AML21: 02 Convolutional Neural Network for MNIST¶

Based on https://github.com/Atcold/pytorch-Deep-Learning

Data and Libraries¶

In [2]:
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy

# this 'device' will be used for training our model
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
cuda:0

Load the MNIST dataset¶

Observe that we set shuffle=True, which means that data is randomized

In [3]:
input_size  = 28*28   # images are 28x28 pixels
output_size = 10      # there are 10 classes

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=64, shuffle=True)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=1000, shuffle=True)
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz
Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz
Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz
Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw
Processing...
Done!
/opt/conda/lib/python3.7/site-packages/torchvision/datasets/mnist.py:480: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)
  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
In [4]:
# show some training images
plt.figure(figsize=(16, 4))

# fetch a batch of train images; RANDOM
image_batch, label_batch = next(iter(train_loader))

for i in range(20):
    image = image_batch[i]
    label = label_batch[i].item()
    plt.subplot(2, 10, i + 1)
    #image, label = train_loader.dataset.__getitem__(i)
    plt.imshow(image.squeeze().numpy())
    plt.axis('off')
    plt.title(label)

Helper functions for training and testing¶

In [10]:
# function to count number of parameters
def get_n_params(model):
    np=0
    for p in list(model.parameters()):
        np += p.nelement()
    return np

accuracy_list = []
# we pass a model object to this trainer, and it trains this model for one epoch
def train(epoch, model):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        # send to device
        data, target = data.to(device), target.to(device)
        
        optimizer.zero_grad()
        output = model(data)
        #loss = F.nll_loss(output, target)
        loss = F.cross_entropy(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))
            
def test(model):
    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        # send to device
        data, target = data.to(device), target.to(device)
        
        output = model(data)
        #test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
        test_loss += F.cross_entropy(output, target, reduction='sum').item() # sum up batch loss
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability                                                                 
        correct += pred.eq(target.data.view_as(pred)).cpu().sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    accuracy_list.append(accuracy)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        accuracy))

The Convolutional Network¶

In [16]:
class CNN(nn.Module):
    def __init__(self, input_size, output_size):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=12, kernel_size=3,padding=0)
        self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=6,padding=0)
        self.conv3 = nn.Conv2d(in_channels=24, out_channels=32, kernel_size=6,padding=0)
        self.fc1 = nn.Linear(8*4*4, 200)
        self.fc2 = nn.Linear(200, 10)
        
    def forward(self, x, verbose=False):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, kernel_size=2)
        x = self.conv3(x)
        x = F.relu(x)
        x = F.max_pool2d(x, kernel_size=2)
        x = x.view(-1, 8*4*4)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = F.log_softmax(x, dim=1)
        return x

Train the Network¶

In [17]:
print("Training on ", device)
model_cnn = CNN(input_size, output_size)
model_cnn.to(device)
optimizer = optim.SGD(model_cnn.parameters(), lr=0.01, momentum=0.5)
print('Number of parameters: {}'.format(get_n_params(model_cnn)))

for epoch in range(0, 10):
    train(epoch, model_cnn)
    test(model_cnn)
Training on  cuda:0
Number of parameters: 66002
Train Epoch: 0 [0/60000 (0%)]	Loss: 2.308860
Train Epoch: 0 [6400/60000 (11%)]	Loss: 2.165044
Train Epoch: 0 [12800/60000 (21%)]	Loss: 0.379953
Train Epoch: 0 [19200/60000 (32%)]	Loss: 0.318275
Train Epoch: 0 [25600/60000 (43%)]	Loss: 0.183063
Train Epoch: 0 [32000/60000 (53%)]	Loss: 0.189834
Train Epoch: 0 [38400/60000 (64%)]	Loss: 0.280002
Train Epoch: 0 [44800/60000 (75%)]	Loss: 0.095124
Train Epoch: 0 [51200/60000 (85%)]	Loss: 0.209925
Train Epoch: 0 [57600/60000 (96%)]	Loss: 0.225800

Test set: Average loss: 0.1622, Accuracy: 9465/10000 (95%)

Train Epoch: 1 [0/60000 (0%)]	Loss: 0.169783
Train Epoch: 1 [6400/60000 (11%)]	Loss: 0.128311
Train Epoch: 1 [12800/60000 (21%)]	Loss: 0.152857
Train Epoch: 1 [19200/60000 (32%)]	Loss: 0.099723
Train Epoch: 1 [25600/60000 (43%)]	Loss: 0.038499
Train Epoch: 1 [32000/60000 (53%)]	Loss: 0.029974
Train Epoch: 1 [38400/60000 (64%)]	Loss: 0.079771
Train Epoch: 1 [44800/60000 (75%)]	Loss: 0.061657
Train Epoch: 1 [51200/60000 (85%)]	Loss: 0.099150
Train Epoch: 1 [57600/60000 (96%)]	Loss: 0.118388

