Update 14_cnn.py

Author: kmiddleton

14_cnn.py

@@ -5,13 +5,24 @@
 import torchvision.transforms as transforms
 import matplotlib.pyplot as plt
 import numpy as np
+import ssl
 
-# Device configuration
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+ssl._create_default_https_context = ssl._create_unverified_context
+
+# GPU device configuration
+if torch.cuda.is_available():
+  device = torch.device('cuda')
+  print('Using GPU')
+elif torch.backends.mps.is_available():
+  device = torch.device('mps')
+  print('Using MPS')
+else:
+  device = torch.device('cpu')
+  print('Using CPU')
 
 # Hyper-parameters 
-num_epochs = 5
-batch_size = 4
+num_epochs = 20
+batch_size = 8
 learning_rate = 0.001
 
 # dataset has PILImage images of range [0, 1]. 
@@ -22,109 +33,106 @@
 
 # CIFAR10: 60000 32x32 color images in 10 classes, with 6000 images per class
 train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,
-                                        download=True, transform=transform)
+                                             download=True, transform=transform)
 
 test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,
-                                       download=True, transform=transform)
+                                            download=True, transform=transform)
 
 train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,
-                                          shuffle=True)
+                                           shuffle=True)
 
 test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,
-                                         shuffle=False)
+                                          shuffle=False)
 
-classes = ('plane', 'car', 'bird', 'cat',
-           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+classes = ('plane', 'car', 'bird', 'cat', 'deer',
+           'dog', 'frog', 'horse', 'ship', 'truck')
 
 def imshow(img):
-    img = img / 2 + 0.5  # unnormalize
-    npimg = img.numpy()
-    plt.imshow(np.transpose(npimg, (1, 2, 0)))
-    plt.show()
-
+  img = img / 2 + 0.5  # un-normalize
+  npimg = img.numpy()
+  plt.imshow(np.transpose(npimg, (1, 2, 0)))
+  plt.show()
 
 # get some random training images
 dataiter = iter(train_loader)
 images, labels = next(dataiter)
 
 # show images
-imshow(torchvision.utils.make_grid(images))
+# imshow(torchvision.utils.make_grid(images))
 
 class ConvNet(nn.Module):
-    def __init__(self):
-        super(ConvNet, 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):
-        # -> n, 3, 32, 32
-        x = self.pool(F.relu(self.conv1(x)))  # -> n, 6, 14, 14
-        x = self.pool(F.relu(self.conv2(x)))  # -> n, 16, 5, 5
-        x = x.view(-1, 16 * 5 * 5)            # -> n, 400
-        x = F.relu(self.fc1(x))               # -> n, 120
-        x = F.relu(self.fc2(x))               # -> n, 84
-        x = self.fc3(x)                       # -> n, 10
-        return x
-
+  def __init__(self):
+    super(ConvNet, 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):
+    # -> n, 3, 32, 32
+    x = self.pool(F.leaky_relu(self.conv1(x)))  # -> n, 6, 14, 14
+    x = self.pool(F.leaky_relu(self.conv2(x)))  # -> n, 16, 5, 5
+    x = x.view(-1, 16 * 5 * 5)                  # -> n, 400
+    x = F.leaky_relu(self.fc1(x))               # -> n, 120
+    x = F.leaky_relu(self.fc2(x))               # -> n, 84
+    x = self.fc3(x)                             # -> n, 10
+    return x
 
 model = ConvNet().to(device)
 
 criterion = nn.CrossEntropyLoss()
-optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
 
 n_total_steps = len(train_loader)
 for epoch in range(num_epochs):
-    for i, (images, labels) in enumerate(train_loader):
-        # origin shape: [4, 3, 32, 32] = 4, 3, 1024
-        # input_layer: 3 input channels, 6 output channels, 5 kernel size
-        images = images.to(device)
-        labels = labels.to(device)
+  for i, (images, labels) in enumerate(train_loader):
+    # origin shape: [4, 3, 32, 32] = 4, 3, 1024
+    # input_layer: 3 input channels, 6 output channels, 5 kernel size
+    images = images.to(device)
+    labels = labels.to(device)
 
-        # Forward pass
-        outputs = model(images)
-        loss = criterion(outputs, labels)
+    # Forward pass
+    outputs = model(images)
+    loss = criterion(outputs, labels)
 
-        # Backward and optimize
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
+    # Backward and optimize
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
 
-        if (i+1) % 2000 == 0:
-            print (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')
+    if (i+1) % 2000 == 0:
+      print (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')
 
 print('Finished Training')
 PATH = './cnn.pth'
 torch.save(model.state_dict(), PATH)
 
 with torch.no_grad():
-    n_correct = 0
-    n_samples = 0
-    n_class_correct = [0 for i in range(10)]
-    n_class_samples = [0 for i in range(10)]
-    for images, labels in test_loader:
-        images = images.to(device)
-        labels = labels.to(device)
-        outputs = model(images)
-        # max returns (value ,index)
-        _, predicted = torch.max(outputs, 1)
-        n_samples += labels.size(0)
-        n_correct += (predicted == labels).sum().item()
-        
-        for i in range(batch_size):
-            label = labels[i]
-            pred = predicted[i]
-            if (label == pred):
-                n_class_correct[label] += 1
-            n_class_samples[label] += 1
-
-    acc = 100.0 * n_correct / n_samples
-    print(f'Accuracy of the network: {acc} %')
-
-    for i in range(10):
-        acc = 100.0 * n_class_correct[i] / n_class_samples[i]
-        print(f'Accuracy of {classes[i]}: {acc} %')
-
+  n_correct = 0
+  n_samples = 0
+  n_class_correct = [0 for i in range(10)]
+  n_class_samples = [0 for i in range(10)]
+  for images, labels in test_loader:
+    images = images.to(device)
+    labels = labels.to(device)
+    outputs = model(images)
+    # max returns (value ,index)
+    _, predicted = torch.max(outputs, 1)
+    n_samples += labels.size(0)
+    n_correct += (predicted == labels).sum().item()
+    
+    for i in range(batch_size):
+      label = labels[i]
+      pred = predicted[i]
+      if (label == pred):
+        n_class_correct[label] += 1
+      n_class_samples[label] += 1
+
+  acc = 100.0 * n_correct / n_samples
+  print(f'Accuracy of the network: {acc} %')
+
+  for i in range(10):
+    acc = 100.0 * n_class_correct[i] / n_class_samples[i]
+    print(f'Accuracy of {classes[i]}: {acc} %')