human
I am working on the file "18-11-22-Deep-Learning-with-PyTorch/03-Convolutional Neural Networks/Part 1 - Multi-layer Perceptron MNIST.ipynb".
The first task is:
Define the Network Architecture
The architecture will be responsible for seeing as input a 784-dim Tensor of pixel values for each image, and producing a Tensor of length 10 (our number of classes) that indicates the class scores for an input image. This particular example uses two hidden layers and dropout to avoid overfitting.
Can you write Python code for it?
 ... Can you provide a the Python code you requested?
assistant
#Here is the Python code you requested.

import torch.nn as nn
import torch.nn.functional as F

## TODO: Define the NN architecture
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # Linear layer (784 -> 128 hidden nodes)
        self.fc1 = nn.Linear(in_features=(28 * 28),
                             out_features=128)
        self.fc2 = nn.Linear(in_features=128,
                             out_features=10)

    def forward(self, x):
        # flatten image input
        x = x.view(-1, 28 * 28)
        # add hidden layer, with relu activation function
        x = F.relu(self.fc1(x))
        x = F.softmax(self.fc2(x), dim=1)
        return x

# initialize the NN
model = Net()
print(model)