Test set: Average loss: 0.0793, Accuracy: 9748/10000 (97%)

Train Epoch: 2 [0/60000 (0%)]	Loss: 0.043124
Train Epoch: 2 [6400/60000 (11%)]	Loss: 0.204609
Train Epoch: 2 [12800/60000 (21%)]	Loss: 0.055560
Train Epoch: 2 [19200/60000 (32%)]	Loss: 0.054713
Train Epoch: 2 [25600/60000 (43%)]	Loss: 0.091090
Train Epoch: 2 [32000/60000 (53%)]	Loss: 0.024141
Train Epoch: 2 [38400/60000 (64%)]	Loss: 0.051452
Train Epoch: 2 [44800/60000 (75%)]	Loss: 0.015246
Train Epoch: 2 [51200/60000 (85%)]	Loss: 0.037737
Train Epoch: 2 [57600/60000 (96%)]	Loss: 0.041802

Test set: Average loss: 0.0583, Accuracy: 9816/10000 (98%)

Train Epoch: 3 [0/60000 (0%)]	Loss: 0.043293
Train Epoch: 3 [6400/60000 (11%)]	Loss: 0.011458
Train Epoch: 3 [12800/60000 (21%)]	Loss: 0.019903
Train Epoch: 3 [19200/60000 (32%)]	Loss: 0.193260
Train Epoch: 3 [25600/60000 (43%)]	Loss: 0.046639
Train Epoch: 3 [32000/60000 (53%)]	Loss: 0.079991
Train Epoch: 3 [38400/60000 (64%)]	Loss: 0.118307
Train Epoch: 3 [44800/60000 (75%)]	Loss: 0.041812
Train Epoch: 3 [51200/60000 (85%)]	Loss: 0.025002
Train Epoch: 3 [57600/60000 (96%)]	Loss: 0.015684

Test set: Average loss: 0.0465, Accuracy: 9841/10000 (98%)

Train Epoch: 4 [0/60000 (0%)]	Loss: 0.072492
Train Epoch: 4 [6400/60000 (11%)]	Loss: 0.063518
Train Epoch: 4 [12800/60000 (21%)]	Loss: 0.009607
Train Epoch: 4 [19200/60000 (32%)]	Loss: 0.105356
Train Epoch: 4 [25600/60000 (43%)]	Loss: 0.066226
Train Epoch: 4 [32000/60000 (53%)]	Loss: 0.050183
Train Epoch: 4 [38400/60000 (64%)]	Loss: 0.130452
Train Epoch: 4 [44800/60000 (75%)]	Loss: 0.063028
Train Epoch: 4 [51200/60000 (85%)]	Loss: 0.044991
Train Epoch: 4 [57600/60000 (96%)]	Loss: 0.028322

Test set: Average loss: 0.0472, Accuracy: 9849/10000 (98%)

Train Epoch: 5 [0/60000 (0%)]	Loss: 0.020115
Train Epoch: 5 [6400/60000 (11%)]	Loss: 0.066754
Train Epoch: 5 [12800/60000 (21%)]	Loss: 0.051151
Train Epoch: 5 [19200/60000 (32%)]	Loss: 0.016741
Train Epoch: 5 [25600/60000 (43%)]	Loss: 0.013083
Train Epoch: 5 [32000/60000 (53%)]	Loss: 0.020682
Train Epoch: 5 [38400/60000 (64%)]	Loss: 0.004839
Train Epoch: 5 [44800/60000 (75%)]	Loss: 0.009717
Train Epoch: 5 [51200/60000 (85%)]	Loss: 0.024498
Train Epoch: 5 [57600/60000 (96%)]	Loss: 0.005110

Test set: Average loss: 0.0517, Accuracy: 9837/10000 (98%)

Train Epoch: 6 [0/60000 (0%)]	Loss: 0.014875
Train Epoch: 6 [6400/60000 (11%)]	Loss: 0.038760
Train Epoch: 6 [12800/60000 (21%)]	Loss: 0.067141
Train Epoch: 6 [19200/60000 (32%)]	Loss: 0.042503
Train Epoch: 6 [25600/60000 (43%)]	Loss: 0.010095
Train Epoch: 6 [32000/60000 (53%)]	Loss: 0.041471
Train Epoch: 6 [38400/60000 (64%)]	Loss: 0.016541
Train Epoch: 6 [44800/60000 (75%)]	Loss: 0.043632
Train Epoch: 6 [51200/60000 (85%)]	Loss: 0.005362
Train Epoch: 6 [57600/60000 (96%)]	Loss: 0.003417

