| /external/tensorflow/tensorflow/contrib/predictor/ |
| testing_common.py | 53 predictions = {'sum': math_ops.add(x, y, name='sum'),
58 for k, v in predictions.items()}
62 predictions=predictions,
68 for k, v in predictions.items()}
71 predictions=predictions,
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| estimators_test.py | 63 predictions = features["transformed_x"]
66 return predictions, loss, update_global_step
72 # predictions = transformed_x (9)
78 "label": metric_spec.MetricSpec(lambda predictions, labels: labels)
103 predictions = features["x"]
106 return predictions, loss, update_global_step
112 # predictions = transformed_x (9)
118 "label": metric_spec.MetricSpec(lambda predictions, labels: labels)
144 predictions = features["x"]
147 return predictions, loss, update_global_ste
[all...] |
| estimator_input_test.py | 149 mode=mode, predictions=prediction, loss=loss, train_op=train_op)
199 predictions = np.array(list(est2.predict(x=boston_input)))
200 other_score = _sklearn.mean_squared_error(predictions,
215 predictions = np.array(list(est.predict(x=boston.data)))
216 other_score = _sklearn.mean_squared_error(predictions, boston.target)
233 predictions = np.array(list(est.predict(x=boston_input)))
234 other_score = _sklearn.mean_squared_error(predictions, boston.target)
250 predictions = est.predict(x=iris.data)
252 self.assertEqual(predictions['prob'].shape[0], iris.target.shape[0])
253 self.assertAllClose(predictions['class'], predictions_class
[all...] |
| /external/tensorflow/tensorflow/contrib/framework/python/framework/ |
| tensor_util.py | 84 "labels and predictions have also been switched.")
85 def remove_squeezable_dimensions(predictions, labels, name=None):
86 """Squeeze last dim if ranks of `predictions` and `labels` differ by 1.
92 predictions: Predicted values, a `Tensor` of arbitrary dimensions.
93 labels: Label values, a `Tensor` whose dimensions match `predictions`.
97 Tuple of `predictions` and `labels`, possibly with last dim squeezed.
100 [predictions, labels]):
101 predictions = ops.convert_to_tensor(predictions)
103 predictions_shape = predictions.get_shape(
[all...] |
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| confusion_matrix_test.py | 47 labels=[1, 2, 4], predictions=[2, 2, 4])))
49 def _testConfMatrix(self, labels, predictions, truth, weights=None,
52 dtype = predictions.dtype
54 labels, predictions, dtype=dtype, weights=weights,
61 predictions = np.arange(5, dtype=dtype)
71 self._testConfMatrix(labels=labels, predictions=predictions, truth=truth)
125 predictions = np.asarray([1, 2, 3], dtype=dtype)
137 self._testConfMatrix(labels=labels, predictions=predictions, truth=truth
[all...] |
| /external/libtextclassifier/lang_id/ |
| lang-id_jni.cc | 48 const std::vector<std::pair<std::string, float>>& predictions = local
49 lang_id_result.predictions;
54 env->NewObjectArray(predictions.size(), result_class.get(), nullptr);
55 for (int i = 0; i < predictions.size(); i++) {
58 env->NewStringUTF(predictions[i].first.c_str()),
59 static_cast<jfloat>(predictions[i].second)));
|
| /external/tensorflow/tensorflow/examples/learn/ |
| iris_custom_decay_dnn.py | 40 # Compute predictions.
43 predictions = {
47 return tf.estimator.EstimatorSpec(mode, predictions=predictions)
65 labels=labels, predictions=predicted_classes)
86 predictions = classifier.predict(input_fn=test_input_fn)
87 y_predicted = np.array(list(p['class'] for p in predictions))
|
| iris_custom_model.py | 41 # Compute predictions.
