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This repo shows the CNN implementation based in pytorch for the fashion mnist dataset. The basic structure of the CNN is : fashion_model( (layer1): Sequential( (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): MaxPool2d(kerne…

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Fashion_Mnist_Pytorch

This repo shows the CNN implementation based in pytorch for the fashion mnist dataset. The basic structure of the CNN is : Conv(1,16) ==>> BatchNorm ==>> MaxPool ==>> Conv(16,32) ==> BatchNorm ==>> MaxPool ==>> Fully Connected Layers

The Data set could be downloaded from the : https://github.com/zalandoresearch/fashion-mnist The Overall accuracy achieved in 10 epochs for train dataset is 93% while for test dataset it was 91%.

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This repo shows the CNN implementation based in pytorch for the fashion mnist dataset. The basic structure of the CNN is : fashion_model( (layer1): Sequential( (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() (3): MaxPool2d(kerne…

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