class Sampler():
def __init__(self, inputDim=1, outputDim=1, optimizer=Adam()):
self.inputDim = inputDim
self.outputDim = outputDim
self.mean = Dense(self.inputDim,
self.outputDim,
activation=Identity(),
optimizer=copy.copy(optimizer))
self.logVar = Dense(self.inputDim,
self.outputDim,
activation=Identity(),
optimizer=copy.copy(optimizer))
def feedforward(self, input):
self.latentMean = self.mean.feedforward(input)
self.latentLogVar = self.logVar.feedforward(input)
self.epsilon = np.random.standard_normal(size=(self.outputDim,
input.shape[1]))
self.sample = self.latentMean + np.exp(
self.latentLogVar / 2.) * self.epsilon
return self.sample
def backpropagate(self, lastGradient):
gradLogVar = {}
gradMean = {}
tmp = self.outputDim * lastGradient.shape[1]
# KL divergence gradients
gradLogVar["KL"] = (np.exp(self.latentLogVar) - 1) / (2 * tmp)
gradMean["KL"] = self.latentMean / tmp
# MSE gradients
gradLogVar["MSE"] = 0.5 * lastGradient * self.epsilon * np.exp(
self.latentLogVar / 2.)
gradMean["MSE"] = lastGradient
# backpropagate gradients thorugh self.mean and self.logVar
return self.mean.backward(gradMean["KL"] +
gradMean["MSE"]) + self.logVar.backward(
gradLogVar["KL"] + gradLogVar["MSE"])
def getKLDivergence(self, output):
# output.shape[1] == batchSize
return -np.sum(1 + self.latentLogVar - np.square(self.latentMean) -
np.exp(self.latentLogVar)) / (2 * self.outputDim *
output.shape[1])
