Daily task

Tasks/daily tasks/Hashina/day_3.py

@@ -0,0 +1,18 @@
+from torch import nn
+
+class Network(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+        self.hidden = nn.Linear(1, 2)
+        self.output = nn.Linear(2, 1)
+
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        x = self.hidden(x)
+        x = self.sigmoid(x)
+        x = self.output(x)
+
+        return x
+

Tasks/daily tasks/Hashina/day_4.py

@@ -1,18 +1,98 @@
-from torch import nn
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+import torchvision.transforms as transforms
+import torch.optim as optim
 
-class Network(nn.Module):
+transform = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize(
+            (0.5, 0.5, 0.5),
+            (0.5, 0.5, 0.5)
+        )
+    ]
+)
+
+trainset = torchvision.datasets.CIFAR10(
+    root='./data',
+    train=True,
+    download=False,
+    transform=transform
+)
+
+testset = torchvision.datasets.CIFAR10(
+    root='./data',
+    train=False,
+    download=False,
+    transform=transform
+)
+
+trainloader = torch.utils.data.DataLoader(
+    trainset,
+    batch_size=4,
+    shuffle=True,
+    num_workers=2
+)
+
+testloader = torch.utils.data.DataLoader(
+    testset,
+    batch_size=4,
+    shuffle=False,
+    num_workers=2
+)
+
+classes = (
+    'plane', 'car', 'bird', 'cat',
+           'deer', 'dog', 'frog', 'horse', 'ship', 'truck'
+)
+
+class Net(nn.Module):
     def __init__(self):
-        super().__init__()
-
-        self.hidden = nn.Linear(1, 2)
-        self.output = nn.Linear(2, 1)
-
-        self.sigmoid = nn.Sigmoid()
-
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
     def forward(self, x):
-        x = self.hidden(x)
-        x = self.sigmoid(x)
-        x = self.output(x)
-
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
         return x
-
+
+
+net = Net()
+
+loss_function = nn.CrossEntropyLoss()
+optimizer = optim.SGD(
+    net.parameters(),
+    lr=0.001
+)
+
+for epoch in range(2):
+    running_loss = 0.0
+    for i, data in enumerate(trainloader, 0):
+        # data = (inputs, labels)
+        inputs, labels = data
+        optimizer.zero_grad()
+
+        outputs = net(inputs)
+        loss = loss_function(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+        running_loss = running_loss + loss.item()
+        if i % 2000 == 1999:
+            print(
+                '[%d, %5d] loss: %.3f' %
+                (epoch + 1, i+1, running_loss/2000)
+            )
+            running_loss = 0.0
+print("vola")