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Fix another bug in computing shape for SDCA fake bias column

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Change: 146266321
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David Soergel 2017-02-01 11:16:28 -08:00 committed by TensorFlower Gardener
parent 667bf9879f
commit 287e845c52
1 changed files with 15 additions and 6 deletions
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21
tensorflow/contrib/learn/python/learn/estimators/linear.py
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@ -78,12 +78,21 @@ def _add_bias_column(feature_columns, columns_to_tensors, bias_variable,
if not feature_columns:
raise ValueError("feature_columns can't be empty.")
# Using an arbitrary input tensor to figure out batch_size.
some_input = next(iter(columns_to_tensors.values()))
if isinstance(some_input, sparse_tensor.SparseTensor):
batch_size = tensor_util.constant_value(some_input.dense_shape)[0]
else:
batch_size = array_ops.shape(some_input)[0]
# Loop through input tensors until we can figure out batch_size.
batch_size = None
for column in columns_to_tensors.values():
if isinstance(column, tuple):
column = column[0]
if isinstance(column, sparse_tensor.SparseTensor):
shape = tensor_util.constant_value(column.dense_shape)
if shape is not None:
batch_size = shape[0]
break
else:
batch_size = array_ops.shape(column)[0]
break
if batch_size is None:
raise ValueError("Could not infer batch size from input features.")
bias_column = layers.real_valued_column(bias_column_name)
columns_to_tensors[bias_column] = array_ops.ones([batch_size, 1],