Lines Matching refs:input_fn
82 def input_fn():
96 classifier.fit(input_fn=input_fn, steps=100)
97 loss1 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
98 classifier.fit(input_fn=input_fn, steps=200)
99 loss2 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
106 def input_fn():
122 classifier.fit(input_fn=input_fn, steps=100)
123 loss1 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
124 classifier.fit(input_fn=input_fn, steps=200)
125 loss2 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
137 classifier.fit(input_fn=test_data.iris_input_multiclass_fn, steps=100)
139 input_fn=test_data.iris_input_multiclass_fn, steps=100)
159 classifier.fit(input_fn=_input_fn, steps=100)
160 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
203 classifier.fit(input_fn=_input_fn, steps=50)
205 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
211 input_fn=predict_input_fn, as_iterable=True))
216 classifier.predict(input_fn=predict_input_fn, as_iterable=True))
235 classifier.fit(input_fn=_input_fn, steps=100)
236 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
261 classifier.fit(input_fn=_input_fn, steps=100)
262 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
281 classifier.fit(input_fn=_input_fn, steps=100)
282 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
305 classifier.fit(input_fn=test_data.iris_input_multiclass_fn, steps=100)
325 classifier.fit(input_fn=test_data.iris_input_multiclass_fn, steps=100)
327 input_fn=test_data.iris_input_multiclass_fn, steps=100)
341 classifier.fit(input_fn=test_data.iris_input_multiclass_fn, steps=100)
343 input_fn=test_data.iris_input_multiclass_fn, steps=100)
354 classifier.fit(input_fn=test_data.iris_input_multiclass_fn, steps=100)
356 input_fn=test_data.iris_input_multiclass_fn, steps=100)
384 classifier.fit(input_fn=_input_fn, steps=100)
386 input_fn=_input_fn,
406 input_fn=predict_input_fn)))
415 input_fn=_input_fn,
428 input_fn=_input_fn,
435 input_fn=_input_fn,
445 def input_fn(num_epochs=None):
457 classifier.fit(input_fn=input_fn, steps=500)
459 predict_input_fn = functools.partial(input_fn, num_epochs=1)
461 classifier.predict_proba(input_fn=predict_input_fn))
502 classifier.fit(input_fn=_input_fn, steps=200)
503 loss = classifier.evaluate(input_fn=_input_fn, steps=1)['loss']
509 def input_fn(num_epochs=None):
526 classifier.fit(input_fn=input_fn, steps=30)
527 predict_input_fn = functools.partial(input_fn, num_epochs=1)
530 input_fn=predict_input_fn, as_iterable=True))
533 input_fn=predict_input_fn, as_iterable=True))
540 input_fn=predict_input_fn, as_iterable=True))
543 input_fn=predict_input_fn, as_iterable=True))
577 classifier.fit(input_fn=_input_fn_train, steps=100)
578 scores = classifier.evaluate(input_fn=_input_fn_eval, steps=1)
589 scores_train_set = classifier.evaluate(input_fn=_input_fn_train, steps=1)
612 classifier.fit(input_fn=_input_fn, steps=100)
613 loss_unweighted = classifier.evaluate(input_fn=_input_fn, steps=1)['loss']
617 classifier.fit(input_fn=_input_fn, steps=100)
618 loss_weighted = classifier.evaluate(input_fn=_input_fn, steps=1)['loss']
625 def input_fn():
639 classifier.fit(input_fn=input_fn, steps=100)
647 def input_fn():
662 classifier.fit(input_fn=input_fn, steps=100)
668 def input_fn():
683 classifier.fit(input_fn=input_fn, steps=100)
690 def input_fn():
704 loss_no_reg = classifier_no_reg.fit(input_fn=input_fn, steps=100).evaluate(
705 input_fn=input_fn, steps=1)['loss']
706 loss_with_reg = classifier_with_reg.fit(input_fn=input_fn,
708 input_fn=input_fn,
715 def input_fn():
727 classifier.fit(input_fn=input_fn, steps=100)
728 loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
734 def input_fn():
750 classifier.fit(input_fn=input_fn, steps=100)
751 loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
757 # input_fn is identical to the one in testSdcaOptimizerRealValuedFeatures
760 def input_fn():
774 classifier.fit(input_fn=input_fn, steps=100)
775 loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
781 def input_fn():
800 classifier.fit(input_fn=input_fn, steps=50)
