Lines Matching refs:features
78 self, mode, features, labels=None, train_op_fn=None, logits=None,
127 features = {
137 dnn_linear_combined._dnn_linear_combined_model_fn(features, labels,
163 features = {
178 features, labels, model_fn.ModeKeys.TRAIN, params)
315 features = {
324 return features, labels
380 features = {}
384 features.update({
392 features['dummy_sparse_column'] = sparse_tensor.SparseTensor(
399 return features, labels
429 features = {}
433 features.update({
441 features['dummy_sparse_column'] = sparse_tensor.SparseTensor(
447 return features, labels
474 features = {
482 return features, labels
549 features = {
564 return features, labels
602 features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),}
604 return features, labels
624 features = {
630 return features, labels
634 features = {
640 return features, labels
662 features = {
667 return features, labels
672 features = {
677 return features, labels
770 features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32)}
771 return features, labels
777 features = {'x': y}
778 return features
798 features = {
805 return features, labels
884 features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),}
885 return features, labels
928 features, targets = input_fn()
929 features[input_feature_key] = array_ops.placeholder(dtypes.string)
930 return features, targets
944 features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),}
945 return features, labels
969 features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),}
970 return features, labels
1173 features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),}
1174 return features, labels
1230 features = {'x': constant_op.constant([[100.], [3.], [2.], [2.]])}
1231 return features, labels
1249 features = {'x': array_ops.ones(shape=[4, 1], dtype=dtypes.float32),}
1250 return features, labels
1270 features = {
1275 return features, labels
1280 features = {
1285 return features, labels
1307 features = {
1312 return features, labels
1317 features = {
1322 return features, labels
1342 features = {
1353 return features, constant_op.constant(labels, dtype=dtypes.float32)
1381 features = {
1392 return features, constant_op.constant(labels, dtype=dtypes.float32)
1422 features = {
1429 return features, labels
1484 features = {
1491 return features, labels
1542 features = {
1553 return features, constant_op.constant(labels, dtype=dtypes.float32)
1575 features, targets = _input_fn()
1576 features[input_feature_key] = array_ops.placeholder(dtypes.string)
1577 return features, targets
1591 features = {
1597 return features, labels
1622 features = {
1633 return features, constant_op.constant([1., 0., 0.2], dtype=dtypes.float32)
1674 features = {
1685 return features, constant_op.constant([1., 0., 0.2], dtype=dtypes.float32)
1712 features = {
1723 return features, constant_op.constant([1., 0., 0.2], dtype=dtypes.float32)
1743 features = {
1754 return features, constant_op.constant([1., 0., 0.2], dtype=dtypes.float32)
1782 features = {'x': constant_op.constant([[100.], [3.], [2.], [2.]])}
1783 return features, labels
1785 def feature_engineering_fn(features, labels):
1786 _, _ = features, labels
1788 features = {'x': constant_op.constant([[1000.], [30.], [20.], [20.]])}
1789 return features, labels
