TAdam算法代码实现及调用
时间: 2023-10-23 07:08:28 浏览: 74
KNN算法的API调用实现
TAdam算法是Adam优化算法的一个改进版本,主要针对Adam算法在高维稀疏梯度下降优化中表现不佳的问题进行改进。
以下是TAdam算法的Python代码实现及调用示例:
```python
import numpy as np
# 定义TAdam优化器类
class TAdam:
def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, gamma=1e-3):
self.learning_rate = learning_rate
self.beta1 = beta1
self.beta2 = beta2
self.epsilon = epsilon
self.gamma = gamma
self.m = None
self.v = None
self.t = 0
# 定义优化器的优化方法
def optimize(self, gradient):
self.t += 1
if self.m is None:
self.m = np.zeros_like(gradient)
self.v = np.zeros_like(gradient)
self.m = self.beta1 * self.m + (1 - self.beta1) * gradient
self.v = self.beta2 * self.v + (1 - self.beta2) * np.square(gradient)
m_hat = self.m / (1 - self.beta1 ** self.t)
v_hat = self.v / (1 - self.beta2 ** self.t)
adaptive_lr = self.learning_rate * np.sqrt(1 - self.beta2 ** self.t) / (1 - self.beta1 ** self.t)
delta = -adaptive_lr * m_hat / (np.sqrt(v_hat) + self.epsilon)
return delta + self.gamma * gradient
# 实例化TAdam优化器
optimizer = TAdam()
# 在训练过程中调用TAdam优化器的optimize方法进行参数优化
for epoch in range(num_epochs):
for batch_x, batch_y in train_data:
gradient = compute_gradient(batch_x, batch_y)
delta = optimizer.optimize(gradient)
update_parameters(delta)
```
在训练过程中,只需要调用TAdam优化器的`optimize`方法即可进行参数优化。优化器的参数可以在实例化的时候传入,比如`learning_rate=0.001`、`beta1=0.9`、`beta2=0.999`、`epsilon=1e-8`、`gamma=1e-3`等。
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