Convolutional Neural Networks are a kind of network inspired by the cats’ visual cortex.
A cat visual cortex is made of 2 distinct type of cells:
- simple cells which specializes into edge detection.
- complex cells with larger receptive field which are sensitive to a small region of the visual field and are less sensitive to the exact position of edges.
Convolutional neural network are inspired by the latter type of cells. Each neuron is sensitive to a small region of the input data and less to a specific position of a pattern.
Continue reading “Convolutional Neural Network”
As we’ve seen in the Tensorflow introduction having access to the computation is a powerful feature. We can define any operation we’d like and tensor flow (or Theano) will compute the gradient and perform the optimisation for us. That’s great!
However if you always define the same kind of operation you’ll eventually find this approach a bit tedious. This is where we need a higher level of abstraction that allows us to define our neural net in terms of layer and not in terms of operations.
Continue reading “Keras – Tensorflow and Theano abstraction”
Today I continue my neural network post series with some considerations on neural network implementation.
So far we covered what is a neural network and how it works but we are still left with numerous choices regarding its design.
How many layers should we use, how many units (neurons) in each layer, which activation functions, which cost function, … ? There are so many questions and choices to make that it has bothered me for quite some time now.
If you scroll the web you may find some advice on these questions. But this is it – you can only get advice as there is no clear answers. It’s just trial and errors so you’d better try for yourself and see how different designs perform on your problem.
Continue reading “Neural network design”
Following my previous post on neural network I thought it would be nice to see how to implement these concepts with tensorflow.
Tensor flow is a new library developed by google. It is aimed at building fast and efficient machine learning pipelines.
Actually it is based on the computation graph that we discussed earlier.
It provides a C++ and Python interface and can run on CPU or GPU (linux only).
Continue reading “Tensorflow introduction”