human
What is a the Python program you requested given the following:
I am working on the file "Homework/Principles of Artificial Neural Networks/Week 16 Visualization/pre.ipynb".
The first task is:
3 Gradient based Methods
In gradient-based algorithms, the gradient of the output with respect to the input is used for constructing the saliency maps. The algorithms in this class differ in the way the gradients are modified during backpropagation. Relevance score based algorithms try to attribute the relevance of each input pixel by backpropagating the probability score instead of the gradient. However, all of these methods involve a single forward and backward pass through the net to generate heatmaps as opposed to multiple forward passes for the perturbation based methods. Evidently, all of these methods are computationally cheaper as well as free of artifacts originating from perturbation techniques. 
3.1 Saliency Map
In this section we describe how a classification ConvNet can be queried about the spatial support of
a particular class in a given image. Given an image $I_0$, a class $c$, and a classification ConvNet with
the class score function $S_c(I)$, we would like to rank the pixels of $I_0$ based on their influence on the
score $S_c(I_0)$.
We start with a motivational example. Consider the linear score model for the class $c$:
$$S_c(I) = w^T_cI + b_c$$
where the image I is represented in the vectorised (one-dimensional) form, and wc and bc are respectively the weight vector and the bias of the model. In this case, it is easy to see that the magnitude
of elements of w defines the importance of the corresponding pixels of I for the class $c$.
In the case of deep ConvNets, the class score $S_c(I)$ is a highly non-linear function of $I$, so the
reasoning of the previous paragraph can not be immediately applied. However, given an image
$I_0$, we can approximate $S_c(I)$ with a linear function in the neighbourhood of $I_0$ by computing the
first-order Taylor expansion:
$$Sc(I) ≈ w^TI + b$$
where $w$ is the derivative of $S_c$ with respect to the image $I$ at the point (image) $I_0$:
$$w=\frac{\partial S_c}{\partial I} | I_0$$
3.1.1 Train CNN Classifier
Similarly, we build and train a CNN classifier well....
Can you write Python program for it?
assistant
#Here is the Python program you requested.

import torch
from torch.autograd import Variable
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from torch import nn, optim

class MNIST_CNN(nn.Module):
    def __init__(self):
        super(MNIST_CNN, self).__init__()
        self.layer1 = nn.Sequential(nn.Conv2d(1, 32, 3, stride=1, padding=1), nn.ReLU(True),
                                    nn.MaxPool2d(2, 2, padding=1), nn.Dropout(p=0.7))
        self.layer2 = nn.Sequential(nn.Conv2d(32, 64, 3, stride=1, padding=1), nn.ReLU(True),
                                    nn.MaxPool2d(2, 2, padding=1), nn.Dropout(p=0.7))
        self.layer3 = nn.Sequential(nn.Conv2d(64, 128, 3, stride=1, padding=1), nn.ReLU(True),
                                    nn.MaxPool2d(2, 2, padding=1), nn.Dropout(p=0.7))
        self.layer4 = nn.Sequential(nn.Linear(128 * 5 * 5, 625), nn.ReLU(True),
                                    nn.Dropout(0.5))
        self.layer5 = nn.Sequential(nn.Linear(625, 10))

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = x.view(-1, 128 * 5 * 5)
        x = self.layer4(x)
        x = self.layer5(x)
        return x