AlexNet implementation - in progress

README.md

@@ -15,8 +15,8 @@ A collection of deep learning models implemented in PyTorch and PyTorch Lightnin
 
 Hopefully each of them will get a dedicated blog post on my humble tech blog [maczan.pl](https://maczan.pl)
 
-- [x] LeNet
-- [ ] AlexNet
+- [x] [LeNet](http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf)
+- [ ] [AlexNet](https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf) 📝 in progress 📚
 - [ ] VGG
 - [ ] ResNet
 - [ ] Inception

src/models/components/alexnet.py

@@ -0,0 +1,36 @@
+import torch
+from torch import nn
+
+
+class AlexNet(nn.Module):
+    """
+    Paper: https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
+    """
+
+    def __init__(self):
+        super().__init__()
+
+        self.model = nn.Sequential(
+            nn.Conv2d(in_channels=224, out_channels=55, kernel_size=(11, 11), stride=4),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=(5, 5)),
+            #
+            nn.Conv2d(in_channels=55, out_channels=27, kernel_size=(5, 5)),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=(3, 3)),
+            #
+            nn.Conv2d(in_channels=55, out_channels=27, kernel_size=(5, 5)),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=(3, 3)),
+            #
+            nn.Conv2d(in_channels=55, out_channels=27, kernel_size=(5, 5)),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=(3, 3)),
+            #
+            nn.Conv2d(in_channels=55, out_channels=27, kernel_size=(5, 5)),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=(3, 3)),
+            #
+            nn.ReLU(),
+            nn.Linear(in_features=128, out_features=192),
+        )