davrot/pytutorial
1
0
Fork 0
mirror of https://github.com/davrot/pytutorial.git synced 2025-04-16 12:16:42 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Update README.md

Browse source 
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
This commit is contained in:
David Rotermund 2024-01-02 18:06:33 +01:00 • committed by GitHub
parent a02de1355e
commit cd5c813242
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 121 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

121
pytorch/networks/README.md
View file

@ -210,7 +210,128 @@ CLASS torch.nn.LazyLinear(out_features, bias=True, device=None, dtype=None)
Let us build the network layer by layer and assume we don't know **number_of_output_channels_flatten1 = 576**. But we know that the input has 1 input channel and 24x24 pixel in the spatial domain.
```python
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
fake_input = torch.zeros((1, input_number_of_channel, input_dim_x, input_dim_y))
print(fake_input.shape) # -> torch.Size([1, 1, 24, 24])
network = torch.nn.Sequential()
network.append(
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,
)
)
fake_input = network[-1](fake_input)
print(fake_input.shape) # -> torch.Size([1, 32, 20, 20])
network.append(torch.nn.ReLU())
fake_input = network[-1](fake_input)
print(fake_input.shape) # -> torch.Size([1, 32, 20, 20])
network.append(
torch.nn.MaxPool2d(
kernel_size=kernel_size_pool1, stride=stride_pool1, padding=padding_pool1
)
)
fake_input = network[-1](fake_input)
print(fake_input.shape) # -> torch.Size([1, 32, 10, 10])
network.append(
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,
)
)
fake_input = network[-1](fake_input)
print(fake_input.shape) # -> torch.Size([1, 64, 6, 6])
network.append(torch.nn.ReLU())
fake_input = network[-1](fake_input)
print(fake_input.shape) # -> torch.Size([1, 64, 6, 6])
network.append(
torch.nn.MaxPool2d(
kernel_size=kernel_size_pool2, stride=stride_pool2, padding=padding_pool2
)
)
fake_input = network[-1](fake_input)
print(fake_input.shape) # -> torch.Size([1, 64, 3, 3])
network.append(
torch.nn.Flatten(
start_dim=1,
)
)
fake_input = network[-1](fake_input)
print(fake_input.shape) # torch.Size([1, 576])
number_of_output_channels_flatten1 = fake_input.shape[1]
network.append(
torch.nn.Linear(
in_features=number_of_output_channels_flatten1,
out_features=number_of_output_channels_full1,
bias=True,
)
)
fake_input = network[-1](fake_input)
print(fake_input.shape) # torch.Size([1, 1024])
network.append(torch.nn.ReLU())
fake_input = network[-1](fake_input)
print(fake_input.shape) # torch.Size([1, 1024])
network.append(
torch.nn.Linear(
in_features=number_of_output_channels_full1,
out_features=number_of_output_channels_out,
bias=True,
)
)
fake_input = network[-1](fake_input)
print(fake_input.shape) # torch.Size([1, 10])
print(network)
```