingDeclaration() ==> com.github.javaparser.symbolsolver.model.resolution.S…
22 …itList(com.github.javaparser.symbolsolver.model.resolution.TypeSolver, com.github.javaparser.symbo…
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Dcom_github_javaparser_symbolsolver_resolution_MethodResolutionLogic.txt6 …utionLogic.findCommonType(java.util.List<com.github.javaparser.symbolsolver.model.typesystem.Type>)
10 …model.declarations.MethodDeclaration, java.lang.String, java.util.List<com.github.javaparser.symbo…
12 …Line 70) method.getName() ==> com.github.javaparser.symbolsolver.model.declarations.Declaration.ge…
13 …Line 73) method.hasVariadicParameter() ==> com.github.javaparser.symbolsolver.model.declarations.M…
14 …Line 74) method.getNumberOfParams() ==> com.github.javaparser.symbolsolver.model.declarations.Meth…
15 …Line 75) method.getNumberOfParams() ==> com.github.javaparser.symbolsolver.model.declarations.Meth…
17 …Line 77) method.getLastParam().getType() ==> com.github.javaparser.symbolsolver.model.declarations…
18 …Line 77) method.getLastParam() ==> com.github.javaparser.symbolsolver.model.declarations.MethodLik…
20 …=> com.github.javaparser.symbolsolver.model.typesystem.Type.isAssignableBy(com.github.javaparser.s…
21 …Line 80) method.getTypeParameters() ==> com.github.javaparser.symbolsolver.model.declarations.Type…
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Dcom_github_javaparser_symbolsolver_resolution_ConstructorResolutionLogic.txt6 …utionLogic.findCommonType(java.util.List<com.github.javaparser.symbolsolver.model.typesystem.Type>)
10 …model.declarations.ConstructorDeclaration, java.util.List<com.github.javaparser.symbolsolver.model…
11 …Line 65) constructor.hasVariadicParameter() ==> com.github.javaparser.symbolsolver.model.declarati…
12 …Line 66) constructor.getNumberOfParams() ==> com.github.javaparser.symbolsolver.model.declarations…
13 …Line 67) constructor.getNumberOfParams() ==> com.github.javaparser.symbolsolver.model.declarations…
15 …Line 69) constructor.getLastParam().getType() ==> com.github.javaparser.symbolsolver.model.declara…
16 …Line 69) constructor.getLastParam() ==> com.github.javaparser.symbolsolver.model.declarations.Meth…
18 …=> com.github.javaparser.symbolsolver.model.typesystem.Type.isAssignableBy(com.github.javaparser.s…
19 …Line 72) constructor.getTypeParameters() ==> com.github.javaparser.symbolsolver.model.declarations…
20 …lver.model.typesystem.Type, com.github.javaparser.symbolsolver.model.declarations.TypeParameterDec…
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Dcom_github_javaparser_symbolsolver_model_typesystem_LazyType.txt2 …Line 25) getType().isArray() ==> com.github.javaparser.symbolsolver.model.typesystem.Type.isArray()
3 Line 25) getType() ==> com.github.javaparser.symbolsolver.model.typesystem.LazyType.getType()
4 …Line 30) getType().arrayLevel() ==> com.github.javaparser.symbolsolver.model.typesystem.Type.array…
5 Line 30) getType() ==> com.github.javaparser.symbolsolver.model.typesystem.LazyType.getType()
6 …Line 35) getType().isPrimitive() ==> com.github.javaparser.symbolsolver.model.typesystem.Type.isPr…
7 Line 35) getType() ==> com.github.javaparser.symbolsolver.model.typesystem.LazyType.getType()
8 Line 40) getType().isNull() ==> com.github.javaparser.symbolsolver.model.typesystem.Type.isNull()
9 Line 40) getType() ==> com.github.javaparser.symbolsolver.model.typesystem.LazyType.getType()
10 …Line 45) getType().isReference() ==> com.github.javaparser.symbolsolver.model.typesystem.Type.isRe…
11 Line 45) getType() ==> com.github.javaparser.symbolsolver.model.typesystem.LazyType.getType()
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/external/tensorflow/tensorflow/contrib/lite/toco/
Dtooling_util.cc65 bool IsInputArray(const Model& model, const string& name) { in IsInputArray() argument
66 for (const auto& input_array : model.flags.input_arrays()) { in IsInputArray()
74 bool IsArrayConsumed(const Model& model, const string& name) { in IsArrayConsumed() argument
