nn_linear and nn_maxpool and nn_non_activation

.gitignore

@@ -3,4 +3,5 @@
 /datasets_tranforms/CIFAR10/
 /datasets_transforms/CIFAR10/
 /datasets_tranforms/p10/
-/logs/
+/logs/
+/data/

nn_linear.py

@@ -0,0 +1,31 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2024/5/22 15:10
+# @Author  : yzh
+# @File    : nn_linear.py
+# @Software: PyCharm
+import torch
+import torchvision
+from torch import nn
+from torch.nn import Linear
+from torch.utils.data import DataLoader
+
+dataset = torchvision.datasets.CIFAR10('./data',train=False,transform=torchvision.transforms.ToTensor(),
+                                       download=True)
+
+dataloader = DataLoader(dataset,batch_size=64,drop_last=True)
+
+class Model(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = Linear()
+
+    def forward(self,input):
+        output = self.linear(input)
+        return output
+
+model = Model()
+
+for data in dataloader:
+    imgs,targets = data
+    imgs = torch.flatten(imgs)
+    print(imgs.shape)

nn_maxpool.py

@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2024/5/22 14:05
+# @Author  : yzh
+# @File    : nn_maxpool.py
+# @Software: PyCharm
+import torch
+from torch import nn
+from torch.nn import MaxPool2d
+
+input = torch.tensor([[1,2,0,3,1],
+                      [0,1,2,3,1],
+                      [1,2,1,0,0],
+                      [5,2,3,1,1],
+                      [2,1,0,1,1]],dtype=torch.float)
+input = torch.reshape(input,(-1,1,5,5))
+class Model(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.maxpool1 = MaxPool2d(kernel_size=3,ceil_mode=True)
+
+    def forward(self,input):
+        output = self.maxpool1(input)
+        return output
+model = Model()
+output = model(input)
+print(output)

nn_maxpool_dataloader.py

@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2024/5/22 14:15
+# @Author  : yzh
+# @File    : nn_maxpool_dataloader.py
+# @Software: PyCharm
+import torch
+import torchvision
+from torch import nn
+from torch.nn import MaxPool2d
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+
+dataset = torchvision.datasets.CIFAR10(',/datasets_tranforms/CIFAR10', train=True,
+                                       transform=torchvision.transforms.ToTensor(), download=True)
+
+dataloader = DataLoader(dataset,batch_size=64)
+
+class Model(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.maxpool = MaxPool2d(kernel_size=3)
+
+    def forward(self,input):
+        output = self.maxpool(input)
+        return output
+
+model = Model()
+
+writer = SummaryWriter('logs')
+step = 0
+for data in dataloader:
+    imgs,targets = data
+    writer.add_images('input2',imgs,step)
+    output = model(imgs)
+    writer.add_images('output2',output,step)
+    step = step+1
+writer.close()

nn_relu.py

@@ -0,0 +1,44 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2024/5/22 14:37
+# @Author  : yzh
+# @File    : nn_relu.py
+# @Software: PyCharm
+import torch
+import torchvision
+from torch import nn
+from torch.nn import ReLU, Sigmoid
+from torch.utils.data import DataLoader
+from torch.utils.tensorboard import SummaryWriter
+
+input = torch.tensor([[-1,0.5],
+                      [3,-2]])
+input = torch.reshape(input,(-1,1,2,2))
+
+dataset = torchvision.datasets.CIFAR10('./datasets_tranforms/CIFAR10',train=False,
+                                       transform=torchvision.transforms.ToTensor(),download=True)
+dataloader = DataLoader(dataset,batch_size=64)
+
+class Model(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # self.relu = ReLU()
+        # exchage sigmoid
+        self.sigmoid = Sigmoid()
+    def forward(self,input):
+        # output = self.relu(input)
+        output = self.sigmoid(input)
+        return output
+
+model = Model()
+# output = model(input)
+#
+# print(output)
+writer = SummaryWriter('logs')
+step = 0
+for data in dataloader:
+    imgs,targets = data
+    writer.add_images('sigmoid_input',imgs,step)
+    output = model(imgs)
+    writer.add_images('sigmoid_output',output,step)
+    step = step+1
+writer.close()