def bprop(self):
ca.multiply(self.mu.out, self.out_grad, self.mu.out_grad)
self.mu.out_grad *= self.out_grad
ca.exp(self.log_sigma.out, out=self.log_sigma.out_grad)
self.log_sigma.out_grad -= 1
self.log_sigma.out_grad *= 0.5
self.log_sigma.out_grad *= self.out_grad
def bprop(self):
grad = ca.reshape(self.grad_array, self.bcast_shape)
ca.multiply(self.mu.array, grad, self.mu.grad_array)
ca.exp(self.logvar.array, out=self.logvar.grad_array)
self.logvar.grad_array -= 1
self.logvar.grad_array *= 0.5
self.logvar.grad_array *= grad
def bprop(self):
grad = ca.reshape(self.grad_array, self.bcast_shape)
ca.multiply(self.mu.array, grad, self.mu.grad_array)
ca.exp(self.logvar.array, out=self.logvar.grad_array)
self.logvar.grad_array -= 1
self.logvar.grad_array *= 0.5
self.logvar.grad_array *= grad
def fprop(self):
# e_i = exp(x_i - max(x))
# y = e_i / sum(e)
tmp1 = ca.amax(self.x.array, axis=1, keepdims=True)
ca.subtract(self.x.array, tmp1, self.array)
ca.exp(self.array, self.array)
ca.sum(self.array, axis=1, keepdims=True, out=tmp1)
self.array /= tmp1
def fprop(self):
tmp1 = self.mu.out**2
ca.negative(tmp1, tmp1)
tmp1 += self.log_sigma.out
tmp1 += 1
tmp1 -= ca.exp(self.log_sigma.out)
ca.sum(tmp1, axis=1, keepdims=True, out=self.out)
self.out *= -0.5
def fprop(self):
tmp1 = self.mu.out**2
ca.negative(tmp1, tmp1)
tmp1 += self.log_sigma.out
tmp1 += 1
tmp1 -= ca.exp(self.log_sigma.out)
self.out = ca.sum(tmp1)
self.out *= -0.5
def fprop(self):
tmp1 = self.mu.array**2
ca.negative(tmp1, tmp1)
tmp1 += self.logvar.array
tmp1 += 1
tmp1 -= ca.exp(self.logvar.array)
ca.sum(tmp1, axis=self.axis, out=self.array)
self.array *= -0.5
def fprop(self):
tmp1 = self.mu.array**2
ca.negative(tmp1, tmp1)
tmp1 += self.log_sigma.array
tmp1 += 1
tmp1 -= ca.exp(self.log_sigma.array)
self.array = ca.sum(tmp1)
self.array *= -0.5
def fprop(self):
tmp1 = self.mu.array**2
ca.negative(tmp1, tmp1)
tmp1 += self.logvar.array
tmp1 += 1
tmp1 -= ca.exp(self.logvar.array)
ca.sum(tmp1, axis=self.axis, out=self.array)
self.array *= -0.5
def fprop(self):
tmp1 = self.mu.out**2
ca.negative(tmp1, tmp1)
tmp1 += self.log_sigma.out
tmp1 += 1
tmp1 -= ca.exp(self.log_sigma.out)
self.out = ca.sum(tmp1)
self.out *= -0.5
def fprop(self):
tmp1 = self.mu.array**2
ca.negative(tmp1, tmp1)
tmp1 += self.log_sigma.array
tmp1 += 1
tmp1 -= ca.exp(self.log_sigma.array)
self.array = ca.sum(tmp1)
self.array *= -0.5
def bprop(self):
ca.negative(self.x.out, self.x.out_grad)
ca.exp(self.x.out_grad, self.x.out_grad)
self.x.out_grad += 1
ca.divide(1.0, self.x.out_grad, out=self.x.out_grad)
self.x.out_grad *= self.out_grad
def fprop(self):
ca.exp(self.x.out, self.out)
# TODO: use log1p()
self.out += 1
ca.log(self.out, self.out)
def bprop(self):
ca.multiply(self.mu.array, self.grad_array, self.mu.grad_array)
ca.exp(self.log_sigma.array, out=self.log_sigma.grad_array)
self.log_sigma.grad_array -= 1
self.log_sigma.grad_array *= 0.5
self.log_sigma.grad_array *= self.grad_array
def fprop(self, x):
self._tmp_x = x
return ca.log(1.0 + ca.exp(x))
def fprop(self):
ca.exp(self.x.out, out=self.out)
def softmax(x):
e = ca.exp(x - ca.amax(x, axis=1, keepdims=True))
return e/ca.sum(e, axis=1, keepdims=True)
def bprop(self):
ca.exp(self.x.out, out=self.x.out_grad)
self.x.out_grad *= self.out_grad
def bprop(self, y_grad):
return 1.0 / (1.0 + ca.exp(-self._tmp_x)) * y_grad
def fprop(self, x):
self._tmp_x = x
return ca.log(1.0 + ca.exp(x))
def bprop(self, y_grad):
return 1.0/(1.0 + ca.exp(-self._tmp_x)) * y_grad
def fprop(self):
ca.exp(self.x.out, out=self.out)
def bprop(self):
ca.multiply(self.mu.array, self.grad_array, self.mu.grad_array)
ca.exp(self.log_sigma.array, out=self.log_sigma.grad_array)
self.log_sigma.grad_array -= 1
self.log_sigma.grad_array *= 0.5
self.log_sigma.grad_array *= self.grad_array
def fprop(self):
ca.exp(self.x.array, out=self.array)
def bprop(self):
ca.exp(self.x.out, out=self.x.out_grad)
self.x.out_grad *= self.out_grad
def bprop(self):
ca.exp(self.x.array, out=self.x.grad_array)
self.x.grad_array *= self.grad_array
def fprop(self):
ca.exp(self.x.array, out=self.array)
def softmax(x):
e = ca.exp(x - ca.amax(x, axis=1, keepdims=True))
return e / ca.sum(e, axis=1, keepdims=True)
def bprop(self):
ca.multiply(self.mu.out, self.out_grad, self.mu.out_grad)
ca.exp(self.log_sigma.out, out=self.log_sigma.out_grad)
self.log_sigma.out_grad -= 1
self.log_sigma.out_grad *= 0.5
self.log_sigma.out_grad *= self.out_grad
def bprop(self):
ca.exp(self.x.array, out=self.x.grad_array)
self.x.grad_array *= self.grad_array