Test set: Average loss: 0.0413, Accuracy: 9869/10000 (99%)

Train Epoch: 7 [0/60000 (0%)]	Loss: 0.006296
Train Epoch: 7 [6400/60000 (11%)]	Loss: 0.009946
Train Epoch: 7 [12800/60000 (21%)]	Loss: 0.019403
Train Epoch: 7 [19200/60000 (32%)]	Loss: 0.031145
Train Epoch: 7 [25600/60000 (43%)]	Loss: 0.057772
Train Epoch: 7 [32000/60000 (53%)]	Loss: 0.001385
Train Epoch: 7 [38400/60000 (64%)]	Loss: 0.019975
Train Epoch: 7 [44800/60000 (75%)]	Loss: 0.007929
Train Epoch: 7 [51200/60000 (85%)]	Loss: 0.051805
Train Epoch: 7 [57600/60000 (96%)]	Loss: 0.021721

Test set: Average loss: 0.0391, Accuracy: 9878/10000 (99%)

Train Epoch: 8 [0/60000 (0%)]	Loss: 0.004407
Train Epoch: 8 [6400/60000 (11%)]	Loss: 0.015427
Train Epoch: 8 [12800/60000 (21%)]	Loss: 0.006642
Train Epoch: 8 [19200/60000 (32%)]	Loss: 0.003515
Train Epoch: 8 [25600/60000 (43%)]	Loss: 0.006077
Train Epoch: 8 [32000/60000 (53%)]	Loss: 0.017722
Train Epoch: 8 [38400/60000 (64%)]	Loss: 0.003335
Train Epoch: 8 [44800/60000 (75%)]	Loss: 0.008803
Train Epoch: 8 [51200/60000 (85%)]	Loss: 0.032059
Train Epoch: 8 [57600/60000 (96%)]	Loss: 0.011211

Test set: Average loss: 0.0374, Accuracy: 9892/10000 (99%)

Train Epoch: 9 [0/60000 (0%)]	Loss: 0.004002
Train Epoch: 9 [6400/60000 (11%)]	Loss: 0.024875
Train Epoch: 9 [12800/60000 (21%)]	Loss: 0.010543
Train Epoch: 9 [19200/60000 (32%)]	Loss: 0.017098
Train Epoch: 9 [25600/60000 (43%)]	Loss: 0.013674
Train Epoch: 9 [32000/60000 (53%)]	Loss: 0.005442
Train Epoch: 9 [38400/60000 (64%)]	Loss: 0.001328
Train Epoch: 9 [44800/60000 (75%)]	Loss: 0.040370
Train Epoch: 9 [51200/60000 (85%)]	Loss: 0.006340
Train Epoch: 9 [57600/60000 (96%)]	Loss: 0.032047

Test set: Average loss: 0.0433, Accuracy: 9867/10000 (99%)

Show some predictions of the test network¶

In [18]:
def visualize_pred(img, pred_prob, real_label):
    ''' Function for viewing an image and it's predicted classes.
    '''
    #pred_prob = pred_prob.data.numpy().squeeze()

    fig, (ax1, ax2) = plt.subplots(figsize=(6,9), ncols=2)
    ax1.imshow(img.numpy().squeeze())
    ax1.axis('off')
    pred_label = numpy.argmax(pred_prob)
    ax1.set_title([real_label, pred_label])
    
    ax2.barh(numpy.arange(10), pred_prob)
    ax2.set_aspect(0.1)
    ax2.set_yticks(numpy.arange(10))
    ax2.set_yticklabels(numpy.arange(10))
    ax2.set_title('Prediction Probability')
    ax2.set_xlim(0, 1.1)
    plt.tight_layout()
In [19]:
model_cnn.to('cpu') 

# fetch a batch of test images
image_batch, label_batch = next(iter(test_loader))

# Turn off gradients to speed up this part
with torch.no_grad():
    log_pred_prob_batch = model_cnn(image_batch)
for i in range(10):
    img = image_batch[i]
    real_label = label_batch[i].item()
    log_pred_prob = log_pred_prob_batch[i]
    # Output of the network are log-probabilities, need to take exponential for probabilities
    pred_prob = torch.exp(log_pred_prob).data.numpy().squeeze()
    visualize_pred(img, pred_prob, real_label)

