def log_prob(self, xs, zs):
"""Return scalar, the log joint density log p(xs, zs)."""
# Note there are no parameters we're being Bayesian about. The
# parameters are baked into how we specify the neural networks.
X, y = xs['X'], xs['y']
self.neural_network(X)
result = self.pi * norm.prob(y, self.mus, self.sigmas)
result = tf.log(tf.reduce_sum(result, 1))
return tf.reduce_sum(result)
def log_prob(self, xs, zs): """Return scalar, the log joint density log p(xs, zs).""" # Note there are no parameters we're being Bayesian about. The # parameters are baked into how we specify the neural networks. X, y = xs['X'], xs['y'] self.neural_network(X) result = self.pi * norm.prob(y, self.mus, self.sigmas) result = tf.log(tf.reduce_sum(result, 1)) return tf.reduce_sum(result)
def log_prob(self, xs, zs):
"""log p((xs,ys), (z,theta)) = sum_{n=1}^N log p((xs[n,:],ys[n]), theta)"""
# Note there are no parameters we're being Bayesian about. The
# parameters are baked into how we specify the neural networks.
X, y = xs['X'], xs['y']
self.neural_network(X)
result = self.pi * norm.prob(y, self.mus, self.sigmas)
result = tf.log(tf.reduce_sum(result, 1))
return tf.reduce_sum(result)
def log_prob(self, xs, zs):
"""log p((xs,ys), (z,theta)) = sum_{n=1}^N log p((xs[n,:],ys[n]), theta)"""
# Note there are no parameters we're being Bayesian about. The
# parameters are baked into how we specify the neural networks.
X, y = xs['X'], xs['y']
self.neural_network(X)
result = self.pi * norm.prob(y, self.mus, self.sigmas)
result = tf.log(tf.reduce_sum(result, 1))
return tf.reduce_sum(result)