44 predictions = {
48 return tf.estimator.EstimatorSpec(mode, predictions=predictions)
62 labels=labels, predictions=predicted_classes)
83 predictions = classifier.predict(input_fn=test_input_fn)
84 y_predicted = np.array(list(p['class'] for p in predictions))
|
| /cts/tests/tests/gesture/src/android/gesture/cts/ |
| GestureStorageTester.java | 73 ArrayList<Prediction> predictions = mFixture.recognize(newLineGesture); local
74 assertEquals(1, predictions.size());
75 assertEquals(TEST_GESTURE_NAME, predictions.get(0).name);
|
| /external/tensorflow/tensorflow/contrib/metrics/python/ops/ |
| metric_ops_large_test.py | 40 predictions = random_ops.random_uniform(
45 labels=labels, predictions=predictions, num_thresholds=201)
|
| /external/tensorflow/tensorflow/core/kernels/ |
| in_topk_op.cc | 56 errors::InvalidArgument("predictions must be 2-dimensional"));
60 errors::InvalidArgument("First dimension of predictions ",
64 const auto& predictions = predictions_in.matrix<T>(); variable
74 const auto num_classes = predictions.dimension(1);
79 T target_prediction = predictions(b, target);
84 T pred = predictions(b, i);
103 .HostMemory("predictions")
110 .HostMemory("predictions")
118 .HostMemory("predictions")
126 .HostMemory("predictions")
[all...] |
| /external/tensorflow/tensorflow/python/saved_model/ |
| signature_def_utils_impl.py | 71 def regression_signature_def(examples, predictions):
72 """Creates regression signature from given examples and predictions.
80 predictions: A float `Tensor`.
92 if predictions is None:
93 raise ValueError('Regression predictions cannot be None.')
100 output_tensor_info = utils.build_tensor_info(predictions)
120 """Creates classification signature from given examples and predictions.
213 inputs, loss, predictions=None, metrics=None):
216 predictions=predictions, metrics=metrics
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/estimator_batch/ |
| custom_loss_head.py | 43 head_name: name of the head. Predictions, summary, metrics keys are
49 metrics_fn: a function that takes predictions dict, labels and weights and
71 def _metrics(self, eval_loss, predictions, labels, weights):
73 return self._metrics_fn(predictions, labels, weights)
|
| /external/tensorflow/tensorflow/python/saved_model/model_utils/ |
| export_utils.py | 47 # tensors and output predictions, losses, and/or metrics (depending on the mode)
250 mode, serving_export_outputs=None, predictions=None, loss=None,
262 predictions: A dict of Tensors or single Tensor representing model
263 predictions. This argument is only used if serving_export_outputs is not
284 return get_export_outputs(serving_export_outputs, predictions)
287 loss=loss, predictions=predictions, metrics=metrics)}
290 loss=loss, predictions=predictions, metrics=metrics)}
293 def get_export_outputs(export_outputs, predictions)
[all...] |
| export_output_test.py | 246 predictions = {u'output1': constant_op.constant(['foo'])}
254 outputter = MockSupervisedOutput(loss, predictions, metrics)
257 outputter.predictions['predictions/output1'], predictions['output1'])
265 loss['my_loss'], predictions['output1'], metrics['metrics'])
268 outputter.predictions, {'predictions': predictions['output1']})
276 self.assertEqual(outputter.predictions, None
[all...] |
| /external/tensorflow/tensorflow/contrib/tensor_forest/client/ |
| random_forest_test.py | 81 predictions = list(classifier.predict(input_fn=predict_input_fn))
83 [pred['probabilities'] for pred in predictions])
104 predictions = list(regressor.predict(input_fn=predict_input_fn))
105 self.assertAllClose([24.], [pred['scores'] for pred in predictions], atol=1)
134 predictions = list(classifier.predict(input_fn=input_fn))
136 for pred in predictions:
192 predictions = list(est.predict(input_fn=predict_input_fn))
194 [pred['probabilities'] for pred in predictions])
219 predictions = list(regressor.predict(input_fn=predict_input_fn))
221 [[24.]], [pred['predictions'] for pred in predictions], atol=1
[all...] |
| /external/tensorflow/tensorflow/contrib/timeseries/examples/ |
| known_anomaly.py | 119 # feature, we should get relatively confident predictions before the indicated
121 # those times) and relatively uncertain predictions after.
