def numerical_gradient(self, x, t):
loss_W = lambda W: self.loss(x, t)
grads = {}
for idx in range(1, self.hidden_layer_num + 2):
grads['W' + str(idx)] = numerical_gradient(
loss_W, self.params['W' + str(idx)])
grads['b' + str(idx)] = numerical_gradient(
loss_W, self.params['b' + str(idx)])
return grads
def numerical_gradient(self, x, t):
loss_W = lambda W: self.loss(x, t)
grads = {}
grads['W1'] = numerical_gradient(loss_W, self.params['W1'])
grads['b1'] = numerical_gradient(loss_W, self.params['b1'])
grads['W2'] = numerical_gradient(loss_W, self.params['W2'])
grads['b2'] = numerical_gradient(loss_W, self.params['b2'])
return grads
def numerical_gradient(self, X, T):
loss_W = lambda W: self.loss(X, T, train_flg=True)
grads = {}
for idx in range(1, self.hidden_layer_num + 2):
grads['W' + str(idx)] = numerical_gradient(
loss_W, self.params['W' + str(idx)])
grads['b' + str(idx)] = numerical_gradient(
loss_W, self.params['b' + str(idx)])
if self.use_batchnorm and idx != self.hidden_layer_num + 1:
grads['gamma' + str(idx)] = numerical_gradient(
loss_W, self.params['gamma' + str(idx)])
grads['beta' + str(idx)] = numerical_gradient(
loss_W, self.params['beta' + str(idx)])
return grads
class simpleNet:
def __init__(self):
self.W = np.random.randn(2, 3)
def predict(self, x):
return np.dot(x, self.W)
def loss(self, x, t):
z = self.predict(x)
y = softmax(z)
loss = cross_entropy_error(y, t)
return loss
net = simpleNet()
print(net.W)
x = np.array([0.6, 0.9])
p = net.predict(x)
print(p)
print(np.argmax(p))
t = np.array([0, 0, 1])
print(net.loss(x, t))
f = lambda w: net.loss(x, t)
dW = numerical_gradient(f, net.W)
print(dW)