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CNN with Inception Modules for MNIST dataset

Classification of MNIST digits by convolutional neural networks (CNN) with Dropout and Inception layers. No preprocessing done.

The code is written using Tenserflow.

Used in Digit Recognizer competition on Kaggle https://www.kaggle.com/c/digit-recognizer

Network architecture

Layer Type Kernel Size / Stride Output Size #1×1 #3×3 reduce #3×3 #5×5 reduce #5×5 pool proj
Input - 28x28x1
Conv 5 x 5 / 1 28x28x64 64
Conv 3 x 3 / 1 28x28x128 128
MaxPool 3 x 3 / 2 14x14x128
Norm 14x14x128
Inception 14x14x128 8 64 96 8 16 8
Inception 14x14x256 64 96 128 16 32 32
Inception 14x14x512 160 112 224 24 64 64
MaxPool 3 x 3 / 2 7x7x512
Norm 7x7x512
Inception 7x7x512 128 128 256 32 64 64
AvgPool 7 x 7 / 7 1x1x512
Dropout 1x1x512
FC 1x1x10
Softmax 1x1x10

Accuracy of the model: 0.99261 with my test set and 0.99428 in Kaggle competition.

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