In my experiment, I train a multilayer CNN

for street view house numbers recognition and check the accuracy of test data.

The coding is done in python using Tensorflow, a powerful library for

implementation and training deep neural networks. The central unit of data in

TensorFlow is the tensor. A tensor consists of a set of primitive values shaped

into an array of any number of dimensions. A tensor’s rank is its number of

dimensions. 20 Along with TensorFlow used some other library function such as

Numpy, Mathplotlib, SciPy etc.

Firstly, as I have technical resource

limitation I perform my analysis only using the train and test dataset. And

omit extra dataset which is 2.7GB. Secondly, to make the analysis simpler I

find and delete all those data points which have more than 5 digits in the

image. For the implementation, I randomly shuffle valid dataset I have used the

pickle file svhn_multi which I created by preprocessing the data from the

original SVHN dataset. Then used the pickle file and train a 7-layer Convoluted

Neural Network. Finally, I cast off the test data to check for accuracy of the

trained model to detect number from street house number image.

At

the very beginning of my experiment, first convolution layer I used 16 feature

maps with 5×5 filters, and originate 28x28x16 output. A few ReLU layers are

also added after each layer to add more non-linearity to the decision-making

process. After first sub-sampling the output size decrease in 14x14x10. The

second convolution has 512 feature maps with 5×5 filters and produces 10x10x32

output. In this moment applied sub-sampling second time and shrink the output

size to 5x5x32. Finally, the third convolution has 2048 feature maps with same

filter size. It is mentionable that the stride size =1 in my experiment along

with this zero padding also used here. During my experiment, I used dropout

technique to reduce the overfitting. Finally, the last layer is SoftMax

regression layer. Weights are initialized randomly using Xavier initialization

which keeps the weights in the right range. It automatically scales the

initialization based on the number of output and input neurons. Now I train the

network and log the accuracy, loss and validation accuracy in steps of 500.

Initially, I used a static learning rate of

0.01 but later switched to exponential decay learning rate with an initial

learning rate of 0.05 which decays every 10000 steps with a base of 0.95. To

minimize the loss, I used Adagrad Optimizer. When I reached a satisfactory

accuracy level for the test dataset then stop the learning and save the

hyperparameters in the cnn_multi checkpoint file. When I need to perform the detection,

it will load that time without train the model again.

Initially, the model

produced an accuracy of 89% with just 15000 steps. It’s a great starting point

and certainly, after a few hours of training the accuracy will reach my

benchmark of 90%. However, I added some simple improvements to further increase

the accuracy of few number of learning steps. First, added a dropout layer after

the third convolution layer just before fully connected layer. This allows the

network to become more robust and prevents overfitting. Secondly, introduced

exponential decay to learning rate instead of keeping it constant. This helps

the network to take bigger steps at first so that it learns fast but over time

as we move closer to the global minimum, take smaller noisier steps. With these

changes, the model is now able to produce an accuracy of 92.9% on the test set

with 15000 steps. Since there are a large training set and about 13068 images

in the test set, there is a chance of more improvement if we trained the model

for a longer