def predict(self, xReal, xInft):
'''X should be rank 2 tensor'''
yReal, yInft = td.add(
*td.tensordot(xReal, xInft, self.weight_, self.wInft_), self.bias_,
self.bInft_)
yInft = tf.transpose(yInft, [0, 2, 1])
return self.outputActivation_(yReal, yInft)
def partial_fit(self, xReal, xInft, yReal, yInft):
'''Perform single round of training without reset. Returns average loss and gradient.'''
pred = self.trainGrad_(xReal, xInft, yReal, yInft)
reg = self.regularizeGrad_()
loss = td.add(*pred, *reg)
self.weight_ = self.wUpdate_(
self.weight_, tf.reshape(loss[1][1:], self.weight_.shape))
self.bias_ = self.bUpdate_(self.bias_,
tf.reshape(loss[1][0:1], self.bias_.shape))
return loss
def jsDivergence( targetReal, targetInft, predReal, predInft ):
m = td.multiply( 0.5, 0.0, *td.add( targetReal, targetInft, predReal, predInft ) )
tpart = td.multiply( targetReal, targetInft, *td.log( *td.divide( targetReal, targetInft, *m ) ) )
ppart = td.multiply( predReal, predInft, *td.log( *td.divide( predReal, predInft, *m ) ) )
return td.multiply( 0.5, 0.0, *td.add( *tpart, *ppart ) )
def logistic(real, inft):
return td.divide(1.0, 0.0, *td.add(1.0, 0.0, *td.exp(-real, -inft)))
def softplus(real, inft):
return td.log(*td.add(*td.exp(real, inft), 1.0, 0.0))