def weighted_sum_from_feature_columns(columns_to_tensors,
feature_columns,
num_outputs,
weight_collections=None,
trainable=True,
scope=None):
"""A tf.contrib.layer style linear prediction builder based on FeatureColumns.
Generally a single example in training data is described with feature columns.
This function generates weighted sum for each num_outputs. Weighted sum refers
to logits in classification problems. It refers to prediction itself for
linear regression problems.
Example:
```
# Building model for training
feature_columns = (
real_valued_column("my_feature1"),
...
)
columns_to_tensor = tf.parse_example(...)
logits = weighted_sum_from_feature_columns(
columns_to_tensors=columns_to_tensor,
feature_columns=feature_columns,
num_outputs=1)
loss = tf.nn.sigmoid_cross_entropy_with_logits(logits, labels)
```
Args:
columns_to_tensors: A mapping from feature column to tensors. 'string' key
means a base feature (not-transformed). It can have FeatureColumn as a
key too. That means that FeatureColumn is already transformed by input
pipeline. For example, `inflow` may have handled transformations.
feature_columns: A set containing all the feature columns. All items in the
set should be instances of classes derived from FeatureColumn.
num_outputs: An integer specifying number of outputs. Default value is 1.
weight_collections: List of graph collections to which weights are added.
trainable: If `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
scope: Optional scope for variable_scope.
Returns:
A tuple containing:
* A Tensor which represents predictions of a linear model.
* A dictionary which maps feature_column to corresponding Variable.
* A Variable which is used for bias.
Raises:
ValueError: if FeatureColumn cannot be used for linear predictions.
"""
check_feature_columns(feature_columns)
with variable_scope.variable_scope(
scope,
default_name='weighted_sum_from_feature_columns',
values=columns_to_tensors.values()):
output_tensors = []
column_to_variable = dict()
transformer = _Transformer(columns_to_tensors)
# pylint: disable=protected-access
for column in sorted(set(feature_columns), key=lambda x: x.key):
transformed_tensor = transformer.transform(column)
try:
embedding_lookup_arguments = column._wide_embedding_lookup_arguments(
transformed_tensor)
variable, predictions = _create_embedding_lookup(
column,
columns_to_tensors,
embedding_lookup_arguments,
num_outputs,
trainable,
weight_collections)
except NotImplementedError:
with variable_scope.variable_scope(
None,
default_name=column.name,
values=columns_to_tensors.values()):
tensor = column._to_dense_tensor(transformed_tensor)
tensor = fc._reshape_real_valued_tensor(tensor, 2, column.name)
variable = [
contrib_variables.model_variable(
name='weight',
shape=[tensor.get_shape()[1], num_outputs],
initializer=init_ops.zeros_initializer(),
trainable=trainable,
collections=weight_collections)
]
predictions = math_ops.matmul(tensor, variable[0], name='matmul')
except ValueError as ee:
raise ValueError('Error creating weighted sum for column: {}.\n'
'{}'.format(column.name, ee))
output_tensors.append(predictions)
column_to_variable[column] = variable
_log_variable(variable)
_maybe_restore_from_checkpoint(column._checkpoint_path(), variable)
# pylint: enable=protected-access
predictions_no_bias = math_ops.add_n(output_tensors)
bias = contrib_variables.model_variable(
'bias_weight',
shape=[num_outputs],
initializer=init_ops.zeros_initializer(),
trainable=trainable,
collections=_add_variable_collection(weight_collections))
_log_variable(bias)
predictions = nn_ops.bias_add(predictions_no_bias, bias)
return predictions, column_to_variable, bias
def weighted_sum_from_feature_columns(columns_to_tensors,
feature_columns,
num_outputs,
weight_collections=None,
trainable=True,
scope=None):
"""A tf.contrib.layer style linear prediction builder based on FeatureColumns.
Generally a single example in training data is described with feature columns.
This function generates weighted sum for each num_outputs. Weighted sum refers
to logits in classification problems. It refers to prediction itself for
linear regression problems.
Example:
```
# Building model for training
feature_columns = (
real_valued_column("my_feature1"),
...
)
columns_to_tensor = tf.parse_example(...)
logits = weighted_sum_from_feature_columns(
columns_to_tensors=columns_to_tensor,
feature_columns=feature_columns,
num_outputs=1)
loss = tf.nn.sigmoid_cross_entropy_with_logits(logits, labels)
```
Args:
columns_to_tensors: A mapping from feature column to tensors. 'string' key
means a base feature (not-transformed). It can have FeatureColumn as a
key too. That means that FeatureColumn is already transformed by input
pipeline. For example, `inflow` may have handled transformations.
feature_columns: A set containing all the feature columns. All items in the
set should be instances of classes derived from FeatureColumn.
num_outputs: An integer specifying number of outputs. Default value is 1.
weight_collections: List of graph collections to which weights are added.
trainable: If `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
scope: Optional scope for variable_scope.
Returns:
A tuple containing:
* A Tensor which represents predictions of a linear model.
* A dictionary which maps feature_column to corresponding Variable.
* A Variable which is used for bias.
Raises:
ValueError: if FeatureColumn cannot be used for linear predictions.
"""
check_feature_columns(feature_columns)
with variable_scope.variable_scope(
scope,
default_name='weighted_sum_from_feature_columns',
values=columns_to_tensors.values()):
output_tensors = []
column_to_variable = dict()
transformer = _Transformer(columns_to_tensors)
# pylint: disable=protected-access
for column in sorted(set(feature_columns), key=lambda x: x.key):
transformed_tensor = transformer.transform(column)
try:
embedding_lookup_arguments = column._wide_embedding_lookup_arguments(
transformed_tensor)
variable, predictions = _create_embedding_lookup(
column,
columns_to_tensors,
embedding_lookup_arguments,
num_outputs,
trainable,
weight_collections)
except NotImplementedError:
with variable_scope.variable_scope(
None,
default_name=column.name,
values=columns_to_tensors.values()):
tensor = column._to_dense_tensor(transformed_tensor)
tensor = fc._reshape_real_valued_tensor(tensor, 2, column.name)
variable = [contrib_variables.model_variable(
name='weight',
shape=[tensor.get_shape()[1], num_outputs],
initializer=init_ops.zeros_initializer,
trainable=trainable,
collections=weight_collections)]
predictions = math_ops.matmul(tensor, variable[0], name='matmul')
except ValueError as ee:
raise ValueError('Error creating weighted sum for column: {}.\n'
'{}'.format(column.name, ee))
output_tensors.append(predictions)
column_to_variable[column] = variable
_log_variable(variable)
_maybe_restore_from_checkpoint(column._checkpoint_path(), variable)
# pylint: enable=protected-access
predictions_no_bias = math_ops.add_n(output_tensors)
bias = contrib_variables.model_variable(
'bias_weight',
shape=[num_outputs],
initializer=init_ops.zeros_initializer,
trainable=trainable,
collections=_add_variable_collection(weight_collections))
_log_variable(bias)
predictions = nn_ops.bias_add(predictions_no_bias, bias)
return predictions, column_to_variable, bias
