pytutorial/pytorch/networks

Creating networks

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* TOC {:toc}

The goal

In these days, building networks is very important.

Questions to David Rotermund

A fast way to build a network with Sequential

CLASS torch.nn.Sequential(*args: Module)

A sequential container. Modules will be added to it in the order they are passed in the constructor.

Example:

We can just chain the layers together:

import torch

input_number_of_channel: int = 1
input_dim_x: int = 24
input_dim_y: int = 24

number_of_output_channels_conv1: int = 32
number_of_output_channels_conv2: int = 64
number_of_output_channels_flatten1: int
number_of_output_channels_full1: int = 1024
number_of_output_channels_out: int = 10

kernel_size_conv1: tuple[int, int] = (5, 5)
kernel_size_pool1: tuple[int, int] = (2, 2)
kernel_size_conv2: tuple[int, int] = (5, 5)
kernel_size_pool2: tuple[int, int] = (2, 2)

stride_conv1: tuple[int, int] = (1, 1)
stride_pool1: tuple[int, int] = (2, 2)
stride_conv2: tuple[int, int] = (1, 1)
stride_pool2: tuple[int, int] = (2, 2)

padding_conv1: int = 0
padding_pool1: int = 0
padding_conv2: int = 0
padding_pool2: int = 0

number_of_output_channels_flatten1 = 576

network = torch.nn.Sequential(
    torch.nn.Conv2d(
        in_channels=input_number_of_channel,
        out_channels=number_of_output_channels_conv1,
        kernel_size=kernel_size_conv1,
        stride=stride_conv1,
        padding=padding_conv1,
    ),
    torch.nn.ReLU(),
    torch.nn.MaxPool2d(
        kernel_size=kernel_size_pool1, stride=stride_pool1, padding=padding_pool1
    ),
    torch.nn.Conv2d(
        in_channels=number_of_output_channels_conv1,
        out_channels=number_of_output_channels_conv2,
        kernel_size=kernel_size_conv2,
        stride=stride_conv2,
        padding=padding_conv2,
    ),
    torch.nn.ReLU(),
    torch.nn.MaxPool2d(
        kernel_size=kernel_size_pool2, stride=stride_pool2, padding=padding_pool2
    ),
    torch.nn.Flatten(
        start_dim=1,
    ),
    torch.nn.Linear(
        in_features=number_of_output_channels_flatten1,
        out_features=number_of_output_channels_full1,
        bias=True,
    ),
    torch.nn.ReLU(),
    torch.nn.Linear(
        in_features=number_of_output_channels_full1,
        out_features=number_of_output_channels_out,
        bias=True,
    ),
)

print(network)

Sequential(
  (0): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1))
  (1): ReLU()
  (2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
  (3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))
  (4): ReLU()
  (5): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)
  (6): Flatten(start_dim=1, end_dim=-1)
  (7): Linear(in_features=576, out_features=1024, bias=True)
  (8): ReLU()
  (9): Linear(in_features=1024, out_features=10, bias=True)
)

Congratulations you now have the network you wanted.

Inspecting the network object

print(network.__dict__)

The output is:

{'training': True, 
'_parameters': OrderedDict(), 
'_buffers': OrderedDict(), 
'_non_persistent_buffers_set': set(), 
'_backward_pre_hooks': OrderedDict()
'_backward_hooks': OrderedDict(),
'_is_full_backward_hook': None, 
'_forward_hooks': OrderedDict(), 
'_forward_hooks_with_kwargs': OrderedDict(), 
'_forward_pre_hooks': OrderedDict(), 
'_forward_pre_hooks_with_kwargs': OrderedDict(),
'_state_dict_hooks': OrderedDict(), 
'_state_dict_pre_hooks': OrderedDict(), 
'_load_state_dict_pre_hooks': OrderedDict(), 
'_load_state_dict_post_hooks': OrderedDict(), 
'_modules': OrderedDict([('0', Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1))), ('1', ReLU()), ('2', MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)), ('3', Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))), ('4', ReLU()), ('5', MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)), ('6', Flatten(start_dim=1, end_dim=-1)), ('7', Linear(in_features=576, out_features=1024, bias=True)), ('8', ReLU()), ('9', Linear(in_features=1024, out_features=10, bias=True))])}

The obvious question is: What does this tell us? We see that the network is set to training mode but more importantly we can see our network architecture:

print(network.__dict__["_modules"])
```python

```python
OrderedDict([
    ('0', Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1))), 
    ('1', ReLU()), 
    ('2', MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)), 
    ('3', Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))), 
    ('4', ReLU()), 
    ('5', MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)), 
    ('6', Flatten(start_dim=1, end_dim=-1)), 
    ('7', Linear(in_features=576, out_features=1024, bias=True)), 
    ('8', ReLU()), 
    ('9', Linear(in_features=1024, out_features=10, bias=True))])

Using the network

First we need some input data

input_number_of_channel: int = 1
input_dim_x: int = 24
input_dim_y: int = 24

number_of_pattern: int = 111
fake_input = torch.rand(
    (number_of_pattern, input_number_of_channel, input_dim_x, input_dim_y),
    dtype=torch.float32,
)

Output:

output = network(fake_input)
print(fake_input.shape)  # -> torch.Size([111, 1, 24, 24])
print(output.shape)  # -> torch.Size([111, 10])