Network with Dropout¶

In [32]:
class CNNDropout(nn.Module):
    def __init__(self, input_size, output_size):
        super(CNNDropout, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=12, kernel_size=3,padding=0)
        self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=6,padding=0)
        self.conv3 = nn.Conv2d(in_channels=24, out_channels=32, kernel_size=6,padding=0)
        self.fc1 = nn.Linear(8*4*4, 200)
        self.do1 = nn.Dropout2d(p=0.8)
        self.fc2 = nn.Linear(200, 10)
        
    def forward(self, x, verbose=False):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, kernel_size=2)
        x = self.conv3(x)
        x = F.relu(x)
        x = F.max_pool2d(x, kernel_size=2)
        x = x.view(-1, 8*4*4)
        x = self.fc1(x)
        x = self.do1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = F.log_softmax(x, dim=1)
        return x
In [33]:
print("Training on ", device)
model_2 = CNNDropout(input_size, output_size)
model_2.to(device)
optimizer = optim.SGD(model_2.parameters(), lr=0.01, momentum=0.5)
print('Number of parameters: {}'.format(get_n_params(model_2)))

for epoch in range(0, 10):
    model_2.train() # model in training mode. Turns on dropout, batch-norm etc during training
    train(epoch, model_2)
    model_2.eval() # model in evaluation mode. Turn off dropout, batch-norm etc during validation/testing
    test(model_2)
Training on  cuda:0
Number of parameters: 66002
Train Epoch: 0 [0/60000 (0%)]	Loss: 2.335004
Train Epoch: 0 [6400/60000 (11%)]	Loss: 1.971706
Train Epoch: 0 [12800/60000 (21%)]	Loss: 0.863391
Train Epoch: 0 [19200/60000 (32%)]	Loss: 0.490685
Train Epoch: 0 [25600/60000 (43%)]	Loss: 0.739785
Train Epoch: 0 [32000/60000 (53%)]	Loss: 0.525101
Train Epoch: 0 [38400/60000 (64%)]	Loss: 0.650745
Train Epoch: 0 [44800/60000 (75%)]	Loss: 0.474892
Train Epoch: 0 [51200/60000 (85%)]	Loss: 0.379315
Train Epoch: 0 [57600/60000 (96%)]	Loss: 0.219513

Test set: Average loss: 0.1600, Accuracy: 9518/10000 (95%)

Train Epoch: 1 [0/60000 (0%)]	Loss: 0.449828
Train Epoch: 1 [6400/60000 (11%)]	Loss: 0.310924
Train Epoch: 1 [12800/60000 (21%)]	Loss: 0.244960
Train Epoch: 1 [19200/60000 (32%)]	Loss: 0.402632
Train Epoch: 1 [25600/60000 (43%)]	Loss: 0.171564
Train Epoch: 1 [32000/60000 (53%)]	Loss: 0.135999
Train Epoch: 1 [38400/60000 (64%)]	Loss: 0.212614
Train Epoch: 1 [44800/60000 (75%)]	Loss: 0.132487
Train Epoch: 1 [51200/60000 (85%)]	Loss: 0.190448
Train Epoch: 1 [57600/60000 (96%)]	Loss: 0.151809

Test set: Average loss: 0.0795, Accuracy: 9750/10000 (98%)

Train Epoch: 2 [0/60000 (0%)]	Loss: 0.196738
Train Epoch: 2 [6400/60000 (11%)]	Loss: 0.211350
Train Epoch: 2 [12800/60000 (21%)]	Loss: 0.131960
Train Epoch: 2 [19200/60000 (32%)]	Loss: 0.070028
Train Epoch: 2 [25600/60000 (43%)]	Loss: 0.202823
Train Epoch: 2 [32000/60000 (53%)]	Loss: 0.129699
Train Epoch: 2 [38400/60000 (64%)]	Loss: 0.086665
Train Epoch: 2 [44800/60000 (75%)]	Loss: 0.092100
Train Epoch: 2 [51200/60000 (85%)]	Loss: 0.305474
Train Epoch: 2 [57600/60000 (96%)]	Loss: 0.166811

Test set: Average loss: 0.0630, Accuracy: 9804/10000 (98%)

Train Epoch: 3 [0/60000 (0%)]	Loss: 0.099700
Train Epoch: 3 [6400/60000 (11%)]	Loss: 0.083424
Train Epoch: 3 [12800/60000 (21%)]	Loss: 0.153879
Train Epoch: 3 [19200/60000 (32%)]	Loss: 0.066203
Train Epoch: 3 [25600/60000 (43%)]	Loss: 0.057744
Train Epoch: 3 [32000/60000 (53%)]	Loss: 0.169258
Train Epoch: 3 [38400/60000 (64%)]	Loss: 0.039940
Train Epoch: 3 [44800/60000 (75%)]	Loss: 0.127385
Train Epoch: 3 [51200/60000 (85%)]	Loss: 0.105391
Train Epoch: 3 [57600/60000 (96%)]	Loss: 0.038290

