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| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2016-12-12 13:57:21 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2016-12-12 14:07:41 -0800 |
| commit | a46b6d211eac423c72d3a57a177daf2f64db8642 (patch) | |
| tree | 8b9633ba87fdd4677994a0fec7c61a71862ca412 /tensorflow/contrib/linear_optimizer/python | |
| parent | 55735379ccda8a64e49717e95e9e0915e7b8dc8e (diff) | |
Major intent of this CL is to rename split_v -> split in the python API.
Requires:
1) Add name arguments to tf.split calls introduced since major Rosie CL cleaning
this up across the codebase.
2) Change uses of array_ops.split to use named arguments, which was not covered
in the Rosie CL.
3) Rename split_v calls to split.
Change: 141806936
Diffstat (limited to 'tensorflow/contrib/linear_optimizer/python')
| -rw-r--r-- | tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py | 35 |
1 files changed, 22 insertions, 13 deletions
diff --git a/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py b/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py index 644347f0b5..9edb00e7b0 100644 --- a/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py +++ b/tensorflow/contrib/linear_optimizer/python/sdca_optimizer.py @@ -89,11 +89,12 @@ class SDCAOptimizer(object): # very sparse features with weights and not weights. return SparseFeatureColumn( array_ops.reshape( - array_ops.split(1, 2, sparse_indices)[0], [-1]), + array_ops.split( + value=sparse_indices, num_or_size_splits=2, axis=1)[0], [-1]), array_ops.reshape( - array_ops.split(1, 2, sparse_indices)[1], [-1]), - array_ops.reshape( - math_ops.to_float(sparse_values), [-1])) + array_ops.split( + value=sparse_indices, num_or_size_splits=2, axis=1)[1], [-1]), + array_ops.reshape(math_ops.to_float(sparse_values), [-1])) def _training_examples_and_variables(): """Returns dictionaries for training examples and variables.""" @@ -135,19 +136,27 @@ class SDCAOptimizer(object): columns_to_variables[column][0]) elif isinstance(column, (layers.feature_column._CrossedColumn, layers.feature_column._SparseColumn)): - sparse_features.append(SparseFeatureColumn( - array_ops.reshape( - array_ops.split(1, 2, transformed_tensor.indices)[0], [-1]), - array_ops.reshape(transformed_tensor.values, [-1]), None)) + sparse_features.append( + SparseFeatureColumn( + array_ops.reshape( + array_ops.split( + value=transformed_tensor.indices, + num_or_size_splits=2, + axis=1)[0], [-1]), + array_ops.reshape(transformed_tensor.values, [-1]), + None)) sparse_feature_weights.append(columns_to_variables[column][0]) elif isinstance(column, layers.feature_column._WeightedSparseColumn): id_tensor = column.id_tensor(transformed_tensor) weight_tensor = column.weight_tensor(transformed_tensor) - sparse_feature_with_values.append(SparseFeatureColumn( - array_ops.reshape( - array_ops.split(1, 2, id_tensor.indices)[0], [-1]), - array_ops.reshape(id_tensor.values, [-1]), array_ops.reshape( - weight_tensor.values, [-1]))) + sparse_feature_with_values.append( + SparseFeatureColumn( + array_ops.reshape( + array_ops.split( + value=id_tensor.indices, num_or_size_splits=2, axis=1) + [0], [-1]), + array_ops.reshape(id_tensor.values, [-1]), + array_ops.reshape(weight_tensor.values, [-1]))) sparse_feature_with_values_weights.append( columns_to_variables[column][0]) else: |