human
I am working on the file "DEEP LEARNING/Pytorch from scratch/MLP/mnist_mlp_with_val.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?

Give me 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

# define the NN architecture
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # number of hidden nodes in each layer (512)
        hidden_1 = 512
        hidden_2 = 512
        # linear layer (784 -> hidden_1)
        self.fc1 = nn.Linear(28 * 28, hidden_1)
        # linear layer (n_hidden -> hidden_2)
        self.fc2 = nn.Linear(hidden_1, hidden_2)
        # linear layer (n_hidden -> 10)
        self.fc3 = nn.Linear(hidden_2, 10)
        # dropout layer (p=0.2)
        # dropout prevents overfitting of data
        self.dropout = nn.Dropout(0.2)

    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))
        # add dropout layer
        x = self.dropout(x)
        # add hidden layer, with relu activation function
        x = F.relu(self.fc2(x))
        # add dropout layer
        x = self.dropout(x)
        # add output layer
        x = self.fc3(x)
        return x

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