Creating a sequential block

You decided to redesign your binary CNN model template by creating a block of convolutional layers. This will help you stack multiple layers sequentially. With this improved model, you will be able to easily design various CNN architectures.

torch and torch.nn as nn have been imported.

This exercise is part of the course

Deep Learning for Images with PyTorch

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Exercise instructions

In the __init__() method, define a block of convolutional layers and assign it to self.conv_block.
In the forward() pass, pass the inputs through the convolutional block you defined.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

class BinaryImageClassification(nn.Module):
  def __init__(self):
    super(BinaryImageClassification, self).__init__()
    # Create a convolutional block
    self.conv_block = ____(
      nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
      nn.ReLU(),
      nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
      nn.ReLU(),
    )
    
  def forward(self, x):
    # Pass inputs through the convolutional block
    x = ____
    return x

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