class Param:
'''
Copied and modified from GPflow(https://github.com/GPflow/)
'''
def __init__(self,
value,
transform=None,
fixed=False,
name=None,
learning_rate=None,
summ=False):
self.value = value
self.fixed = fixed
if name is None:
self.name = "param"
else:
self.name = name
if transform is None:
self.transform = transforms.Identity()
else:
self.transform = transform
if self.fixed:
self.tf_opt_var = tf.constant(self.value,
name=name,
dtype=float_type)
else:
self.tf_opt_var = Variable(self.transform.backward(self.value),
name=name,
dtype=float_type)
if learning_rate is not None and fixed is False:
self.tf_opt_var.set_learning_rate(learning_rate)
if summ:
self.variable_summaries(self.tf_opt_var)
def get_optv(self):
return self.tf_opt_var
def get_tfv(self):
if self.fixed:
return self.tf_opt_var
else:
return self.transform.tf_forward(self.tf_opt_var)
def variable_summaries(self, var):
tf.summary.histogram(self.name, var)
@property
def shape(self):
return self.value.shape
class Param:
'''
Inheriting from GPFlow
TODO : add a fixed flag in which case this should return tf.tensor instead of tf.Variable
'''
def __init__(self,value,transform = None,fixed=False,name=None,learning_rate=None,summ=False):
self.value = value
self.fixed = fixed
if name is None:
self.name = "param"
else:
self.name = name
if transform is None:
self.transform=transforms.Identity()
else:
self.transform = transform
if self.fixed:
self.tf_opt_var = tf.constant(self.value,name=name,dtype=float_type)
else:
self.tf_opt_var = Variable(self.transform.backward(self.value),name=name,dtype=float_type)
if learning_rate is not None:
self.tf_opt_var.set_learning_rate(learning_rate)
if summ:
self.variable_summaries(self.tf_opt_var)
def get_optv(self):
return self.tf_opt_var
def get_tfv(self):
if self.fixed:
return self.tf_opt_var
else:
return self.transform.tf_forward(self.tf_opt_var)
def variable_summaries(self,var):
"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
mean = tf.reduce_mean(var)
tf.summary.scalar(self.name, mean)
tf.summary.histogram(self.name, var)
@property
def shape(self):
return self.value.shape