Test set: Average loss: 0.0516, Accuracy: 9829/10000 (98%)

Train Epoch: 4 [0/60000 (0%)]	Loss: 0.129199
Train Epoch: 4 [6400/60000 (11%)]	Loss: 0.189729
Train Epoch: 4 [12800/60000 (21%)]	Loss: 0.108842
Train Epoch: 4 [19200/60000 (32%)]	Loss: 0.092315
Train Epoch: 4 [25600/60000 (43%)]	Loss: 0.062148
Train Epoch: 4 [32000/60000 (53%)]	Loss: 0.135866
Train Epoch: 4 [38400/60000 (64%)]	Loss: 0.264096
Train Epoch: 4 [44800/60000 (75%)]	Loss: 0.205851
Train Epoch: 4 [51200/60000 (85%)]	Loss: 0.024897
Train Epoch: 4 [57600/60000 (96%)]	Loss: 0.027149

Test set: Average loss: 0.0432, Accuracy: 9879/10000 (99%)

Train Epoch: 5 [0/60000 (0%)]	Loss: 0.049005
Train Epoch: 5 [6400/60000 (11%)]	Loss: 0.032781
Train Epoch: 5 [12800/60000 (21%)]	Loss: 0.131818
Train Epoch: 5 [19200/60000 (32%)]	Loss: 0.108439
Train Epoch: 5 [25600/60000 (43%)]	Loss: 0.053640
Train Epoch: 5 [32000/60000 (53%)]	Loss: 0.082156
Train Epoch: 5 [38400/60000 (64%)]	Loss: 0.073942
Train Epoch: 5 [44800/60000 (75%)]	Loss: 0.045115
Train Epoch: 5 [51200/60000 (85%)]	Loss: 0.033417
Train Epoch: 5 [57600/60000 (96%)]	Loss: 0.184845

Test set: Average loss: 0.0402, Accuracy: 9873/10000 (99%)

Train Epoch: 6 [0/60000 (0%)]	Loss: 0.019229
Train Epoch: 6 [6400/60000 (11%)]	Loss: 0.072498
Train Epoch: 6 [12800/60000 (21%)]	Loss: 0.063877
Train Epoch: 6 [19200/60000 (32%)]	Loss: 0.068244
Train Epoch: 6 [25600/60000 (43%)]	Loss: 0.032394
Train Epoch: 6 [32000/60000 (53%)]	Loss: 0.044542
Train Epoch: 6 [38400/60000 (64%)]	Loss: 0.039451
Train Epoch: 6 [44800/60000 (75%)]	Loss: 0.038444
Train Epoch: 6 [51200/60000 (85%)]	Loss: 0.415266
Train Epoch: 6 [57600/60000 (96%)]	Loss: 0.057995

Test set: Average loss: 0.0372, Accuracy: 9893/10000 (99%)

Train Epoch: 7 [0/60000 (0%)]	Loss: 0.128391
Train Epoch: 7 [6400/60000 (11%)]	Loss: 0.088243
Train Epoch: 7 [12800/60000 (21%)]	Loss: 0.068955
Train Epoch: 7 [19200/60000 (32%)]	Loss: 0.257048
Train Epoch: 7 [25600/60000 (43%)]	Loss: 0.023842
Train Epoch: 7 [32000/60000 (53%)]	Loss: 0.060697
Train Epoch: 7 [38400/60000 (64%)]	Loss: 0.035625
Train Epoch: 7 [44800/60000 (75%)]	Loss: 0.013304
Train Epoch: 7 [51200/60000 (85%)]	Loss: 0.065375
Train Epoch: 7 [57600/60000 (96%)]	Loss: 0.120518

Test set: Average loss: 0.0405, Accuracy: 9879/10000 (99%)

Train Epoch: 8 [0/60000 (0%)]	Loss: 0.059325
Train Epoch: 8 [6400/60000 (11%)]	Loss: 0.161978
Train Epoch: 8 [12800/60000 (21%)]	Loss: 0.126335
Train Epoch: 8 [19200/60000 (32%)]	Loss: 0.032725
Train Epoch: 8 [25600/60000 (43%)]	Loss: 0.010286
Train Epoch: 8 [32000/60000 (53%)]	Loss: 0.022545
Train Epoch: 8 [38400/60000 (64%)]	Loss: 0.058781
Train Epoch: 8 [44800/60000 (75%)]	Loss: 0.015948
Train Epoch: 8 [51200/60000 (85%)]	Loss: 0.066140
Train Epoch: 8 [57600/60000 (96%)]	Loss: 0.076426

