import numpy as np
from chainer0 import Function, Variable
from chainer0.functions.basic_math import Add, Mul
x = Variable(np.array([2.0]))
y = x**2 + x + 1.0
y.backward(enable_double_backprop=True)
dx = x.grad_var
print('y', y.data)
print('dx', x.grad)
assert y.data == 7.
assert x.grad == 5.
x.cleargrad()
dx.backward()
print('ddx', x.grad)
assert x.grad == 2.
dx = x.grad_var
x.cleargrad()
dx.backward()
print('dddx', x.grad)
assert x.grad == 0.
[0.74, -2.49, 1.39]]))
#targets = Variable(np.array([[True], [True], [False], [True]]))
#weights = Variable(np.array([[0.0], [0.0], [0.0]]))
targets = Variable(np.array([True, True, False, True]))
weights = Variable(np.array([0.0, 0.0, 0.0]))
# Define a function that returns gradients of training loss using Autograd.
print("Initial loss:", training_loss(weights).data)
assert training_loss(weights).data == 2.772588722239781
for i in range(100):
weights.cleargrad()
loss = training_loss(weights)
loss.backward()
#weights -= weights.grad * 0.01
weights.data -= weights.grad * 0.01
print("Trained loss:", training_loss(weights).data)
assert training_loss(weights).data == 0.38900754315581143
'''
training_gradient_fun = grad(training_loss)
# Optimize weights using gradient descent.
print("Initial loss:", training_loss(weights))
for i in range(100):