lab-10-8-mnist_nn_selu(wip).ipynb

Kernel: Python 3

# Lab 10 MNIST and softmax
import torch
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import random

device = 'cuda' if torch.cuda.is_available() else 'cpu'

# for reproducibility
random.seed(777)
torch.manual_seed(777)
if device == 'cuda':
    torch.cuda.manual_seed_all(777)

# parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
keep_prob = 0.7

# MNIST dataset
mnist_train = dsets.MNIST(root='MNIST_data/',
                          train=True,
                          transform=transforms.ToTensor(),
                          download=True)

mnist_test = dsets.MNIST(root='MNIST_data/',
                         train=False,
                         transform=transforms.ToTensor(),
                         download=True)

# dataset loader
data_loader = torch.utils.data.DataLoader(dataset=mnist_train,
                                          batch_size=batch_size,
                                          shuffle=True,
                                          drop_last=True)

# nn layers
linear1 = torch.nn.Linear(784, 512, bias=True)
linear2 = torch.nn.Linear(512, 512, bias=True)
linear3 = torch.nn.Linear(512, 512, bias=True)
linear4 = torch.nn.Linear(512, 512, bias=True)
linear5 = torch.nn.Linear(512, 10, bias=True)
selu = torch.nn.SELU()

# xavier initialization
torch.nn.init.xavier_uniform_(linear1.weight)
torch.nn.init.xavier_uniform_(linear2.weight)
torch.nn.init.xavier_uniform_(linear3.weight)
torch.nn.init.xavier_uniform_(linear4.weight)
torch.nn.init.xavier_uniform_(linear5.weight)

Parameter containing:
tensor([[-0.0565,  0.0423, -0.0155,  ...,  0.1012,  0.0459, -0.0191],
        [ 0.0772,  0.0452, -0.0638,  ...,  0.0476, -0.0638,  0.0528],
        [ 0.0311, -0.1023, -0.0701,  ...,  0.0412, -0.1004,  0.0738],
        ...,
        [ 0.0334,  0.0187, -0.1021,  ...,  0.0280, -0.0583, -0.1018],
        [-0.0506, -0.0939, -0.0467,  ..., -0.0554, -0.0325,  0.0640],
        [-0.0183, -0.0123,  0.1025,  ..., -0.0214,  0.0220, -0.0741]],
       requires_grad=True)

# model
model = torch.nn.Sequential(linear1, selu,
                            linear2, selu,
                            linear3, selu,
                            linear4, selu,
                            linear5).to(device)

# define cost/loss & optimizer
criterion = torch.nn.CrossEntropyLoss().to(device)    # Softmax is internally computed.
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

total_batch = len(data_loader)
model.train()    # set the model to train mode (dropout=True)
for epoch in range(training_epochs):
    avg_cost = 0

    for X, Y in data_loader:
        # reshape input image into [batch_size by 784]
        # label is not one-hot encoded
        X = X.view(-1, 28 * 28).to(device)
        Y = Y.to(device)

        optimizer.zero_grad()
        hypothesis = model(X)
        cost = criterion(hypothesis, Y)
        cost.backward()
        optimizer.step()

        avg_cost += cost / total_batch

    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))

print('Learning finished')

Epoch: 0001 cost = 0.314744234
Epoch: 0002 cost = 0.168358013
Epoch: 0003 cost = 0.133582667
Epoch: 0004 cost = 0.118396178
Epoch: 0005 cost = 0.102306806
Epoch: 0006 cost = 0.086196125
Epoch: 0007 cost = 0.090515122
Epoch: 0008 cost = 0.082275547
Epoch: 0009 cost = 0.072253749
Epoch: 0010 cost = 0.080003284
Epoch: 0011 cost = 0.063514479
Epoch: 0012 cost = 0.066991881
Epoch: 0013 cost = 0.063922137
Epoch: 0014 cost = 0.064661607
Epoch: 0015 cost = 0.054599971
Learning finished

# Test model and check accuracy
with torch.no_grad():
    model.eval()    # set the model to evaluation mode (dropout=False)

    # Test the model using test sets
    X_test = mnist_test.test_data.view(-1, 28 * 28).float().to(device)
    Y_test = mnist_test.test_labels.to(device)

    prediction = model(X_test)
    correct_prediction = torch.argmax(prediction, 1) == Y_test
    accuracy = correct_prediction.float().mean()
    print('Accuracy:', accuracy.item())

    # Get one and predict
    r = random.randint(0, len(mnist_test) - 1)
    X_single_data = mnist_test.test_data[r:r + 1].view(-1, 28 * 28).float().to(device)
    Y_single_data = mnist_test.test_labels[r:r + 1].to(device)

    print('Label: ', Y_single_data.item())
    single_prediction = model(X_single_data)
    print('Prediction: ', torch.argmax(single_prediction, 1).item())

Accuracy: 0.819599986076355
Label:  8
Prediction:  8