def logistic_regression(X, y, class_weight=None, init_mean=None,
init_stddev=1.0):
"""Creates logistic regression TensorFlow subgraph.
Args:
X: tensor or placeholder for input features,
shape should be [batch_size, n_features].
y: tensor or placeholder for target,
shape should be [batch_size, n_classes].
class_weight: tensor, [n_classes], where for each class
it has weight of the class. If not provided
will check if graph contains tensor `class_weight:0`.
If that is not provided either all ones are used.
init_mean: the mean value to use for initialization.
init_stddev: the standard devation to use for initialization.
Returns:
Predictions and loss tensors.
Side effects:
The variables linear_regression.weights and linear_regression.bias are
initialized as follows. If init_mean is not None, then initialization
will be done using a random normal initializer with the given init_mean
and init_stddv. (These may be set to 0.0 each if a zero initialization
is desirable for convex use cases.) If init_mean is None, then the
uniform_unit_scaling_initialzer will be used.
"""
with vs.variable_scope('logistic_regression'):
logging_ops.histogram_summary('logistic_regression.X', X)
logging_ops.histogram_summary('logistic_regression.y', y)
# Set up the requested initialization.
if (init_mean is None):
weights = vs.get_variable('weights',
[X.get_shape()[1], y.get_shape()[-1]])
bias = vs.get_variable('bias',
[y.get_shape()[-1]])
else:
weights = vs.get_variable('weights',
[X.get_shape()[1], y.get_shape()[-1]],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
bias = vs.get_variable('bias',
[y.get_shape()[-1]],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
logging_ops.histogram_summary('logistic_regression.weights', weights)
logging_ops.histogram_summary('logistic_regression.bias', bias)
# If no class weight provided, try to retrieve one from pre-defined
# tensor name in the graph.
if not class_weight:
try:
class_weight = ops.get_default_graph().get_tensor_by_name('class_weight:0')
except KeyError:
pass
return softmax_classifier(X, y, weights, bias,
class_weight=class_weight)
def test_softmax_classifier(self):
with self.test_session() as session:
features = tf.placeholder(tf.float32, [None, 3])
labels = tf.placeholder(tf.float32, [None, 2])
weights = tf.constant([[0.1, 0.1], [0.1, 0.1], [0.1, 0.1]])
biases = tf.constant([0.2, 0.3])
class_weight = tf.constant([0.1, 0.9])
prediction, loss = ops.softmax_classifier(features, labels, weights, biases, class_weight)
self.assertEqual(prediction.get_shape()[1], 2)
self.assertEqual(loss.get_shape(), [])
value = session.run(loss, {features: [[0.2, 0.3, 0.2]], labels: [[0, 1]]})
self.assertAllClose(value, 0.55180627)
def logistic_regression(X,
y,
class_weight=None,
init_mean=None,
init_stddev=1.0):
"""Creates logistic regression TensorFlow subgraph.
Args:
X: tensor or placeholder for input features,
shape should be [batch_size, n_features].
y: tensor or placeholder for target,
shape should be [batch_size, n_classes].
class_weight: tensor, [n_classes], where for each class
it has weight of the class. If not provided
will check if graph contains tensor `class_weight:0`.
If that is not provided either all ones are used.
init_mean: the mean value to use for initialization.
init_stddev: the standard devation to use for initialization.
Returns:
Predictions and loss tensors.
Side effects:
The variables linear_regression.weights and linear_regression.bias are
initialized as follows. If init_mean is not None, then initialization
will be done using a random normal initializer with the given init_mean
and init_stddv. (These may be set to 0.0 each if a zero initialization
is desirable for convex use cases.) If init_mean is None, then the
uniform_unit_scaling_initialzer will be used.
"""
with vs.variable_scope('logistic_regression'):
logging_ops.histogram_summary('logistic_regression.X', X)
logging_ops.histogram_summary('logistic_regression.y', y)
# Set up the requested initialization.
if (init_mean is None):
weights = vs.get_variable(
'weights',
[X.get_shape()[1], y.get_shape()[-1]])
bias = vs.get_variable('bias', [y.get_shape()[-1]])
else:
weights = vs.get_variable(
'weights',
[X.get_shape()[1], y.get_shape()[-1]],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
bias = vs.get_variable(
'bias', [y.get_shape()[-1]],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev))
logging_ops.histogram_summary('logistic_regression.weights', weights)
logging_ops.histogram_summary('logistic_regression.bias', bias)
# If no class weight provided, try to retrieve one from pre-defined
# tensor name in the graph.
if not class_weight:
try:
class_weight = ops.get_default_graph().get_tensor_by_name(
'class_weight:0')
except KeyError:
pass
return softmax_classifier(X,
y,
weights,
bias,
class_weight=class_weight)