Pytorch with the MNIST Dataset - MINST
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PyTorch Deep Explainer MNIST example
A simple example showing how to explain an MNIST CNN trained using PyTorch with Deep Explainer.
Adopted from: https://www.kaggle.com/ceshine/pytorch-deep-explainer-mnist-example
Install the modified SHAP package
```!pip install https://github.com/ceshine/shap/archive/master.zip
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Collecting https://github.com/ceshine/shap/archive/master.zip Downloading https://github.com/ceshine/shap/archive/master.zip [K | 74.1MB 155.9MB/s Requirement already satisfied (use –upgrade to upgrade): shap==0.25.0 from https://github.com/ceshine/shap/archive/master.zip in /usr/local/lib/python3.6/dist-packages Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from shap==0.25.0) (1.14.6) Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from shap==0.25.0) (1.1.0) Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from shap==0.25.0) (0.20.3) Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from shap==0.25.0) (3.0.3) Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from shap==0.25.0) (0.22.0) Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from shap==0.25.0) (4.28.1) Requirement already satisfied: ipython in /usr/local/lib/python3.6/dist-packages (from shap==0.25.0) (5.5.0) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap==0.25.0) (2.3.1) Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap==0.25.0) (2.5.3) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap==0.25.0) (1.0.1) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap==0.25.0) (0.10.0) Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->shap==0.25.0) (2018.9) Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython->shap==0.25.0) (1.0.15) Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython->shap==0.25.0) (0.7.5) Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from ipython->shap==0.25.0) (4.4.0) Requirement already satisfied: pexpect; sys_platform != “win32” in /usr/local/lib/python3.6/dist-packages (from ipython->shap==0.25.0) (4.6.0) Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython->shap==0.25.0) (2.1.3) Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython->shap==0.25.0) (40.9.0) Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython->shap==0.25.0) (0.8.1) Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython->shap==0.25.0) (4.3.2) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->shap==0.25.0) (1.11.0) Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->shap==0.25.0) (0.1.7) Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != “win32”->ipython->shap==0.25.0) (0.6.0) Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->shap==0.25.0) (0.2.0) Building wheels for collected packages: shap Building wheel for shap (setup.py) … [?25ldone [?25h Stored in directory: /tmp/pip-ephem-wheel-cache-xug5_6wp/wheels/8a/28/17/098d434a3f59f8529cb0ea4729568482332eef9127589ae8a8 Successfully built shap
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### Proceed
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```import torch, torchvision
from torchvision import datasets, transforms
from torch import nn, optim
from torch.nn import functional as F
import numpy as np
import shap
Set Parameters for Neural Network
- Convolutional Neural network followed by fully connected.
```batch_size = 128 num_epochs = 2 device = torch.device(‘cpu’)
class Net(nn.Module): def init(self): super(Net, self).init()
self.conv_layers = nn.Sequential(
nn.Conv2d(1, 10, kernel_size=5),
nn.MaxPool2d(2),
nn.ReLU(),
nn.Conv2d(10, 20, kernel_size=5),
nn.Dropout(),
nn.MaxPool2d(2),
nn.ReLU(),
)
self.fc_layers = nn.Sequential(
nn.Linear(320, 50),
nn.ReLU(),
nn.Dropout(),
nn.Linear(50, 10),
nn.Softmax(dim=1)
)
def forward(self, x):
x = self.conv_layers(x)
x = x.view(-1, 320)
x = self.fc_layers(x)
return x
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def train(model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output.log(), 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, device, test_loader): model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss(output.log(), target).item() # sum up batch loss pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
train_loader = torch.utils.data.DataLoader( datasets.MNIST(‘mnist_data’, train=True, download=True, transform=transforms.Compose([ transforms.ToTensor() ])), batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader( datasets.MNIST(‘mnist_data’, train=False, transform=transforms.Compose([ transforms.ToTensor() ])), batch_size=batch_size, shuffle=True)
model = Net().to(device) optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
for epoch in range(1, num_epochs + 1): train(model, device, train_loader, optimizer, epoch) test(model, device, test_loader)
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Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to mnist_data/MNIST/raw/train-images-idx3-ubyte.gz Extracting mnist_data/MNIST/raw/train-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to mnist_data/MNIST/raw/train-labels-idx1-ubyte.gz Extracting mnist_data/MNIST/raw/train-labels-idx1-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to mnist_data/MNIST/raw/t10k-images-idx3-ubyte.gz Extracting mnist_data/MNIST/raw/t10k-images-idx3-ubyte.gz Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to mnist_data/MNIST/raw/t10k-labels-idx1-ubyte.gz Extracting mnist_data/MNIST/raw/t10k-labels-idx1-ubyte.gz Processing… Done! Train Epoch: 1 [0/60000 (0%)] Loss: 2.302780 Train Epoch: 1 [12800/60000 (21%)] Loss: 2.191153 Train Epoch: 1 [25600/60000 (43%)] Loss: 1.284060 Train Epoch: 1 [38400/60000 (64%)] Loss: 0.900758 Train Epoch: 1 [51200/60000 (85%)] Loss: 0.818337
Test set: Average loss: 0.0050, Accuracy: 8891/10000 (89%)
Train Epoch: 2 [0/60000 (0%)] Loss: 0.652153 Train Epoch: 2 [12800/60000 (21%)] Loss: 0.740618 Train Epoch: 2 [25600/60000 (43%)] Loss: 0.725341 Train Epoch: 2 [38400/60000 (64%)] Loss: 0.542940 Train Epoch: 2 [51200/60000 (85%)] Loss: 0.454126
Test set: Average loss: 0.0029, Accuracy: 9300/10000 (93%)
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```# since shuffle=True, this is a random sample of test data
batch = next(iter(test_loader))
images, _ = batch
background = images[:100]
test_images = images[100:103]
e = shap.DeepExplainer(model, background)
shap_values = e.shap_values(test_images)
```shap_numpy = [np.swapaxes(np.swapaxes(s, 1, -1), 1, 2) for s in shap_values] test_numpy = np.swapaxes(np.swapaxes(test_images.numpy(), 1, -1), 1, 2)
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```# plot the feature attributions
shap.image_plot(shap_numpy, -test_numpy)
The plot above shows the explanations for each class on four predictions. Note that the explanations are ordered for the classes 0-9 going left to right along the rows.