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Update README.md

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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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pytorch/networks/README.md
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@ -117,5 +117,73 @@ Sequential(
Congratulations you now have the network you wanted.
## Inspecting the network object
```python
print(network.__dict__)
```
The output is:
```python
{'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:
```python
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
```python
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:
```python
output = network(fake_input)
print(fake_input.shape) # -> torch.Size([111, 1, 24, 24])
print(output.shape) # -> torch.Size([111, 10])
```