def fletcher_reeves(x_init, f, be, epsilon=1e-9):
x_new = be.zeros_like(x_init)
f_init = f(x_init)
f_new = f(x_new)
grad_f = Autodiff(f_init, be, next_error=None)
grad_f = grad_f.get_grad_op_tree([x_init])[0]
while True:
alpha, _ = None # implement the line search
def fletcher_reeves(x_init, f, be, epsilon=1e-9):
x_new = be.zeros_like(x_init)
f_init = f(x_init)
f_new = f(x_new)
grad_f = Autodiff(f_init, be, next_error=None)
grad_f = grad_f.get_grad_op_tree([x_init])[0]
while True:
alpha, _ = None # implement the line search
def gradient_descent(x_init, f, be, learning_rate=0.1, epsilon=1e-9):
x_new = be.zeros_like(x_init)
f_init = f(x_init)
f_new = f(x_new)
grad_f = Autodiff(f_init, be=be, next_error=None)
while True:
x_new[:] = x_init - learning_rate * grad_f.get_grad_tensor([x_init])[0]
if conv_vec_test(x_init, x_new, be) < epsilon:
# if conv_test(f_init, f_new, be) < epsilon:
f_val = be.empty((1, 1))
f_val[:] = f_new
return x_new, f_val
x_init[:] = x_new
def gradient_descent(x_init, f, be, learning_rate=0.1, epsilon=1e-9):
x_new = be.zeros_like(x_init)
f_init = f(x_init)
f_new = f(x_new)
grad_f = Autodiff(f_init, be=be, next_error=None)
while True:
x_new[:] = x_init - learning_rate * grad_f.get_grad_tensor([x_init])[0]
if conv_vec_test(x_init, x_new, be) < epsilon:
# if conv_test(f_init, f_new, be) < epsilon:
f_val = be.empty((1, 1))
f_val[:] = f_new
return x_new, f_val
x_init[:] = x_new
def newton_method(x_init, f, be, epsilon=1e-9):
x_new = be.zeros_like(x_init)
f_init = f(x_init)
f_new = f(x_new)
grad_f = Autodiff(f_init, be=be, next_error=None)
grad_f = grad_f.get_grad_op_tree([x_init])[0]
hessian_f = Autodiff(grad_f, be=be, next_error=None)
hessian_f = hessian_f.get_grad_op_tree([x_init])[0]
while True:
x_new[:] = x_init - grad_f / hessian_f
# if conv_vec_test(x_init, x_new, be) < epsilon:
if conv_test(f_init, f_new, be) < epsilon:
f_val = be.empty((1, 1))
f_val[:] = f_new
return x_new, f_val
x_init[:] = x_new
def bprop(self, error):
"""
Use Autodiff.back_prop_grad to back propagate gradients for the
corresponding tensors.
"""
if not self.deltas:
self.deltas = error.reshape((self.nfm, -1))
# autodiff will automatically cache and reuse the object
# if we know the `error` buffer at init, we can also create the autodiff
# object at layer's init
ad = Autodiff(self.fprop_op_tree, self.be, next_error=self.deltas)
# back propagate
ad.back_prop_grad([self.x, self.gamma, self.beta], [self.deltas, self.grad_gamma, self.grad_beta])
return error
def bprop(self, error):
"""
Use Autodiff.back_prop_grad to back propagate gradients for the
corresponding tensors.
"""
if not self.deltas:
self.deltas = error.reshape(self.bn_shape)
# autodiff will automatically cache and reuse the object
# if we know the `error` buffer at init, we can also create the autodiff
# object at layer's init
ad = Autodiff(self.fprop_op_tree, self.be, next_error=self.deltas)
# back propagate
ad.back_prop_grad([self.x, self.gamma, self.beta],
[self.deltas, self.grad_gamma, self.grad_beta])
return error
def newton_method(x_init, f, be, epsilon=1e-9):
x_new = be.zeros_like(x_init)
f_init = f(x_init)
f_new = f(x_new)
grad_f = Autodiff(f_init, be=be, next_error=None)
grad_f = grad_f.get_grad_op_tree([x_init])[0]
hessian_f = Autodiff(grad_f, be=be, next_error=None)
hessian_f = hessian_f.get_grad_op_tree([x_init])[0]
while True:
x_new[:] = x_init - grad_f / hessian_f
# if conv_vec_test(x_init, x_new, be) < epsilon:
if conv_test(f_init, f_new, be) < epsilon:
f_val = be.empty((1, 1))
f_val[:] = f_new
return x_new, f_val
x_init[:] = x_new