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Computing Resources

• Operating system: Preferably Linux or MacOS. If you have Windows, things may crash unexpectedly (try installing a virtual machine if you need to)
• RAM: Minimum 8GB
• Disk space: Mininium 16GB

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Software Requirements

In this lab session we are going to be using Python as our main programming language. If you are not familiar with it, I recommend you start with this free Python course offered by Codecademy.

Here is a list of the required programs and libraries necessary for this lab session. (Please install them before coming to our lab session on Tuesday; this will save us a lot of time, plus these include some of the same libraries you may need for your first assignment).

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tensor([[0.6741],
        [0.9680],
        [0.9973],
        [0.9993]])
tensor([[ 0.9889],
        [-0.4191],
        [ 0.8347],
        [-0.0384]])

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test_acc = 0.0
for i, data in enumerate(testloader, 0):
    inputs, labels = data
    inputs = inputs.view(-1, 28, 28)

    outputs = model(inputs)

    test_acc += get_accuracy(outputs, labels, BATCH_SIZE)
        
print('Test Accuracy: %.2f'%( test_acc/i))

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for epoch in range(N_EPHOCS):  # loop over the dataset multiple times
    train_running_loss = 0.0
    train_acc = 0.0
    model.train()
    
    # TRAINING ROUND
    for i, data in enumerate(trainloader):
         # zero the parameter gradients
        optimizer.zero_grad()
        

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import torch.optim as optim

# Device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Model instance
model = ImageRNN(BATCH_SIZE, N_STEPS, N_INPUTS, N_NEURONS, N_OUTPUTS)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

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dataiter = iter(trainloader)
images, labels = dataiter.next()
model = ImageRNN(BATCH_SIZE, N_STEPS, N_INPUTS, N_NEURONS, N_OUTPUTS)
logits = model(images.view(-1, 28,28))
print(logits[0:10])

### output
'''
tensor([[-0.0197,  0.0082,  0.0248,  0.0758, -0.0110,  0.0521, -0.0261,  0.0217,
         -0.0083,  0.0866],

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class ImageRNN(nn.Module):
    def __init__(self, batch_size, n_steps, n_inputs, n_neurons, n_outputs):
        super(ImageRNN, self).__init__()
        
        self.n_neurons = n_neurons
        self.batch_size = batch_size
        self.n_steps = n_steps
        self.n_inputs = n_inputs
        self.n_outputs = n_outputs
        

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# parameters 
N_STEPS = 28
N_INPUTS = 28
N_NEURONS = 150
N_OUTPUTS = 10
N_EPHOCS = 10

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import matplotlib.pyplot as plt
import numpy as np

# functions to show an image
def imshow(img):
    #img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))

# get some random training images

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import torchvision
import torchvision.transforms as transforms

BATCH_SIZE = 64

# list all transformations
transform = transforms.Compose(
    [transforms.ToTensor()])

# download and load training dataset