Update README.md

README.md

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 # CIFAR10 LeNet5 Variation 2: GELU + Dropout Layer
 
-This repository contains our second variation of the original LeNet5 architecture adapted for CIFAR-10. The model consists of two convolutional layers followed by two fully connected layers a dropout layer (p=0.5) and a final fully connected layer, using linear (GELU) activations, extending variation 1, and Kaiming uniform initialization. It is trained with a batch size of 32 using the Adam optimizer (learning rate 0.001) and CrossEntropyLoss. In our experiments, this model achieved a test loss of 0.0539 and a top-1 accuracy of 58.52% on CIFAR-10.
+This repository contains our second variation of the original LeNet5 architecture adapted for CIFAR-10. The model consists of two convolutional layers followed by two fully connected layers a dropout layer (p=0.5) and a final fully connected layer, using linear (GELU) activations, extending variation 1, and Kaiming uniform initialization. It is trained with a batch size of 32 using the Adam optimizer (learning rate 0.001) and CrossEntropyLoss. In our experiments, this model achieved a test loss of 0.0316 and a top-1 accuracy of 64.71% on CIFAR-10.
 
 ## Model Details
 
 - **Architecture:** 2 Convolutional Layers, 2 Fully Connected Layers, 1 Dropout Layer, 1 Final Fully Connected Layer.
-- **Activations:** ReLU (might switch to tanh).
+- **Activations:** GELU.
 - **Weight Initialization:** Kaiming Uniform.
 - **Optimizer:** Adam (lr=0.001).
 - **Loss Function:** CrossEntropyLoss.