I recently coded up the neural network in your book with one hidden layer. Then, as a challenge, I coded a net that allows the user to define any number of layers with any number of nodes. It seems to work and I can repeat the results of you get when you use just one hidden layer. I want to give my net a more robust challenge though! I would be interested in trying to repeat your results above. Could you send let me know the full details of the architecture you used? Regards John
hi there - great job on adding in additional layers in a user-definable way.the architecture is simple: 300 inout nodes, 30 training epochs on the full mnist data, and with each input image rotated +/- 10 degrees to create new training datathe code for this result is on GitHub along with the rest of the examples: https://github.com/makeyourownneuralnetwork/makeyourownneuralnetwork/blob/master/part3_mnist_data_set_with_rotations.ipynbthat code will tell you the details like how we selected starting random weights, and the learning rate
and let me know how you get on with more difficult challenges like recognising faces .. which deeper networks are better at
sorry that link should have beenhttps://github.com/makeyourownneuralnetwork/makeyourownneuralnetwork/blob/master/part3_neural_network_mnist_data_with_rotations.ipynb
Thanks for the advice, I'll try to replicate your work with my code. I'd like to have a go at the face recognition problem as well. Are there any training data sets online I can use for this??
98.06 %After playing around a bit I got 98.06 % accuracy with 5 epochs and only one hidden layer containing 2000 hidden layer nodes. These are statistics of not recognized testdata:Not Recognized:0 : 61 : 42 : 223 : 144 : 195 : 156 : 197 : 358 : 319 : 29
98.17 %Another increase with the following settings (only usage of rotation changed): Hidden layers: 1Hidden layer nodes: 2000Epochs: 5(NEW) Used rotation +/- 10 degrees (as described in the book)"pixel" value range 0.0001 - 0.9999weights matrices init: np.random.rand(x, y) - 0.5 Not Recognized:0 : 61 : 62 : 203 : 104 : 195 : 166 : 137 : 308 : 399 : 24