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 gradient_descent(f, init_x, lr=0.01, step_num=100):
x = init_x
print_step = step_num // 10
for i in range(step_num):
grad = numerical_gradient(f, x)
x -= lr * grad
if i % print_step == 0:
print("step {}: {}".format(i, x))
return x
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 numerical_gradient(self, x, t):
"""求梯度（数值微分）
Parameters
----------
x : 输入数据
t : 教师标签
Returns
-------
具有各层的梯度的字典变量
grads['W1']、grads['W2']、...是各层的权重
grads['b1']、grads['b2']、...是各层的偏置
"""
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
for idx in range(x.size):
tmp_val = x[idx]
# f(x+h)的计算
x[idx] = tmp_val + h
fxh1 = f(x)
# f(x-h)的计算
x[idx] = tmp_val - h
fxh2 = f(x)
grad[idx] = (fxh1 - fxh2) / (2 * h)
x[idx] = tmp_val # 还原值
return grad
#%%
print(numerical_gradient(function_2, np.array([3.0, 4.0])))
print(numerical_gradient(function_2, np.array([0.0, 2.0])))
print(numerical_gradient(function_2, np.array([3.0, 0.0])))
#%%
#numerical_gradient(function_2, np.array([3.0, 0.0]))
matrix = np.random.randint(1, 10, size=(3, 2, 4))
for index, m in enumerate(matrix):
print(index)
print(m.shape)
print(m)
print()
#%%
def gradient_descent(f, init_x, lr=0.01, step_num=100):
x = init_x