wehub-resource-sync
91 lines
2.6 KiB
Python
Executable File
91 lines
2.6 KiB
Python
Executable File
from prml.nn.random.random import RandomVariable
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from prml.nn.tensor.parameter import Parameter
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class Network(object):
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"""
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a base class for network building
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Parameters
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----------
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kwargs : tensor_like
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parameters to be optimized
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Attributes
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----------
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parameter : dict
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dictionary of parameters to be optimized
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random_variable : dict
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dictionary of random varibles
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"""
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def __init__(self, **kwargs):
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self.random_variable = {}
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self.parameter = {}
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for key, value in kwargs.items():
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if isinstance(value, Parameter):
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self.parameter[key] = value
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else:
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try:
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value = Parameter(value)
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except TypeError:
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raise TypeError(f"invalid type argument: {type(value)}")
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self.parameter[key] = value
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object.__setattr__(self, key, value)
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def __setattr__(self, key, value):
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if isinstance(value, RandomVariable):
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self.random_variable[key] = value
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object.__setattr__(self, key, value)
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def clear(self):
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"""
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clear gradient and constructed bayesian network
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"""
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for p in self.parameter.values():
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p.cleargrad()
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self.random_variable = {}
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def log_pdf(self, coef=1.):
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"""
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compute logarithm of probabilty density function
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Parameters
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----------
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coef : float
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coefficient to balance likelihood and prior
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assuming mini-batch size / whole data size for mini-batch training
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Returns
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-------
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logp : tensor_like
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logarithm of probability density function
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"""
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logp = 0
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for rv in self.random_variable.values():
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if rv.observed:
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logp += rv.log_pdf().sum()
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else:
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logp += coef * rv.log_pdf().sum()
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return logp
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def elbo(self, coef=1.):
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"""
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compute evidence lower bound of this model
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ln p(output) >= elbo
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Parameters
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----------
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coef : float
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coefficient to balance likelihood and prior
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assuming mini-batch size / whole data size for mini-batch training
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Returns
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-------
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evidence : tensor_like
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evidence lower bound
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"""
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evidence = 0
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for rv in self.random_variable.values():
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if rv.observed:
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evidence += rv.log_pdf().sum()
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else:
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evidence += -coef * rv.KLqp().sum()
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return evidence
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