801 scores = classifier.evaluate(input_fn=input_fn, steps=1)
807 def input_fn():
831 classifier.fit(input_fn=input_fn, steps=50)
832 scores = classifier.evaluate(input_fn=input_fn, steps=1)
838 def input_fn():
862 classifier.fit(input_fn=input_fn, steps=50)
863 scores = classifier.evaluate(input_fn=input_fn, steps=1)
869 def input_fn():
895 classifier.fit(input_fn=input_fn, steps=10)
896 scores = classifier.evaluate(input_fn=input_fn, steps=1)
902 def input_fn():
933 classifier.fit(input_fn=input_fn, steps=50)
934 scores = classifier.evaluate(input_fn=input_fn, steps=1)
941 def input_fn():
958 classifier.fit(input_fn=input_fn, steps=100)
959 classifier.evaluate(input_fn=input_fn, steps=1)
986 def input_fn():
1000 classifier.fit(input_fn=input_fn, steps=100)
1001 loss1 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
1002 classifier.fit(input_fn=input_fn, steps=200)
1003 loss2 = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
1019 regressor.fit(input_fn=test_data.iris_input_multiclass_fn, steps=100)
1021 input_fn=test_data.iris_input_multiclass_fn, steps=1)
1052 regressor.fit(input_fn=_input_fn, steps=100)
1054 scores = regressor.evaluate(input_fn=_input_fn, steps=1)
1071 regressor.fit(input_fn=_input_fn_train, steps=100)
1072 scores = regressor.evaluate(input_fn=_input_fn_train, steps=1)
1105 regressor.fit(input_fn=_input_fn_train, steps=100)
1106 scores = regressor.evaluate(input_fn=_input_fn_eval, steps=1)
1140 regressor.fit(input_fn=_input_fn_train, steps=100)
1141 scores = regressor.evaluate(input_fn=_input_fn_eval, steps=1)
1174 regressor.fit(input_fn=_input_fn, steps=100)
1176 scores = regressor.evaluate(input_fn=_input_fn, steps=1)
1179 input_fn=_input_fn, as_iterable=False)
1181 predictions = regressor.predict(input_fn=_input_fn, as_iterable=False)
1212 regressor.fit(input_fn=_input_fn, steps=100)
1214 scores = regressor.evaluate(input_fn=_input_fn, steps=1)
1219 input_fn=predict_input_fn, as_iterable=True))
1223 input_fn=predict_input_fn, as_iterable=True))
1248 regressor.fit(input_fn=_input_fn, steps=100)
1250 input_fn=_input_fn,
1266 regressor.predict_scores(input_fn=predict_input_fn)))
1274 input_fn=_input_fn,
1286 input_fn=_input_fn,
1296 input_fn=_input_fn,
1333 regressor.fit(input_fn=_input_fn, steps=100)
1335 predictions = list(regressor.predict_scores(input_fn=predict_input_fn))
1340 predictions2 = list(regressor2.predict_scores(input_fn=predict_input_fn))
1385 regressor.fit(input_fn=_input_fn, steps=100)
1387 scores = regressor.evaluate(input_fn=_input_fn, steps=1)
1419 regressor.fit(input_fn=_input_fn, steps=100)
1421 scores = regressor.evaluate(input_fn=_input_fn, steps=1)
1451 def input_fn():
1465 regressor.fit(input_fn=input_fn, steps=20)
1466 loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
1476 def input_fn():
1507 regressor.fit(input_fn=input_fn, steps=20)
1508 loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
1514 def input_fn():
1539 regressor.fit(input_fn=input_fn, steps=20)
1540 no_l1_reg_loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
1558 regressor.fit(input_fn=input_fn, steps=20)
1559 l1_reg_loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
1586 def input_fn():
1611 regressor.fit(input_fn=input_fn, steps=100)
1619 def input_fn():
1664 regressor.fit(input_fn=input_fn, steps=200)
1681 def input_fn():
1716 regressor.fit(input_fn=input_fn, steps=100)
1751 def input_fn():
1766 linear_estimator.fit(input_fn=input_fn, steps=100)
1767 loss1 = linear_estimator.evaluate(input_fn=input_fn, steps=1)['loss']
1768 linear_estimator.fit(input_fn=input_fn, steps=400)
1769 loss2 = linear_estimator.evaluate(input_fn=input_fn, steps=1)['loss']
1777 def input_fn():
1793 linear_estimator.fit(input_fn=input_fn, steps=10)
1794 loss1 = linear_estimator.evaluate(input_fn=input_fn, steps=1)['loss']
1795 linear_estimator.fit(input_fn=input_fn, steps=100)
1796 loss2 = linear_estimator.evaluate(input_fn=input_fn, steps=1)['loss']
1833 est.fit(input_fn=boston_input_fn, steps=1)
1834 _ = est.evaluate(input_fn=boston_input_fn, steps=1)