Test set: Average loss: 0.0362, Accuracy: 9896/10000 (99%)

Train Epoch: 9 [0/60000 (0%)]	Loss: 0.200360
Train Epoch: 9 [6400/60000 (11%)]	Loss: 0.129017
Train Epoch: 9 [12800/60000 (21%)]	Loss: 0.090571
Train Epoch: 9 [19200/60000 (32%)]	Loss: 0.119991
Train Epoch: 9 [25600/60000 (43%)]	Loss: 0.050012
Train Epoch: 9 [32000/60000 (53%)]	Loss: 0.038412
Train Epoch: 9 [38400/60000 (64%)]	Loss: 0.086347
Train Epoch: 9 [44800/60000 (75%)]	Loss: 0.112749
Train Epoch: 9 [51200/60000 (85%)]	Loss: 0.037908
Train Epoch: 9 [57600/60000 (96%)]	Loss: 0.063779

Test set: Average loss: 0.0354, Accuracy: 9889/10000 (99%)

In [35]:
model_2.to('cpu') 

# fetch a batch of test images
image_batch, label_batch = next(iter(test_loader))

# Turn off gradients to speed up this part
with torch.no_grad():
    log_pred_prob_batch = model_2(image_batch)
for i in range(10):
    img = image_batch[i]
    real_label = label_batch[i].item()
    log_pred_prob = log_pred_prob_batch[i]
    # Output of the network are log-probabilities, need to take exponential for probabilities
    pred_prob = torch.exp(log_pred_prob).data.numpy().squeeze()
    visualize_pred(img, pred_prob, real_label)

Does the CNN use "Visual Information" ?¶

In [36]:
fixed_perm = torch.randperm(784) # Fix a permutation of the image pixels; We apply the same permutation to all images

# show some training images
plt.figure(figsize=(8, 8))

# fetch a batch of train images; RANDOM
image_batch, label_batch = next(iter(train_loader))

for i in range(6):
    image = image_batch[i]
    image_perm = image.view(-1, 28*28).clone()
    image_perm = image_perm[:, fixed_perm]
    image_perm = image_perm.view(-1, 1, 28, 28)
    
    label = label_batch[i].item()
    plt.subplot(3,4 , 2*i + 1)
    #image, label = train_loader.dataset.__getitem__(i)
    plt.imshow(image.squeeze().numpy())
    plt.axis('off')
    plt.title(label)
    plt.subplot(3, 4, 2*i+2)
    plt.imshow(image_perm.squeeze().numpy())
    plt.axis('off')
    plt.title(label)
In [37]:
accuracy_list = []

def scramble_train(epoch, model, perm=torch.arange(0, 784).long()):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        # send to device
        data, target = data.to(device), target.to(device)
        
        # permute pixels
        data = data.view(-1, 28*28)
        data = data[:, perm]
        data = data.view(-1, 1, 28, 28)

        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))
            
def scramble_test(model, perm=torch.arange(0, 784).long()):
    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        # send to device
        data, target = data.to(device), target.to(device)
        
        # permute pixels
        data = data.view(-1, 28*28)
        data = data[:, perm]
        data = data.view(-1, 1, 28, 28)
        
        output = model(data)
        test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss                                                               
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability                                                                 
        correct += pred.eq(target.data.view_as(pred)).cpu().sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    accuracy_list.append(accuracy)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        accuracy))
In [40]:
print("Training on ", device)
model_cnn_3 = CNN(input_size, output_size)
model_cnn_3.to(device)
optimizer = optim.SGD(model_cnn_3.parameters(), lr=0.01, momentum=0.5)
print('Number of parameters: {}'.format(get_n_params(model_cnn_3)))

for epoch in range(0, 10):
    scramble_train(epoch, model_cnn_3, fixed_perm)
    scramble_test(model_cnn_3, fixed_perm)
Training on  cuda:0
Number of parameters: 66002
Train Epoch: 0 [0/60000 (0%)]	Loss: 2.293962
Train Epoch: 0 [6400/60000 (11%)]	Loss: 2.284855
Train Epoch: 0 [12800/60000 (21%)]	Loss: 2.249932
Train Epoch: 0 [19200/60000 (32%)]	Loss: 1.881541
Train Epoch: 0 [25600/60000 (43%)]	Loss: 1.234760
Train Epoch: 0 [32000/60000 (53%)]	Loss: 0.851222
Train Epoch: 0 [38400/60000 (64%)]	Loss: 0.620152
Train Epoch: 0 [44800/60000 (75%)]	Loss: 0.440660
Train Epoch: 0 [51200/60000 (85%)]	Loss: 0.549778
Train Epoch: 0 [57600/60000 (96%)]	Loss: 0.553252

