| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| regression_test.py | 39 regressor = learn.LinearRegressor(
42 regressor.fit(x, y, steps=200)
43 self.assertIn("linear//weight", regressor.get_variable_names())
44 regressor_weights = regressor.get_variable_value("linear//weight")
48 # assert abs(bias - regressor.bias_) < 0.1
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| nonlinear_test.py | 67 regressor = dnn.DNNRegressor(
71 regressor.fit(boston.data,
75 weights = ([regressor.get_variable_value("dnn/hiddenlayer_0/weights")] +
76 [regressor.get_variable_value("dnn/hiddenlayer_1/weights")] +
77 [regressor.get_variable_value("dnn/hiddenlayer_2/weights")] +
78 [regressor.get_variable_value("dnn/logits/weights")])
84 biases = ([regressor.get_variable_value("dnn/hiddenlayer_0/biases")] +
85 [regressor.get_variable_value("dnn/hiddenlayer_1/biases")] +
86 [regressor.get_variable_value("dnn/hiddenlayer_2/biases")] +
87 [regressor.get_variable_value("dnn/logits/biases")]
[all...] |
| linear_test.py | [all...] |
| multioutput_test.py | 38 regressor = learn.LinearRegressor(
41 regressor.fit(x, y, steps=100)
42 score = mean_squared_error(np.array(list(regressor.predict_scores(x))), y)
|
| debug_test.py | 628 regressor = debug.DebugRegressor(
631 regressor.fit(input_fn=input_fn, steps=200)
632 scores = regressor.evaluate(input_fn=input_fn, steps=1)
645 regressor = debug.DebugRegressor(
648 regressor.fit(input_fn=_input_fn, steps=200)
649 scores = regressor.evaluate(input_fn=_input_fn, steps=1)
657 regressor = debug.DebugRegressor(
659 regressor.fit(x=train_x, y=train_y, steps=200)
660 scores = regressor.evaluate(x=train_x, y=train_y, steps=1)
681 regressor = debug.DebugRegressor
[all...] |
| dnn_test.py | [all...] |
| dnn_linear_combined_test.py | [all...] |
| logistic_regressor_test.py | 71 regressor = logistic_regressor.LogisticRegressor(
75 regressor.fit(input_fn=_iris_data_input_fn, steps=1)
76 eval_metrics = regressor.evaluate(input_fn=_iris_data_input_fn, steps=1)
92 regressor.fit(input_fn=_iris_data_input_fn, steps=100)
93 eval_metrics = regressor.evaluate(input_fn=_iris_data_input_fn, steps=1)
|
| debug.py | 238 """A regressor for TensorFlow Debug models.
245 regressor = DebugRegressor()
255 regressor.fit(input_fn=input_fn_train)
258 loss = regressor.evaluate(input_fn=input_fn_eval)["loss"]
261 predicted_targets = regressor.predict_scores(new_samples)
|
| __init__.py | 107 A regressor for TensorFlow DNN models.
|
| /external/webrtc/webrtc/modules/audio_coding/codecs/ilbc/ |
| xcorr_coef.c | 28 int16_t *regressor, /* (i) second array */
58 max=WebRtcSpl_MaxAbsValueW16(regressor, subl + searchLen - 1);
59 rp_beg = regressor;
60 rp_end = regressor + subl;
62 max = WebRtcSpl_MaxAbsValueW16(regressor - searchLen, subl + searchLen - 1);
63 rp_beg = regressor - 1;
64 rp_end = regressor + subl - 1;
78 Energy=WebRtcSpl_DotProductWithScale(regressor, regressor, subl, shifts);
82 rp = ®ressor[pos]
[all...] |
| enhancer_interface.c | 57 int16_t *target, *regressor; local
119 regressor = target - 10;
122 max16 = WebRtcSpl_MaxAbsValueW16(®ressor[-50], ENH_BLOCKL_HALF + 50 - 1);
127 WebRtcSpl_CrossCorrelation(corr32, target, regressor, ENH_BLOCKL_HALF, 50,
148 ener = WebRtcSpl_DotProductWithScale(regressor - lagmax[i],
149 regressor - lagmax[i],
201 regressor=in+tlag-1;
204 max16 = WebRtcSpl_MaxAbsValueW16(regressor, plc_blockl + 3 - 1);
211 WebRtcSpl_CrossCorrelation(corr32, target, regressor, plc_blockl, 3, shifts,
|
| xcorr_coef.h | 31 int16_t *regressor, /* (i) second array */
|
| /external/tensorflow/tensorflow/contrib/linear_optimizer/python/ |
| sdca_estimator_test.py | 262 regressor = sdca_estimator.SDCALinearRegressor(
266 regressor.fit(input_fn=input_fn, steps=20)
267 loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
269 self.assertIn('linear/x/weight', regressor.get_variable_names())
270 regressor_weights = regressor.get_variable_value('linear/x/weight')
303 regressor = sdca_estimator.SDCALinearRegressor(
310 regressor.fit(input_fn=input_fn, steps=20)
311 loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
336 # Regressor with no L1 regularization.
