def _linear(units, features, feature_columns, sparse_combiner='sum'):
linear_model = feature_column._LinearModel( # pylint: disable=protected-access
feature_columns=feature_columns,
units=units,
sparse_combiner=sparse_combiner,
name='linear_model')
output = linear_model(features)
return output
def linear_logit_fn(features):
"""Linear model logit_fn.
Args:
features: This is the first item returned from the `input_fn`
passed to `train`, `evaluate`, and `predict`. This should be a
single `Tensor` or `dict` of same.
Returns:
A `Tensor` representing the logits.
"""
if feature_column_v2.is_feature_column_v2(feature_columns):
shared_state_manager = feature_column_v2.SharedEmbeddingStateManager(
)
linear_model = feature_column_v2.LinearModel(
feature_columns=feature_columns,
units=units,
sparse_combiner=sparse_combiner,
shared_state_manager=shared_state_manager)
logits = linear_model(features)
bias = linear_model.bias_variable
# We'd like to get all the non-bias variables associated with this
# LinearModel. This includes the shared embedding variables as well.
variables = linear_model.variables
variables.remove(bias)
variables.extend(shared_state_manager.variables)
# Expand (potential) Partitioned variables
bias = _get_expanded_variable_list([bias])
variables = _get_expanded_variable_list(variables)
else:
linear_model = feature_column._LinearModel( # pylint: disable=protected-access
feature_columns=feature_columns,
units=units,
sparse_combiner=sparse_combiner,
name='linear_model')
logits = linear_model(features)
cols_to_vars = linear_model.cols_to_vars()
bias = cols_to_vars.pop('bias')
variables = cols_to_vars.values()
if units > 1:
summary.histogram('bias', bias)
else:
# If units == 1, the bias value is a length-1 list of a scalar Tensor,
# so we should provide a scalar summary.
summary.scalar('bias', bias[0][0])
summary.scalar('fraction_of_zero_weights',
_compute_fraction_of_zero(variables))
return logits
def linear_logit_fn(features):
"""Linear model logit_fn.
Args:
features: This is the first item returned from the `input_fn`
passed to `train`, `evaluate`, and `predict`. This should be a
single `Tensor` or `dict` of same.
Returns:
A `Tensor` representing the logits.
"""
if feature_column_v2.is_feature_column_v2(feature_columns):
shared_state_manager = feature_column_v2.SharedEmbeddingStateManager()
linear_model = feature_column_v2.LinearModel(
feature_columns=feature_columns,
units=units,
sparse_combiner=sparse_combiner,
shared_state_manager=shared_state_manager)
logits = linear_model(features)
bias = linear_model.bias_variable
# We'd like to get all the non-bias variables associated with this
# LinearModel. This includes the shared embedding variables as well.
variables = linear_model.variables
variables.remove(bias)
variables.extend(shared_state_manager.variables)
# Expand (potential) Partitioned variables
bias = _get_expanded_variable_list([bias])
variables = _get_expanded_variable_list(variables)
else:
linear_model = feature_column._LinearModel( # pylint: disable=protected-access
feature_columns=feature_columns,
units=units,
sparse_combiner=sparse_combiner,
name='linear_model')
logits = linear_model(features)
cols_to_vars = linear_model.cols_to_vars()
bias = cols_to_vars.pop('bias')
variables = cols_to_vars.values()
if units > 1:
summary.histogram('bias', bias)
else:
# If units == 1, the bias value is a length-1 list of a scalar Tensor,
# so we should provide a scalar summary.
summary.scalar('bias', bias[0][0])
summary.scalar('fraction_of_zero_weights',
_compute_fraction_of_zero(variables))
return logits
def _get_keras_linear_model_predictions(self,
features,
feature_columns,
units=1,
sparse_combiner='sum',
weight_collections=None,
trainable=True,
cols_to_vars=None):
keras_linear_model = _LinearModel(feature_columns,
units,
sparse_combiner,
weight_collections,
trainable,
name='linear_model')
retval = keras_linear_model(features) # pylint: disable=not-callable
if cols_to_vars is not None:
cols_to_vars.update(keras_linear_model.cols_to_vars())
return retval
def _get_keras_linear_model_predictions(
self,
features,
feature_columns,
units=1,
sparse_combiner='sum',
weight_collections=None,
trainable=True,
cols_to_vars=None):
keras_linear_model = _LinearModel(
feature_columns,
units,
sparse_combiner,
weight_collections,
trainable,
name='linear_model')
retval = keras_linear_model(features) # pylint: disable=not-callable
if cols_to_vars is not None:
cols_to_vars.update(keras_linear_model.cols_to_vars())
return retval