adam优化器怎么返回优化后的参数
时间: 2024-01-25 19:12:38 浏览: 31
Adam优化器在每次迭代后会返回优化后的参数。以下是Python实现Adam优化器的示例代码:
```python
import numpy as np
def adam_optimizer(parameters, gradients, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-8):
"""
Adam优化器的实现
:param parameters: 待优化的参数
:param gradients: 参数的梯度
:param learning_rate: 学习率
:param beta1: 一阶矩估计的指数衰减率,默认为0.9
:param beta2: 二阶矩估计的指数衰减率,默认为0.999
:param epsilon: 为了数值稳定性而添加的小值,默认为1e-8
:return: 优化后的参数
"""
# 初始化一阶矩估计和二阶矩估计
m = {}
v = {}
t = 0
for param_name, param in parameters.items():
m[param_name] = np.zeros_like(param)
v[param_name] = np.zeros_like(param)
# 更新参数
for param_name, gradient in gradients.items():
t += 1
m[param_name] = beta1 * m[param_name] + (1 - beta1) * gradient
v[param_name] = beta2 * v[param_name] + (1 - beta2) * (gradient ** 2)
m_hat = m[param_name] / (1 - beta1 ** t)
v_hat = v[param_name] / (1 - beta2 ** t)
parameters[param_name] -= learning_rate * m_hat / (np.sqrt(v_hat) + epsilon)
return parameters
```
使用上述代码,你可以将待优化的参数、参数的梯度、学习率等作为输入,调用`adam_optimizer`函数即可得到优化后的参数。