75 if (GetOpWithInput(model, name)) { in IsArrayConsumed()
78 for (const string& model_output : model.flags.output_arrays()) { in IsArrayConsumed()
83 for (const auto& rnn_state : model.flags.rnn_states()) { in IsArrayConsumed()
91 int CountTrueOutputs(const Model& model, const Operator& op) { in CountTrueOutputs() argument
94 if (IsArrayConsumed(model, output)) { in CountTrueOutputs()
101 int CountOpsWithInput(const Model& model, const string& array_name) { in CountOpsWithInput() argument
103 for (const auto& op : model.operators) { in CountOpsWithInput()
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Dimport_tensorflow.cc323 Model* model) { in ConvertConstOperator() argument
328 auto& array = model->GetOrCreateArray(node.name()); in ConvertConstOperator()
362 Model* model) { in ConvertConvOperator() argument
379 GetOpWithOutput(*model, reordered_weights_name); in ConvertConvOperator()
391 model->operators.emplace_back(reorder); in ConvertConvOperator()
410 model->operators.emplace_back(conv); in ConvertConvOperator()
415 Model* model) { in ConvertDepthwiseConvOperator() argument
432 GetOpWithOutput(*model, reordered_weights_name); in ConvertDepthwiseConvOperator()
444 model->operators.emplace_back(reorder); in ConvertDepthwiseConvOperator()
463 model->operators.emplace_back(conv); in ConvertDepthwiseConvOperator()
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/external/javaparser/javaparser-symbol-solver-testing/src/test/resources/javasymbolsolver_0_6_0/expected_output/java-symbol-solver-logic/
Dcom_github_javaparser_symbolsolver_logic_InferenceContext.txt2 …Line 44) tp.getName() ==> com.github.javaparser.symbolsolver.model.declarations.TypeParameterDecla…
4 …ferenceVariableType.setCorrespondingTp(com.github.javaparser.symbolsolver.model.declarations.TypeP…
6 …Line 48) tp.getName() ==> com.github.javaparser.symbolsolver.model.declarations.TypeParameterDecla…
8 …Line 50) tp.getName() ==> com.github.javaparser.symbolsolver.model.declarations.TypeParameterDecla…
9 ….InferenceContext.placeInferenceVariables(com.github.javaparser.symbolsolver.model.typesystem.Type)
10 ….InferenceContext.placeInferenceVariables(com.github.javaparser.symbolsolver.model.typesystem.Type)
11 …spondance(com.github.javaparser.symbolsolver.model.typesystem.Type, com.github.javaparser.symbolso…
12 ….InferenceContext.placeInferenceVariables(com.github.javaparser.symbolsolver.model.typesystem.Type)
13 …Line 69) formalType.isReferenceType() ==> com.github.javaparser.symbolsolver.model.typesystem.Type…
14 …Line 69) actualType.isReferenceType() ==> com.github.javaparser.symbolsolver.model.typesystem.Type…
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/external/clang/utils/
Dmodfuzz.py22 def valid(self, model): argument
24 if i not in model.decls:
27 if i in model.decls:
31 def apply(self, model, name): argument
33 model.decls[i] = True
34 model.source += self.text % {'name': name}
90 model = CodeModel()
94 for d in mutations(model):
95 d(model)
97 if not model.fails():
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/external/ImageMagick/Magick++/demo/
Ddemo.cpp45 Image model( srcdir + "model.miff" ); in main() local
46 model.label( "Magick++" ); in main()
47 model.borderColor( "black" ); in main()
48 model.backgroundColor( "black" ); in main()
65 Image example = model; in main()
84 example = model; in main()
95 example = model; in main()
101 example = model; in main()
107 example = model; in main()
114 example = model; in main()
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/external/tensorflow/tensorflow/python/keras/_impl/keras/layers/
Dwrappers_test.py32 model = keras.models.Sequential()
33 model.add(
36 model.compile(optimizer='rmsprop', loss='mse')
37 model.fit(
44 model.get_config()
48 model = keras.models.Sequential()
49 model.add(
52 model.compile(optimizer='rmsprop', loss='mse')
53 model.fit(
62 model = keras.models.Sequential()
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