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  /external/tensorflow/tensorflow/contrib/layers/python/layers/
target_column_test.py 21 from tensorflow.contrib.layers.python.layers import target_column as target_column_lib
33 target_column = target_column_lib.regression_target()
38 5. / 3, sess.run(target_column.loss(prediction, labels, {})))
41 target_column = target_column_lib.regression_target(
49 sess.run(target_column.loss(prediction, labels, features)),
53 sess.run(target_column.training_loss(prediction, labels, features)),
60 target_column = target_column_lib.multi_class_target(n_classes=2)
68 sess.run(target_column.loss(logits, labels, {})),
72 target_column = target_column_lib.multi_class_target(
82 sess.run(target_column.loss(logits, labels, features))
    [all...]
__init__.py 33 from tensorflow.contrib.layers.python.layers.target_column import *
  /external/tensorflow/tensorflow/python/estimator/inputs/
pandas_io.py 46 target_column='target'):
63 target_column: str, name to give the target column `y`.
83 if target_column in x:
86 'column with that name: %s' % (target_column, x.columns))
90 x[target_column] = y
120 target = features.pop(target_column)
  /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/
dynamic_rnn_estimator.py 257 target_column,
274 target_column: An initialized `TargetColumn`, calculate predictions.
290 probabilities = target_column.logits_to_predictions(
296 predictions = target_column.logits_to_predictions(
303 activations, labels, sequence_length, target_column, features):
313 target_column: An initialized `TargetColumn`, calculate predictions.
322 return target_column.loss(activations_masked, labels_masked, features)
326 activations, labels, sequence_length, target_column, features):
336 target_column: An initialized `TargetColumn`, calculate predictions.
346 return target_column.loss(last_activations, labels, features
    [all...]
state_saving_rnn_estimator.py 88 activations, labels, sequence_length, target_column, features):
98 target_column: An initialized `TargetColumn`, calculate predictions.
107 return target_column.loss(activations_masked, labels_masked, features)
382 target_column,
402 target_column: An initialized `TargetColumn`, used to calculate prediction
492 num_label_columns=target_column.num_label_columns,
498 rnn_activations, target_column, problem_type, predict_probabilities)
501 target_column, features)
614 target_column = layers.regression_target()
619 target_column = layers.multi_class_target(n_classes=num_classes
    [all...]
rnn_common.py 243 def multi_value_predictions(activations, target_column, problem_type,
265 target_column: An initialized `TargetColumn`, calculate predictions.
280 flat_probabilities = target_column.logits_to_predictions(
283 if target_column.num_label_columns == 1:
294 flat_predictions = target_column.logits_to_predictions(
state_saving_rnn_estimator_test.py 27 from tensorflow.contrib.layers.python.layers import target_column as target_column_lib
338 target_column=target_column_lib.multi_class_target(n_classes=2),
dynamic_rnn_estimator_test.py 27 from tensorflow.contrib.layers.python.layers import target_column as target_column_lib
253 target_column=target_column_lib.multi_class_target(n_classes=2),
  /external/tensorflow/tensorflow/contrib/learn/python/learn/learn_io/
pandas_io.py 57 target_column='target'):
66 target_column=target_column)
  /external/tensorflow/tensorflow/contrib/learn/python/learn/datasets/
text_datasets.py 65 train_path, target_dtype=np.int32, features_dtype=np.str, target_column=0)
67 test_path, target_dtype=np.int32, features_dtype=np.str, target_column=0)
base.py 40 target_column=-1):
50 target[i] = np.asarray(row.pop(target_column), dtype=target_dtype)
59 target_column=-1):
65 target.append(row.pop(target_column))

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