Test set: Average loss: 0.3989, Accuracy: 8785/10000 (88%)

Train Epoch: 1 [0/60000 (0%)]	Loss: 0.399032
Train Epoch: 1 [6400/60000 (11%)]	Loss: 0.329888
Train Epoch: 1 [12800/60000 (21%)]	Loss: 0.500524
Train Epoch: 1 [19200/60000 (32%)]	Loss: 0.343523
Train Epoch: 1 [25600/60000 (43%)]	Loss: 0.303818
Train Epoch: 1 [32000/60000 (53%)]	Loss: 0.143065
Train Epoch: 1 [38400/60000 (64%)]	Loss: 0.163103
Train Epoch: 1 [44800/60000 (75%)]	Loss: 0.275249
Train Epoch: 1 [51200/60000 (85%)]	Loss: 0.279901
Train Epoch: 1 [57600/60000 (96%)]	Loss: 0.213570

Test set: Average loss: 0.2190, Accuracy: 9340/10000 (93%)

Train Epoch: 2 [0/60000 (0%)]	Loss: 0.271831
Train Epoch: 2 [6400/60000 (11%)]	Loss: 0.262702
Train Epoch: 2 [12800/60000 (21%)]	Loss: 0.390651
Train Epoch: 2 [19200/60000 (32%)]	Loss: 0.152644
Train Epoch: 2 [25600/60000 (43%)]	Loss: 0.264381
Train Epoch: 2 [32000/60000 (53%)]	Loss: 0.163024
Train Epoch: 2 [38400/60000 (64%)]	Loss: 0.069380
Train Epoch: 2 [44800/60000 (75%)]	Loss: 0.095450
Train Epoch: 2 [51200/60000 (85%)]	Loss: 0.222773
Train Epoch: 2 [57600/60000 (96%)]	Loss: 0.104548

Test set: Average loss: 0.2540, Accuracy: 9170/10000 (92%)

Train Epoch: 3 [0/60000 (0%)]	Loss: 0.219961
Train Epoch: 3 [6400/60000 (11%)]	Loss: 0.102320
Train Epoch: 3 [12800/60000 (21%)]	Loss: 0.199598
Train Epoch: 3 [19200/60000 (32%)]	Loss: 0.081552
Train Epoch: 3 [25600/60000 (43%)]	Loss: 0.036835
Train Epoch: 3 [32000/60000 (53%)]	Loss: 0.128813
Train Epoch: 3 [38400/60000 (64%)]	Loss: 0.202177
Train Epoch: 3 [44800/60000 (75%)]	Loss: 0.088644
Train Epoch: 3 [51200/60000 (85%)]	Loss: 0.049836
Train Epoch: 3 [57600/60000 (96%)]	Loss: 0.088481

Test set: Average loss: 0.1438, Accuracy: 9541/10000 (95%)

Train Epoch: 4 [0/60000 (0%)]	Loss: 0.115936
Train Epoch: 4 [6400/60000 (11%)]	Loss: 0.304033
Train Epoch: 4 [12800/60000 (21%)]	Loss: 0.137853
Train Epoch: 4 [19200/60000 (32%)]	Loss: 0.209812
Train Epoch: 4 [25600/60000 (43%)]	Loss: 0.087156
Train Epoch: 4 [32000/60000 (53%)]	Loss: 0.191058
Train Epoch: 4 [38400/60000 (64%)]	Loss: 0.111878
Train Epoch: 4 [44800/60000 (75%)]	Loss: 0.088515
Train Epoch: 4 [51200/60000 (85%)]	Loss: 0.144109
Train Epoch: 4 [57600/60000 (96%)]	Loss: 0.143776

Test set: Average loss: 0.1260, Accuracy: 9610/10000 (96%)

Train Epoch: 5 [0/60000 (0%)]	Loss: 0.037363
Train Epoch: 5 [6400/60000 (11%)]	Loss: 0.145014
Train Epoch: 5 [12800/60000 (21%)]	Loss: 0.127526
Train Epoch: 5 [19200/60000 (32%)]	Loss: 0.150762
Train Epoch: 5 [25600/60000 (43%)]	Loss: 0.114897
Train Epoch: 5 [32000/60000 (53%)]	Loss: 0.085731
Train Epoch: 5 [38400/60000 (64%)]	Loss: 0.232176
Train Epoch: 5 [44800/60000 (75%)]	Loss: 0.029349
Train Epoch: 5 [51200/60000 (85%)]	Loss: 0.059221
Train Epoch: 5 [57600/60000 (96%)]	Loss: 0.132286

