def linear_regression(x, y, init_mean=None, init_stddev=1.0):
"""Creates linear regression TensorFlow subgraph.
Args:
x: tensor or placeholder for input features.
y: tensor or placeholder for labels.
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('linear_regression'):
scope_name = vs.get_variable_scope().name
summary.histogram('%s.x' % scope_name, x)
summary.histogram('%s.y' % scope_name, y)
dtype = x.dtype.base_dtype
y_shape = y.get_shape()
if len(y_shape) == 1:
output_shape = 1
else:
output_shape = y_shape[1]
# Set up the requested initialization.
if init_mean is None:
weights = vs.get_variable(
'weights', [x.get_shape()[1], output_shape], dtype=dtype)
bias = vs.get_variable('bias', [output_shape], dtype=dtype)
else:
weights = vs.get_variable('weights', [x.get_shape()[1], output_shape],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev, dtype=dtype),
dtype=dtype)
bias = vs.get_variable('bias', [output_shape],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev, dtype=dtype),
dtype=dtype)
summary.histogram('%s.weights' % scope_name, weights)
summary.histogram('%s.bias' % scope_name, bias)
return losses_ops.mean_squared_error_regressor(x, y, weights, bias)
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