| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/ |
| D | head.py | 83 model_outputs = self.state_manager.define_loss(
87 model_outputs.loss)
88 return model_outputs
102 model_outputs = self.create_loss(features, mode)
105 model_outputs.loss,
108 loss=model_outputs.loss,
116 model_outputs = self.create_loss(features, mode)
119 for prediction_key, prediction_value in model_outputs.predictions.items():
123 feature_keys.FilteringResults.TIMES, model_outputs.prediction_times)
126 model_outputs.end_state))
[all …]
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| D | state_management_test.py | 128 model_outputs = chainer.define_loss(
136 model_outputs.loss.eval()
137 outputs = model_outputs.loss.eval()
222 model_outputs = chainer.define_loss(
229 model_outputs.loss.eval()
230 returned_loss = model_outputs.loss.eval()
302 model_outputs = chainer.define_loss(
305 end_state = session.run(model_outputs.end_state)
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| D | ar_model.py | 804 model_outputs = self._process_window(
814 assert len(model_outputs.predictions) == 3
815 assert "mean" in model_outputs.predictions
816 assert "covariance" in model_outputs.predictions
817 assert "observed" in model_outputs.predictions
820 iteration_number, model_outputs.loss),
822 iteration_number, model_outputs.predictions["mean"]),
824 iteration_number, model_outputs.predictions["covariance"]))
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| D | test_utils.py | 127 model_outputs = eval_state_manager.define_loss(
135 true_param_loss = model_outputs.loss.eval(feed_dict=true_parameters)
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| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| D | state_space_model_test.py | 138 model_outputs = random_model.get_batch_loss(
151 model_outputs.loss.eval()
266 model_outputs = random_model.get_batch_loss(
280 (model_outputs.end_state, model_outputs.predictions))
316 model_outputs = state_manager.define_loss(
323 model_outputs.loss.eval()
325 priors_from_time) = model_outputs.end_state
327 outputs = (model_outputs.loss, posteriors,
328 model_outputs.predictions)
398 model_outputs = passthrough.define_loss(
[all …]
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| /external/tensorflow/tensorflow/lite/tools/accuracy/ilsvrc/ |
| D | imagenet_topk_eval.cc | 47 const std::vector<Tensor>& model_outputs, const Tensor& ground_truth) { in ComputeEval() argument
48 if (model_outputs.size() != 1) { in ComputeEval()
50 model_outputs.size()); in ComputeEval()
52 const Tensor& output = model_outputs[0]; in ComputeEval()
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| D | imagenet_topk_eval.h | 65 Status ComputeEval(const std::vector<Tensor>& model_outputs,
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| /external/tensorflow/tensorflow/lite/tools/accuracy/ |
| D | eval_pipeline_test.cc | 36 Status ComputeEval(const std::vector<Tensor>& model_outputs, in ComputeEval() argument
38 model_outputs_ = model_outputs; in ComputeEval()
45 std::vector<Tensor> model_outputs() { return model_outputs_; } in model_outputs() function in tensorflow::metrics::__anon7671d8850111::NoOpAccuracyEval
73 auto outputs = accuracy_eval.model_outputs(); in TEST()
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| D | accuracy_eval_stage.h | 44 virtual Status ComputeEval(const std::vector<Tensor>& model_outputs,
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| D | eval_pipeline_builder_test.cc | 75 Status ComputeEval(const std::vector<Tensor>& model_outputs, in ComputeEval() argument
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | network.py | 1115 model_outputs = []
1122 model_outputs.append(
1124 model_outputs = nest.pack_sequence_as(self._nested_outputs, model_outputs)
1126 if not nest.is_sequence(model_outputs):
1127 model_outputs = [model_outputs]
1128 model_outputs = tf_utils.convert_inner_node_data(model_outputs)
1129 config['output_layers'] = model_outputs
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| /external/tensorflow/tensorflow/lite/testing/nnapi_tflite_zip_tests/ |
| D | parse_testdata.cc | 195 int model_outputs = interpreter->outputs().size(); in CheckOutputs() local
196 TF_LITE_ENSURE_EQ(context, model_outputs, example.outputs.size()); in CheckOutputs()
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| /external/tensorflow/tensorflow/lite/testing/ |
| D | parse_testdata.cc | 192 int model_outputs = interpreter->outputs().size(); in CheckOutputs() local
193 TF_LITE_ENSURE_EQ(context, model_outputs, example.outputs.size()); in CheckOutputs()
|