added 13

Author: patrickloeber

13_feedforward.py

@@ -0,0 +1,103 @@
+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+import matplotlib.pyplot as plt
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Hyper-parameters 
+input_size = 784 # 28x28
+hidden_size = 500 
+num_classes = 10
+num_epochs = 2
+batch_size = 100
+learning_rate = 0.001
+
+# MNIST dataset 
+train_dataset = torchvision.datasets.MNIST(root='./data', 
+                                           train=True, 
+                                           transform=transforms.ToTensor(),  
+                                           download=True)
+
+test_dataset = torchvision.datasets.MNIST(root='./data', 
+                                          train=False, 
+                                          transform=transforms.ToTensor())
+
+# Data loader
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset, 
+                                           batch_size=batch_size, 
+                                           shuffle=True)
+
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset, 
+                                          batch_size=batch_size, 
+                                          shuffle=False)
+
+examples = iter(test_loader)
+example_data, example_targets = examples.next()
+
+for i in range(6):
+    plt.subplot(2,3,i+1)
+    plt.imshow(example_data[i][0], cmap='gray')
+plt.show()
+
+# Fully connected neural network with one hidden layer
+class NeuralNet(nn.Module):
+    def __init__(self, input_size, hidden_size, num_classes):
+        super(NeuralNet, self).__init__()
+        self.input_size = input_size
+        self.l1 = nn.Linear(input_size, hidden_size) 
+        self.relu = nn.ReLU()
+        self.l2 = nn.Linear(hidden_size, num_classes)  
+    
+    def forward(self, x):
+        out = self.l1(x)
+        out = self.relu(out)
+        out = self.l2(out)
+        # no activation and no softmax at the end
+        return out
+
+model = NeuralNet(input_size, hidden_size, num_classes).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  
+
+# Train the model
+n_total_steps = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):  
+        # origin shape: [100, 1, 28, 28]
+        # resized: [100, 784]
+        images = images.reshape(-1, 28*28).to(device)
+        labels = labels.to(device)
+        
+        # Forward pass
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')
+
+# Test the model
+# In test phase, we don't need to compute gradients (for memory efficiency)
+with torch.no_grad():
+    n_correct = 0
+    n_samples = 0
+    for images, labels in test_loader:
+        images = images.reshape(-1, 28*28).to(device)
+        labels = labels.to(device)
+        outputs = model(images)
+        # max returns (value ,index)
+        _, predicted = torch.max(outputs.data, 1)
+        n_samples += labels.size(0)
+        n_correct += (predicted == labels).sum().item()
+
+    acc = 100.0 * n_correct / n_samples
+    print(f'Accuracy of the network on the 10000 test images: {acc} %')