Update tutorials for pytorch 0.4.0

tutorials/01-basics/feedforward_neural_network/main.py

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+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Hyper-parameters 
+input_size = 784
+hidden_size = 500
+num_classes = 10
+num_epochs = 5
+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)
+
+# 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.fc1 = nn.Linear(input_size, hidden_size) 
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_size, num_classes)  
+
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.relu(out)
+        out = self.fc2(out)
+        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
+total_step = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):  
+        # Move tensors to the configured device
+        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 ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
+                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
+
+# Test the model
+# In test phase, we don't need to compute gradients (for memory efficiency)
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.reshape(-1, 28*28).to(device)
+        labels = labels.to(device)
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+    print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')

tutorials/01-basics/linear_regression/main.py

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+import torch
+import torch.nn as nn
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# Hyper-parameters
+input_size = 1
+output_size = 1
+num_epochs = 60
+learning_rate = 0.001
+
+# Toy dataset
+x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], 
+                    [9.779], [6.182], [7.59], [2.167], [7.042], 
+                    [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)
+
+y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], 
+                    [3.366], [2.596], [2.53], [1.221], [2.827], 
+                    [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)
+
+# Linear regression model
+model = nn.Linear(input_size, output_size)
+
+# Loss and optimizer
+criterion = nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
+
+# Train the model
+for epoch in range(num_epochs):
+    # Convert numpy arrays to torch tensors
+    inputs = torch.from_numpy(x_train)
+    targets = torch.from_numpy(y_train)
+
+    # Forward pass
+    outputs = model(inputs)
+    loss = criterion(outputs, targets)
+
+    # Backward and optimize
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    if (epoch+1) % 5 == 0:
+        print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
+
+# Plot the graph
+predicted = model(torch.from_numpy(x_train)).detach().numpy()
+plt.plot(x_train, y_train, 'ro', label='Original data')
+plt.plot(x_train, predicted, label='Fitted line')
+plt.legend()
+plt.show()
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')

tutorials/01-basics/logistic_regression/main.py

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+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+
+# Hyper-parameters 
+input_size = 784
+num_classes = 10
+num_epochs = 5
+batch_size = 100
+learning_rate = 0.001
+
+# MNIST dataset (images and labels)
+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 (input pipeline)
+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)
+
+# Logistic regression model
+model = nn.Linear(input_size, num_classes)
+
+# Loss and optimizer
+# nn.CrossEntropyLoss() computes softmax internally
+criterion = nn.CrossEntropyLoss()  
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
+
+# Train the model
+total_step = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        # Reshape images to (batch_size, input_size)
+        images = images.reshape(-1, 28*28)
+
+        # 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 ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
+                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
+
+# Test the model
+# In test phase, we don't need to compute gradients (for memory efficieny)
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.reshape(-1, 28*28)
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum()
+
+    print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')