def gradient_descent(f, init_x, lr=0.01, step_num=100):
x = init_x
for i in range(step_num):
grad = numerical_gradient(f, x)
x -= lr * grad
return x
def numerical_gradient(self, x, t):
"""重みパラメーターに対する勾配の算出
Args:
x (numpy.ndarray): ニューラルネットワークへの入力
t (numpy.ndarray): 正解のラベル
Returns:
dictionary: 勾配を格納した辞書
"""
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 gradient_descent(f, init_x, lr=0.01, step_num=100):
x = init_x
x_history = []
for i in range(step_num):
x_history.append(x.copy())
grad = numerical_gradient(f, x)
x -= lr * grad
return x, np.array(x_history)
def gradient_descent(f, init_x, lr=0.01, step_num=100):
x = init_x # 초깃값
x_history = []
for i in range(step_num):
x_history.append( x.copy() ) # x가 갱신되기 전 값을 저장
grad = numerical_gradient(f, x) # 함수의 기울기를 구함
x -= lr * grad # 기울기에 학습률을 곱한 값으로 x를 갱신
return x, np.array(x_history)
def gradient_descent(f, init_x, lr=0.01, step_num=100):
x = init_x
x_history = []
# numerical_gradient 这个函数会求f函数的梯度，用该梯度乘以学习率得到的值进行更新操作
for i in range(step_num):
x_history.append(x.copy())
grad = numerical_gradient(f, x)
x -= lr * grad
return x, np.array(x_history)
def gradient_descent(f, init_x, lr=0.01, step_num=100):
x = init_x
x_history = []
for i in range(step_num):
x_history.append( x.copy() )
grad = numerical_gradient(f, x)
x -= lr * grad
return x, np.array(x_history)
def gradient_descent(f, init_x, lr=0.01, step_num=100):
x = init_x
x_history = []
for i in range(step_num):
x_history.append(x.copy())
grad = numerical_gradient(f, x)
plt.quiver(x[0],
x[1],
-grad[0],
-grad[1],
angles="xy",
color="#666666")
x -= lr * grad
return x, np.array(x_history)
sys.path.append(os.pardir) # 親ディレクトリのファイルをインポートするための設定
import numpy as np
from 2_softmax import softmax
from cross_entropy_function import cross_entropy_error
from gradient_2d import numerical_gradient
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
x = np.array([0.6, 0.9])
t = np.array([0, 0, 1])
net = simpleNet()
f = lambda w: net.loss(x, t)
dW = numerical_gradient(f, net.W)
print(dW)
