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, we used a static learning rate of 0.01 but

later on 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. Also used

Adagrad Optimizer to minimize loss. We stop learning when we reach adequate

accuracy level for the test dataset and we save the hyperparameters in

cnn_multi checkpoint file so that it can be loaded later when we need to

perform detection without training the model again. Refinement The initial

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 further made some simple improvements to further

increase the accuracy of few number of learning steps. 1. Added a dropout layer

to the network after the third convolution layer just before fully connected

layer, which randomly drops weights from the network with a keep probability of

0.9375 to add more redundancy to the network. This allows the network to become

more robust and prevents overfitting. 2. 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 230070 images in training set and about 13068 images in the

test set, the model is expected to improve further if it is trained for a

longer duration.