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.