宁波市有哪些网站建设公司,聊城网站优化信息,软文范文200字,锦州网站做优化简单用用#xff0c;优化器具体参考
深度学习中的优化器原理(SGD,SGDMomentum,Adagrad,RMSProp,Adam)_哔哩哔哩_bilibili
收藏版#xff5c;史上最全机器学习优化器Optimizer汇总 - 知乎 (zhihu.com)
import numpy as np
import matplotlib.pyplot as plt
import torch
# … 简单用用优化器具体参考
深度学习中的优化器原理(SGD,SGDMomentum,Adagrad,RMSProp,Adam)_哔哩哔哩_bilibili
收藏版史上最全机器学习优化器Optimizer汇总 - 知乎 (zhihu.com)
import numpy as np
import matplotlib.pyplot as plt
import torch
# prepare dataset
# x,y是矩阵3行1列 也就是说总共有3个数据每个数据只有1个特征
x_data torch.tensor([[1.0], [2.0], [3.0]])y_data torch.tensor([[2.0], [4.0], [6.0]])loss_SGD []
loss_Adagrad []
loss_Adam []
loss_Adamax []
loss_ASGD []
loss_LBFGS []
loss_RMSprop []
loss_Rprop []class LinearModel(torch.nn.Module):def __init__(self):super().__init__()self.Linear torch.nn.Linear(1,1)def forward(self,x):y_pred self.Linear(x)return y_predmodel LinearModel()criterion torch.nn.MSELoss(reductionsum)
optimizer_SGD torch.optim.SGD(model.parameters(),lr0.01)
optimizer_Adagrad torch.optim.SGD(model.parameters(),lr0.01)
optimizer_Adam torch.optim.SGD(model.parameters(),lr0.01)
optimizer_Adamax torch.optim.SGD(model.parameters(),lr0.01)
optimizer_ASGD torch.optim.SGD(model.parameters(),lr0.01)
optimizer_LBFGS torch.optim.SGD(model.parameters(),lr0.01)
optimizer_RMSprop torch.optim.SGD(model.parameters(),lr0.01)
optimizer_Rprop torch.optim.SGD(model.parameters(),lr0.01)epoch_list []# optimizer_SGD
for epoch in range(100):y_pred model(x_data)loss criterion(y_pred,y_data)epoch_list.append(epoch)loss_SGD.append(loss.data)optimizer_SGD.zero_grad()loss.backward()optimizer_SGD.step()# optimizer_Adagrad
for epoch in range(100):y_pred model(x_data)loss criterion(y_pred,y_data)loss_Adagrad.append(loss.data)optimizer_Adagrad.zero_grad()loss.backward()optimizer_Adagrad.step()# optimizer_Adam
for epoch in range(100):y_pred model(x_data)loss criterion(y_pred,y_data)loss_Adam.append(loss.data)optimizer_Adam.zero_grad()loss.backward()optimizer_Adam.step()# optimizer_Adamax
for epoch in range(100):y_pred model(x_data)loss criterion(y_pred,y_data)loss_Adamax.append(loss.data)optimizer_Adamax.zero_grad()loss.backward()optimizer_Adamax.step()# optimizer_ASGD
for epoch in range(100):y_pred model(x_data)loss criterion(y_pred,y_data)loss_ASGD.append(loss.data)optimizer_ASGD.zero_grad()loss.backward()optimizer_ASGD.step()# optimizer_LBFGS
for epoch in range(100):y_pred model(x_data)loss criterion(y_pred,y_data)loss_LBFGS.append(loss.data)optimizer_LBFGS.zero_grad()loss.backward()optimizer_LBFGS.step()# optimizer_RMSprop
for epoch in range(100):y_pred model(x_data)loss criterion(y_pred,y_data)loss_RMSprop.append(loss.data)optimizer_RMSprop.zero_grad()loss.backward()optimizer_RMSprop.step()# optimizer_Rprop
for epoch in range(100):y_pred model(x_data)loss criterion(y_pred,y_data)loss_Rprop.append(loss.data)optimizer_Rprop.zero_grad()loss.backward()optimizer_Rprop.step()x_test torch.tensor([4.0])
y_test model(x_test)print(y_pred , y_test.data)plt.subplot(241)
plt.title(SGD)
plt.plot(epoch_list,loss_SGD)
plt.ylabel(cost)
plt.xlabel(epoch)plt.subplot(242)
plt.title(Adagrad)
plt.plot(epoch_list,loss_Adagrad)
plt.ylabel(cost)
plt.xlabel(epoch)plt.subplot(243)
plt.title(Adam)
plt.plot(epoch_list,loss_Adam)
plt.ylabel(cost)
plt.xlabel(epoch)plt.subplot(244)
plt.title(Adamax)
plt.plot(epoch_list,loss_Adamax)
plt.ylabel(cost)
plt.xlabel(epoch)plt.subplot(245)
plt.title(ASGD)
plt.plot(epoch_list,loss_ASGD)
plt.ylabel(cost)
plt.xlabel(epoch)plt.subplot(246)
plt.title(LBFGS)
plt.plot(epoch_list,loss_LBFGS)
plt.ylabel(cost)
plt.xlabel(epoch)plt.subplot(247)
plt.title(RMSprop)
plt.plot(epoch_list,loss_RMSprop)
plt.ylabel(cost)
plt.xlabel(epoch)plt.subplot(248)
plt.title(Rprop)
plt.plot(epoch_list,loss_Rprop)
plt.ylabel(cost)
plt.xlabel(epoch)
plt.show()
运行结果
