from torch.nn import ReLU, Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
input = torch.tensor([[1,-0.5],
[-1,3]])
input = torch.reshape(input,(-1, 1, 2, 2))
print(input.shape)
dataset = torchvision.datasets.CIFAR10("../datas", train = False, download=True,
transform=torchvision.transforms.ToTensor())
dataloader = DataLoader(dataset, batch_size=64)
class SUN(nn.Module):
def __init__(self):
super(SUN, self).__init__()
self.relu1 = ReLU() # 添加对应的网络
self.sigmoid = Sigmoid()
def forward (self, input):
output = www.laipuhuo.com.=self.sigmoid(input) # 使用了Sigmoid函数
return output
sun = SUN()
step = 0
write = SummaryWriter("../logs_relu")
for data in dataloader:
imgs,targets = data
write.add_image("input", imgs, global_step=step)
output = sun(imgs)
write.add_image("output",output,global_step=step)
class Solution {
public:
int numDistinct(string s, string t) {
vector<vector<uint64_t>> dp(s.size() 1,vector<uint64_t>(t.size() 1, 0));
// dp[i][j]表示以i - 1结尾的s里 有多少个 以j - 1为结尾的t
for(int i = 0; i www.laipuhuo.com.<= s.size(); i ){
dp[i][0] = 1;
}
for(int i = 1; i <= s.size(); i ){
for(int j = 1; j <= t.size(); j ){
if(s[i - 1] == t[j - 1]){
dp[i][j] = dp[i - 1][j - 1] dp[i - 1][j];
}else{
dp[i][j] = dp[i - 1][j];
}
}
}
return dp[s.size()][t.size()];
}
};
class Solution {
public:
int minDistance(string word1, string word2) {
vector<vector<www.laipuhuo.com.int>> dp(word1.size() 1,vector<int>(word2.size() 1,0));
// dp[i][j]表示 以i-1为结尾的word1和以j-1为结尾的word2 删除元素变得相同的最少操作数
for(int i = 0; i <= word1.size(); i ){
dp[i][0] = i;
}
for(int j = 0; j <= word2.size(); j ){
dp[0][j] = j;
}
for(int i = 1; i <= word1.size(); i ){
for(int j = 1; j <= word2.size(); j ){
if(word1[i - 1] == word2[j - 1]){
dp[i][j] = dp[i - 1][j - 1];
}else{
dp[i][j] = www.laipuhuo.com.min(dp[i - 1][j] 1,min(dp[i][j - 1] 1,dp[i - 1][j - 1] 2));
}
}
}
return dp[word1.size()][word2.size()];
}
};
