I am trying to train a
neural network for recognizing handwritten letters from A to J . I have a training set of size 200000 . Each training set is a list of 784 pixel values. My neural net has
input layer of size 784 ,
hidden layer of size 50 and
output layer of size 10.
I am using fmin_cg minimization function of
scipy library of python. The problem I am facing is that each iteration is taking a lot of time.
- The first iteration took almost 7-10 minutes.
- The second iteration took 20 minutes.
- Third is still running.
This might be due to my outdated computer with only 2 gb of memory and a slow processor but I have previously trained a neural net with the
training set of size 5000 ,
input layer size if 400 ,
hidden layer size 25 and the
output layer of size 10 . This neural net recognized handwritten digits and it was an exercise problem of coursera course on machine learning by Andrew Ng .
So yes I know that the current neural network should take more time to train than the previous one as the training set , input layer, and hidden layer are all much larger than previous neural net but still, I think it's taking a lot of time . Why is it so slow ?
Is it normal for the neural network of this size ? Or should I use other faster optimization algorithm ? Is there a way to measure time complexity of neural networks ?