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Pytorch 分类二维矩阵图像和一维向量的方法及常用函数总结

已有 3498 次阅读 2020-10-23 14:42 |个人分类:Pytorch|系统分类:科研笔记

(一)一维、二维常使用模块方法

二维学习中,主要使用的是:

self.conv1 = nn.Conv2d(in_channels=1, out_channels=5, kernel_size=7, stride=2, padding=1)

self.fc1 = nn.Linear(2432,512)

F.max_pool2d(self.conv1(x), 2)

 

一维

self.conv1 = nn.Conv1d(in_channels=1, out_channels=5, kernel_size=7, stride=2, padding=1)

self.fc1 = nn.Linear(2432,512)

F.max_pool1d(self.conv1(x), 2)

(二)激活函数包括多种,用于处理输出形式,规范等

torch.nn.functional.threshold(input, threshold, value, inplace=False)

torch.nn.functional.relu(input, inplace=False)

torch.nn.functional.hardtanh(input, min_val=-1.0, max_val=1.0, inplace=False)

torch.nn.functional.relu6(input, inplace=False)

torch.nn.functional.elu(input, alpha=1.0, inplace=False)

torch.nn.functional.leaky_relu(input, negative_slope=0.01, inplace=False)

torch.nn.functional.prelu(input, weight)

torch.nn.functional.rrelu(input, lower=0.125, upper=0.3333333333333333, training=False, inplace=False)

torch.nn.functional.logsigmoid(input)

torch.nn.functional.hardshrink(input, lambd=0.5)

torch.nn.functional.tanhshrink(input)

torch.nn.functional.softsign(input)

torch.nn.functional.softplus(input, beta=1, threshold=20)

torch.nn.functional.softmin(input)

torch.nn.functional.softmax(input)

torch.nn.functional.softshrink(input, lambd=0.5)

torch.nn.functional.log_softmax(input)

torch.nn.functional.tanh(input)

torch.nn.functional.sigmoid(input)

注意:relu什么的,实际上种类很多,还有各种改进,

https://www.jianshu.com/p/68bd249327ce,列举了几种样式

(三)优化器的话也是有上十种,基于基类 Optimizer

随机梯度下降算法 SGD算法 

torch.optim.SGD(params, lr=, momentum=0, dampening=0, weight_decay=0, nesterov=False)

 

平均随机梯度下降算法 ASGD算法

torch.optim.ASGD(params, lr=0.01, lambd=0.0001, alpha=0.75, t0=1000000.0, weight_decay=0)

 

AdaGrad算法

torch.optim.Adagrad(params, lr=0.01, lr_decay=0, weight_decay=0)

 

自适应学习率调整 Adadelta算法

torch.optim.Adadelta(params, lr=1.0, rho=0.9, eps=1e-06, weight_decay=0)

 

RMSprop算法

torch.optim.RMSprop(params, lr=0.01, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False)

 

自适应矩估计 Adam算法

torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)

 

 Adamax算法(Adamd的无穷范数变种)

torch.optim.Adamax(params, lr=0.002, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)

 

SparseAdam算法

torch.optim.SparseAdam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08)

 

L-BFGS算法

torch.optim.LBFGS(params, lr=1, max_iter=20, max_eval=None, tolerance_grad=1e-05, tolerance_change=1e-09, history_size=100, line_search_fn=None)

 

弹性反向传播算法 Rprop算法

torch.optim.Rprop(params, lr=0.01, etas=(0.5, 1.2), step_sizes=(1e-06, 50))

 

大佬的很详细

https://blog.csdn.net/shanglianlm/article/details/85019633

(四)池化方法也有好几种

# https://blog.csdn.net/HowardWood/article/details/79508805

torch.nn.functional.avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True)

torch.nn.functional.avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True)

torch.nn.functional.max_pool1d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)

torch.nn.functional.max_pool2d(input, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False)

torch.nn.functional.max_unpool1d(input, indices, kernel_size, stride=None, padding=0, output_size=None)

torch.nn.functional.max_unpool2d(input, indices, kernel_size, stride=None, padding=0, output_size=None)

torch.nn.functional.lp_pool2d(input, norm_type, kernel_size, stride=None, ceil_mode=False)

torch.nn.functional.adaptive_max_pool1d(input, output_size, return_indices=False)

torch.nn.functional.adaptive_max_pool2d(input, output_size, return_indices=False)

torch.nn.functional.adaptive_avg_pool1d(input, output_size)

torch.nn.functional.adaptive_avg_pool2d(input, output_size)

【参考】

神经网络 pytorch 分类二维矩阵图像和一维向量的方法

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