Test set: Average loss: 0.1328, Accuracy: 9580/10000 (96%)

Train Epoch: 6 [0/60000 (0%)]	Loss: 0.129436
Train Epoch: 6 [6400/60000 (11%)]	Loss: 0.081161
Train Epoch: 6 [12800/60000 (21%)]	Loss: 0.079124
Train Epoch: 6 [19200/60000 (32%)]	Loss: 0.028536
Train Epoch: 6 [25600/60000 (43%)]	Loss: 0.093806
Train Epoch: 6 [32000/60000 (53%)]	Loss: 0.073837
Train Epoch: 6 [38400/60000 (64%)]	Loss: 0.193945
Train Epoch: 6 [44800/60000 (75%)]	Loss: 0.059927
Train Epoch: 6 [51200/60000 (85%)]	Loss: 0.024810
Train Epoch: 6 [57600/60000 (96%)]	Loss: 0.137718

Test set: Average loss: 0.1136, Accuracy: 9630/10000 (96%)

Train Epoch: 7 [0/60000 (0%)]	Loss: 0.075250
Train Epoch: 7 [6400/60000 (11%)]	Loss: 0.060729
Train Epoch: 7 [12800/60000 (21%)]	Loss: 0.032376
Train Epoch: 7 [19200/60000 (32%)]	Loss: 0.033410
Train Epoch: 7 [25600/60000 (43%)]	Loss: 0.042025
Train Epoch: 7 [32000/60000 (53%)]	Loss: 0.030652
Train Epoch: 7 [38400/60000 (64%)]	Loss: 0.019675
Train Epoch: 7 [44800/60000 (75%)]	Loss: 0.081590
Train Epoch: 7 [51200/60000 (85%)]	Loss: 0.139589
Train Epoch: 7 [57600/60000 (96%)]	Loss: 0.022650

Test set: Average loss: 0.1182, Accuracy: 9644/10000 (96%)

Train Epoch: 8 [0/60000 (0%)]	Loss: 0.055931
Train Epoch: 8 [6400/60000 (11%)]	Loss: 0.075249
Train Epoch: 8 [12800/60000 (21%)]	Loss: 0.020926
Train Epoch: 8 [19200/60000 (32%)]	Loss: 0.066193
Train Epoch: 8 [25600/60000 (43%)]	Loss: 0.038933
Train Epoch: 8 [32000/60000 (53%)]	Loss: 0.009836
Train Epoch: 8 [38400/60000 (64%)]	Loss: 0.052757
Train Epoch: 8 [44800/60000 (75%)]	Loss: 0.032170
Train Epoch: 8 [51200/60000 (85%)]	Loss: 0.009758
Train Epoch: 8 [57600/60000 (96%)]	Loss: 0.021984

Test set: Average loss: 0.1162, Accuracy: 9652/10000 (97%)

Train Epoch: 9 [0/60000 (0%)]	Loss: 0.034630
Train Epoch: 9 [6400/60000 (11%)]	Loss: 0.020086
Train Epoch: 9 [12800/60000 (21%)]	Loss: 0.009368
Train Epoch: 9 [19200/60000 (32%)]	Loss: 0.014326
Train Epoch: 9 [25600/60000 (43%)]	Loss: 0.006038
Train Epoch: 9 [32000/60000 (53%)]	Loss: 0.039993
Train Epoch: 9 [38400/60000 (64%)]	Loss: 0.019147
Train Epoch: 9 [44800/60000 (75%)]	Loss: 0.021263
Train Epoch: 9 [51200/60000 (85%)]	Loss: 0.101965
Train Epoch: 9 [57600/60000 (96%)]	Loss: 0.081789

Test set: Average loss: 0.1223, Accuracy: 9646/10000 (96%)

Performance decreased from 99% to 96%¶

In [41]:
model_cnn_3.to('cpu') 

# fetch a batch of test images
image_batch, label_batch = next(iter(test_loader))
image_batch_scramble = image_batch.view(-1, 28*28)
image_batch_scramble = image_batch_scramble[:, fixed_perm]
image_batch_scramble = image_batch_scramble.view(-1, 1, 28, 28)
# Turn off gradients to speed up this part
with torch.no_grad():
    log_pred_prob_batch = model_cnn_3(image_batch_scramble)
for i in range(10):
    img = image_batch[i]
    img_perm = image_batch_scramble[i]
    real_label = label_batch[i].item()
    log_pred_prob = log_pred_prob_batch[i]
    # Output of the network are log-probabilities, need to take exponential for probabilities
    pred_prob = torch.exp(log_pred_prob).data.numpy().squeeze()
    visualize_pred(img_perm, pred_prob, real_label)