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import torch
import torchvision
from torch import nn
class TinyCNN(nn.Module):
def __init__(self, input_shape: int, hidden_units: int, output_shape: int) -> None:
super().__init__()
self.conv_block_1 = nn.Sequential(
nn.Conv2d(in_channels=input_shape,
out_channels=hidden_units,
kernel_size=3,
stride=1,
padding=0),
nn.ReLU(),
nn.Conv2d(in_channels=hidden_units,
out_channels=128,
kernel_size=3,
stride=1,
padding=0),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2,
stride=2),
nn.Dropout(p=0.25)
)
self.conv_block_2 = nn.Sequential(
nn.Conv2d(128, 128, kernel_size=3, padding=0),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, padding=0),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(p=0.25)
)
self.conv_block_3 = nn.Sequential(
nn.Conv2d(128, 128, kernel_size=3, padding=0),
nn.ReLU(),
nn.Conv2d(128, 512, kernel_size=3, padding=0),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(p=0.25)
)
self.conv_block_4 = nn.Sequential(
nn.Conv2d(512, 512, kernel_size=3, padding=0),
nn.ReLU(),
nn.Conv2d(512, 512, kernel_size=3, padding=0),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(p=0.25)
)
self.fc_1 = nn.Sequential(
nn.Flatten(),
nn.Linear(in_features=512*16, out_features = 512),
nn.BatchNorm1d(512),
nn.ReLU(),
nn.Dropout(p=0.25)
)
self.fc_2 = nn.Sequential(
nn.Linear(in_features=512,
out_features=256),
nn.BatchNorm1d(256),
nn.ReLU(),
nn.Dropout(p=0.25)
)
self.classifier = nn.Sequential(
nn.Linear(in_features=256,
out_features=output_shape)
)
def forward(self, x):
x = self.conv_block_1(x)
x = self.conv_block_2(x)
x = self.conv_block_3(x)
x = self.conv_block_4(x)
x = self.fc_1(x)
x = self.fc_2(x)
x = self.classifier(x)
return x
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