human
I am working on the file "DEEP LEARNING/Pytorch from scratch/TODO/Autoencoders/convolutional-autoencoder/Upsampling_Solution.ipynb".
The first task is:
Convolutional  Autoencoder
The encoder part of the network will be a typical convolutional pyramid. Each convolutional layer will be followed by a max-pooling layer to reduce the dimensions of the layers. The decoder though might be something new to you. The decoder needs to convert from a narrow representation to a wide reconstructed image. For example, the representation could be a 4x4x8 max-pool layer. This is the output of the encoder, but also the input to the decoder. We want to get a 28x28x1 image out from the decoder so we need to work our way back up from the narrow decoder input layer. A schematic of the network is shown below.
<img src='notebook_ims/conv_enc_2.png' width=600px>
Upsampling + Convolutions, Decoder
This decoder uses a combination of nearest-neighbor upsampling and normal convolutional layers to increase the width and height of the input layers.
It is important to note that transpose convolution layers can lead to artifacts in the final images, such as checkerboard patterns. This is due to overlap in the kernels which can be avoided by setting the stride and kernel size equal. In this Distill article from Augustus Odena, et al, the authors show that these checkerboard artifacts can be avoided by resizing the layers using nearest neighbor or bilinear interpolation (upsampling) followed by a convolutional layer. This is the approach we take, here.
TODO: Build the network shown above.

Build the encoder out of a series of convolutional and pooling layers. 
When building the decoder, use a combination of upsampling and normal, convolutional layers.
Can you write Python code for it?

Write 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 ConvAutoencoder(nn.Module):
    def __init__(self):
        super(ConvAutoencoder, self).__init__()
        ## encoder layers ##
        # conv layer (depth from 1 --> 16), 3x3 kernels
        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)  
        # conv layer (depth from 16 --> 8), 3x3 kernels
        self.conv2 = nn.Conv2d(16, 4, 3, padding=1)
        # pooling layer to reduce x-y dims by two; kernel and stride of 2
        self.pool = nn.MaxPool2d(2, 2)
        
        ## decoder layers ##
        self.conv4 = nn.Conv2d(4, 16, 3, padding=1)
        self.conv5 = nn.Conv2d(16, 1, 3, padding=1)
        

    def forward(self, x):
        # add layer, with relu activation function
        # and maxpooling after
        x = F.relu(self.conv1(x))
        x = self.pool(x)
        # add hidden layer, with relu activation function
        x = F.relu(self.conv2(x))
        x = self.pool(x)  # compressed representation
        
        ## decoder 
        # upsample, followed by a conv layer, with relu activation function  
        # this function is called `interpolate` in some PyTorch versions
        x = F.upsample(x, scale_factor=2, mode='nearest')
        x = F.relu(self.conv4(x))
        # upsample again, output should have a sigmoid applied
        x = F.upsample(x, scale_factor=2, mode='nearest')
        x = F.sigmoid(self.conv5(x))
        
        return x

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