337 regressor = sdca_estimator.SDCALinearRegressor
[all...] |
| sdca_estimator.py | 420 regressor = SDCALinearRegressor(
435 regressor.fit(input_fn=input_fn_train)
436 regressor.evaluate(input_fn=input_fn_eval)
437 regressor.predict_scores(input_fn=input_fn_test) # returns predicted scores.
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/utils/ |
| export_test.py | 92 regressor = learn.LinearRegressor(feature_columns=cont_features)
96 regressor.fit(x, y, steps=10, monitors=[export_monitor])
110 regressor = learn.LinearRegressor(feature_columns=cont_features)
111 regressor.fit(x, y, steps=10, monitors=[export_monitor])
131 regressor = learn.LinearRegressor(feature_columns=[_X_COLUMN])
133 regressor.fit(input_fn=_training_input_fn, steps=10, monitors=[monitor])
149 regressor = learn.LinearRegressor(feature_columns=[_X_COLUMN])
152 regressor.fit(input_fn=_training_input_fn, steps=10, monitors=[monitor])
173 regressor = learn.LinearRegressor(feature_columns=[_X_COLUMN])
175 regressor.fit(input_fn=_training_input_fn, steps=10, monitors=[monitor]
[all...] |
| /external/tensorflow/tensorflow/examples/learn/ |
| boston.py | 45 regressor = tf.estimator.DNNRegressor(
51 regressor.train(input_fn=train_input_fn, steps=2000)
57 predictions = regressor.predict(input_fn=test_input_fn)
66 scores = regressor.evaluate(input_fn=test_input_fn)
|
| /external/tensorflow/tensorflow/examples/tutorials/input_fn/ |
| boston.py | 57 regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols,
62 regressor.train(input_fn=get_input_fn(training_set), steps=5000)
65 ev = regressor.evaluate(
71 y = regressor.predict(
|
| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| structural_ensemble_test.py | 107 regressor = estimators.StructuralEnsembleRegressor(
119 regressor.train(input_fn=train_input_fn, steps=1)
122 evaluation = regressor.evaluate(input_fn=eval_input_fn, steps=1)
126 regressor.predict(input_fn=predict_input_fn)
133 regressor = estimators.StructuralEnsembleRegressor(
143 regressor.train(input_fn=train_input_fn, steps=1)
146 evaluation = regressor.evaluate(input_fn=eval_input_fn, steps=1)
149 regressor.predict(input_fn=predict_input_fn)
|
| /external/tensorflow/tensorflow/contrib/tensor_forest/client/ |
| random_forest_test.py | 60 regressor = random_forest.TensorForestEstimator(hparams.fill())
66 regressor.fit(x=data, y=labels, steps=100, batch_size=50)
67 regressor.evaluate(x=data, y=labels, steps=10)
|
| /external/tensorflow/tensorflow/python/estimator/canned/ |
| prediction_keys.py | 26 PREDICTIONS: Used by models that predict values, such as regressor models.
|
| baseline.py | 282 """A regressor that can establish a simple baseline.
284 This regressor ignores feature values and will learn to predict the average
292 regressor = BaselineRegressor()
302 regressor.train(input_fn=input_fn_train)
305 loss = regressor.evaluate(input_fn=input_fn_eval)["loss"]
308 predictions = regressor.predict(new_samples)
|
| parsing_utils.py | 184 * It is difficult to map expected label by a regressor such as `DNNRegressor`
211 Example usage with a regressor:
220 # This label configuration tells the regressor the following:
|
| linear_test.py | 34 # Tests for Linear Regressor.
|
| dnn_linear_combined_test.py | 96 # A function to mimic linear-regressor init reuse same tests.
427 # A function to mimic dnn-regressor init reuse same tests.
[all...] |
