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Code for my tutorial on CNNs in Pytorch
CNN = SimpleCNN()
trainNet(CNN, batch_size=32, n_epochs=5, learning_rate=0.001)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
#The compose function allows for multiple transforms
#transforms.ToTensor() converts our PILImage to a tensor of shape (C x H x W) in the range [0,1]
#transforms.Normalize(mean,std) normalizes a tensor to a (mean, std) for (R, G, B)
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
train_set = torchvision.datasets.CIFAR10(root='./cifardata', train=True, download=True, transform=transform)
test_set = torchvision.datasets.CIFAR10(root='./cifardata', train=False, download=True, transform=transform)
import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms
self.conv2 = torch.nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)
self.pool2 = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
import torch.optim as optim
def createLossAndOptimizer(net, learning_rate=0.001):
#Loss function
loss = torch.nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=learning_rate)
return(loss, optimizer)
def outputSize(in_size, kernel_size, stride, padding):
output = int((in_size - kernel_size + 2*(padding)) / stride) + 1
seed = 42
from import SubsetRandomSampler
n_training_samples = 20000
train_sampler = SubsetRandomSampler(np.arange(n_training_samples, dtype=np.int64))
n_val_samples = 5000
val_sampler = SubsetRandomSampler(np.arange(n_training_samples, n_training_samples + n_val_samples, dtype=np.int64))
n_test_samples = 5000
test_sampler = SubsetRandomSampler(np.arange(n_test_samples, dtype=np.int64))
from torch.autograd import Variable
import torch.nn.functional as F
class SimpleCNN(torch.nn.Module):
#Our batch shape for input x is (3, 32, 32)
def __init__(self):
super(SimpleCNN, self).__init__()
#Input channels = 3, output channels = 18
self.conv1 = torch.nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)
self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
#4608 input features, 64 output features (see sizing flow below)
self.fc1 = torch.nn.Linear(18 * 16 * 16, 64)
#64 input features, 10 output features for our 10 defined classes
self.fc2 = torch.nn.Linear(64, 10)
def forward(self, x):
#Computes the activation of the first convolution
#Size changes from (3, 32, 32) to (18, 32, 32)
x = F.relu(self.conv1(x))
#Size changes from (18, 32, 32) to (18, 16, 16)
x = self.pool(x)
#Reshape data to input to the input layer of the neural net
#Size changes from (18, 16, 16) to (1, 4608)
#Recall that the -1 infers this dimension from the other given dimension
x = x.view(-1, 18 * 16 *16)
#Computes the activation of the first fully connected layer
#Size changes from (1, 4608) to (1, 64)
x = F.relu(self.fc1(x))
#Computes the second fully connected layer (activation applied later)
#Size changes from (1, 64) to (1, 10)
x = self.fc2(x)
#Import the support vector machine module from the sklearn framework
from sklearn import svm
#Label x and y variables from our dataset
x = ourData.features
y = ourData.labels
#Initialize our algorithm
classifier = svm.SVC()
#Fit model to our data,y)
#Test and validation loaders have constant batch sizes, so we can define them directly
test_loader =, batch_size=4, sampler=test_sampler, num_workers=2)
val_loader =, batch_size=128, sampler=val_sampler, num_workers=2)
import time
def trainNet(net, batch_size, n_epochs, learning_rate):
#Print all of the hyperparameters of the training iteration:
print("===== HYPERPARAMETERS =====")
print("batch_size=", batch_size)
print("epochs=", n_epochs)
print("learning_rate=", learning_rate)
print("=" * 30)
#Get training data
train_loader = get_train_loader(batch_size)
n_batches = len(train_loader)
#Create our loss and optimizer functions
loss, optimizer = createLossAndOptimizer(net, learning_rate)
#Time for printing
training_start_time = time.time()
#Loop for n_epochs
for epoch in range(n_epochs):
running_loss = 0.0
print_every = n_batches // 10
start_time = time.time()
total_train_loss = 0
for i, data in enumerate(train_loader, 0):
#Get inputs
inputs, labels = data
#Wrap them in a Variable object
inputs, labels = Variable(inputs), Variable(labels)
#Set the parameter gradients to zero
#Forward pass, backward pass, optimize
outputs = net(inputs)
loss_size = loss(outputs, labels)
#Print statistics
running_loss +=[0]
total_train_loss +=[0]
#Print every 10th batch of an epoch
if (i + 1) % (print_every + 1) == 0:
print("Epoch {}, {:d}% \t train_loss: {:.2f} took: {:.2f}s".format(
epoch+1, int(100 * (i+1) / n_batches), running_loss / print_every, time.time() - start_time))
#Reset running loss and time
running_loss = 0.0
start_time = time.time()
#At the end of the epoch, do a pass on the validation set
total_val_loss = 0
for inputs, labels in val_loader:
#Wrap tensors in Variables
inputs, labels = Variable(inputs), Variable(labels)
#Forward pass
val_outputs = net(inputs)
val_loss_size = loss(val_outputs, labels)
total_val_loss +=[0]
print("Validation loss = {:.2f}".format(total_val_loss / len(val_loader)))
print("Training finished, took {:.2f}s".format(time.time() - training_start_time))
#DataLoader takes in a dataset and a sampler for loading (num_workers deals with system level memory)
def get_train_loader(batch_size):
train_loader =, batch_size=batch_size,
sampler=train_sampler, num_workers=2)
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