122 (predictions,) = tuple(estimator.predict(
130 [evaluation["mean"][0], predictions["mean"]], axis=0))
132 [evaluation["covariance"][0], predictions["covariance"]], axis=0))
133 all_times = np.concatenate([times, predictions["times"]], axis=0)
143 anomaly_locations.append(predictions["times"][49])
|
| lstm.py | 100 # Transforms LSTM output into mean predictions.
121 def _filtering_step(self, current_times, current_values, state, predictions):
127 predictions. This distinction can be important for probabilistic models,
129 predictions.
136 predictions: The output of the previous `_prediction_step`.
138 A tuple of new state and a predictions dictionary updated to include a
150 predictions["loss"] = tf.reduce_mean(
156 return (new_state_tuple, predictions)
196 # predictions. In this example the features have no extra information, but
229 (predictions,) = tuple(estimator.predict
[all...] |
| /external/tensorflow/tensorflow/examples/get_started/regression/ |
| custom_regression.py | 43 # Reshape the output layer to a 1-dim Tensor to return predictions
44 predictions = tf.squeeze(output_layer, 1)
47 # In `PREDICT` mode we only need to return predictions.
49 mode=mode, predictions={"price": predictions})
52 average_loss = tf.losses.mean_squared_error(labels, predictions)
72 rmse = tf.metrics.root_mean_squared_error(labels, predictions)
|
| /external/tensorflow/tensorflow/examples/tutorials/layers/ |
| cnn_mnist.py | 89 predictions = {
90 # Generate predictions (for PREDICT and EVAL mode)
97 return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
113 labels=labels, predictions=predictions["classes"])}
130 # Set up logging for predictions
|
| /external/tensorflow/tensorflow/python/keras/ |
| integration_test.py | 63 predictions = model.predict(x_train)
64 self.assertEqual(predictions.shape, (x_train.shape[0], 2))
102 predictions = model.predict(x_train)
103 self.assertEqual(predictions.shape, (x_train.shape[0], 2))
137 predictions = model.predict(x_train)
138 self.assertEqual(predictions.shape, (x_train.shape[0], 2))
166 predictions = model.predict(x_train)
167 self.assertEqual(predictions.shape, (x_train.shape[0], 2))
204 predictions = model.predict(x_train)
205 self.assertEqual(predictions.shape, (x_train.shape[0], 2)
[all...] |
| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| target_column.py | 283 predictions = self.logits_to_predictions(logits, proba=False)
285 _run_metrics(predictions, labels, metrics,
328 predictions = math_ops.sigmoid(logits)
333 result[metric_name] = metric_op(predictions, labels_float)
362 predictions = self.logits_to_predictions(logits, proba=True)
364 _run_metrics(predictions, labels, proba_metrics,
431 def _run_metrics(predictions, labels, metrics, weights):
433 labels = math_ops.cast(labels, predictions.dtype)
436 result[name] = metric(predictions, labels, weights=weights)
438 result[name] = metric(predictions, labels
[all...] |
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
| losses_test.py | 64 # For positive lables, p oints with predictions 0.7 and larger get minimum
84 predictions = np.array(
89 predictions)
93 np.square(labels[:5] - predictions[:5]), loss[:5], atol=1e-4)
|
| /external/tensorflow/tensorflow/examples/speech_commands/ |
| label_wav.py | 18 then the predictions from running the model against the audio data will be
61 """Runs the audio data through the graph and prints predictions."""
64 # predictions will contain a two-dimensional array, where one
66 # predictions per class
68 predictions, = sess.run(softmax_tensor, {input_layer_name: wav_data})
71 top_k = predictions.argsort()[-num_top_predictions:][::-1]
74 score = predictions[node_id]
81 """Loads the model and labels, and runs the inference to print predictions."""
|
| /external/tensorflow/tensorflow/contrib/boosted_trees/python/training/functions/ |
| gbdt_batch_test.py | 43 def _squared_loss(label, unused_weights, predictions):
46 math_ops.squared_difference(predictions, label), 1, keepdims=True)
239 predictions = array_ops.constant(
249 "predictions": predictions,
250 "predictions_no_dropout": predictions,
261 _squared_loss(labels, weights, predictions)),
348 predictions = predictions_dict["predictions"]
354 _squared_loss(labels, weights, predictions)),
[all...] |
