nzasif/cnn-mnist

CNN classification model built in Keras used for Digit Recognizer task on Kaggle (https://www.kaggle.com/c/digit-recognizer)

CNN for MNIST dataset

Classification of MNIST digits by means of convolutional neural networks with character shape normalization in the pre-processing phase and data augmentation by affine transformations and addition of random noise.

The code is written using Keras deep learning library.

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

Network architecture

Layer Type	Parameters	Input Size	Output Size
Input	-	28x28x1	-
Convolution (1)	64 filters, kernel 5x5, padding 2	28x28x1	28x28x64
ReLU	-	28x28x64	28x28x64
Convolution (2)	128 filters, kernel 5x5, padding 2	28x28x64	28x28x128
ReLU	-	28x28x128	28x28x128
MaxPooling (1)	stride 2	28x28x128	14x14x128
Convolution (3)	256 filters, kernel 5x5, padding 2	14x14x128	14x14x256
Convolution (4)	256 filters, kernel 3x3, padding 1	14x14x256	14x14x256
ReLU	-	14x14x256	14x14x256
MaxPooling (2)	stride 2	14x14x256	7x7x256
Dropout	0.2	-	-
Convolution (5)	512 filters, kernel 3x3, padding 1	7x7x256	7x7x512
ReLU	-	-	-
Dropout	0.2	-	-
Convolution (6)	512 filters, kernel 3x3, padding 1	7x7x512	7x7x512
ReLU	-	-	-
MaxPooling (3)	stride 2	7x7x512	3x3x512
Dropout	0.5	-	-
Fully-connected (7)	2048	4608	2048
ReLU	-	-	-
Fully-connected (8)	#classes	2048	#classes
Softmax	-	-	-