Lines Matching refs:input_fn
63 def input_fn():
70 return input_fn
97 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
100 input_fn=_input_fn_builder(test_features, None))
116 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
119 input_fn=_input_fn_builder(test_features, None))
133 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
136 input_fn=_input_fn_builder(test_features, None))
155 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
158 input_fn=_input_fn_builder(test_features, None))
178 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
181 input_fn=_input_fn_builder(test_features, None))
199 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
202 input_fn=_input_fn_builder(test_features, None))
224 input_fn = test_data.iris_input_logistic_fn
225 classifier.fit(input_fn=input_fn, steps=5)
226 scores = classifier.evaluate(input_fn=input_fn, steps=1)
242 classifier.fit(input_fn=_input_fn, steps=5)
243 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
290 classifier.fit(input_fn=_input_fn, steps=50)
292 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
296 predictions = list(classifier.predict_classes(input_fn=predict_input_fn))
320 classifier.fit(input_fn=_input_fn_float_label, steps=50)
323 predictions = list(classifier.predict_classes(input_fn=predict_input_fn))
326 classifier.predict_proba(input_fn=predict_input_fn))
333 input_fn = test_data.iris_input_multiclass_fn
334 classifier.fit(input_fn=input_fn, steps=200)
335 scores = classifier.evaluate(input_fn=input_fn, steps=1)
351 classifier.fit(input_fn=_input_fn, steps=200)
352 scores = classifier.evaluate(input_fn=_input_fn, steps=1)
378 classifier.fit(input_fn=_input_fn_train, steps=5)
379 scores = classifier.evaluate(input_fn=_input_fn_train, steps=1)
396 classifier.fit(input_fn=_input_fn_train, steps=5)
397 scores = classifier.evaluate(input_fn=_input_fn_train, steps=1)
427 classifier.fit(input_fn=_input_fn_train, steps=5)
428 scores = classifier.evaluate(input_fn=_input_fn_eval, steps=1)
456 classifier.fit(input_fn=_input_fn_train, steps=5)
457 scores = classifier.evaluate(input_fn=_input_fn_eval, steps=1)
486 classifier.fit(input_fn=_input_fn, steps=5)
488 input_fn=_input_fn,
508 list(classifier.predict_classes(input_fn=predict_input_fn)))
517 input_fn=_input_fn,
550 classifier.fit(input_fn=_input_fn, steps=5)
552 predictions1 = classifier.predict_classes(input_fn=predict_input_fn)
559 predictions2 = classifier2.predict_classes(input_fn=predict_input_fn)
565 def input_fn():
582 classifier.fit(input_fn=input_fn, steps=5)
589 classifier.export(export_dir, input_fn=default_input_fn)
610 input_fn=_input_fn_builder(train_features, train_labels), steps=50)
613 input_fn=_input_fn_builder(test_features, None))
630 input_fn = test_data.iris_input_logistic_fn
631 regressor.fit(input_fn=input_fn, steps=200)
632 scores = regressor.evaluate(input_fn=input_fn, steps=1)
648 regressor.fit(input_fn=_input_fn, steps=200)
649 scores = regressor.evaluate(input_fn=_input_fn, steps=1)
684 regressor.fit(input_fn=_input_fn, steps=200)
686 scores = regressor.evaluate(input_fn=_input_fn, steps=1)
704 regressor.fit(input_fn=_input_fn_train, steps=5)
705 scores = regressor.evaluate(input_fn=_input_fn_train, steps=1)
733 regressor.fit(input_fn=_input_fn_train, steps=5)
734 scores = regressor.evaluate(input_fn=_input_fn_eval, steps=1)
763 regressor.fit(input_fn=_input_fn_train, steps=5)
764 scores = regressor.evaluate(input_fn=_input_fn_eval, steps=1)
787 regressor.fit(input_fn=_input_fn, steps=5)
789 input_fn=_input_fn,
804 list(regressor.predict_scores(input_fn=predict_input_fn)))
812 input_fn=_input_fn,
843 regressor.fit(input_fn=_input_fn, steps=5)
845 predictions = list(regressor.predict_scores(input_fn=predict_input_fn))
850 predictions2 = list(regressor2.predict_scores(input_fn=predict_input_fn))
