Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow (Machine Learning in Python) by LazyProgrammer

Author:LazyProgrammer [Unknown]
Language: eng
Format: epub, azw3, mobi, pdf
Published: 2016-03-11T06:00:00+00:00

I create a train function similar to the simple example above:

train = theano.function(

inputs=[thX, thT],

updates=[(W1, update_W1),(W2, update_W2)],

)

And I create a prediction function to tell me the cost and prediction of my test set so I can later calculate the error rate and classification rate.

get_prediction = theano.function(

inputs=[thX, thT],

outputs=[cost, prediction],

)

And similar to the last section, I do a for-loop where I just call train() again and again until convergence. (Note that the derivative at a minimum will be 0, so at that point the weight won’t change anymore). This code uses a method called “batch gradient descent”, which iterates over batches of the training set one at a time, instead of the entire training set. This is a “stochastic” method, meaning that we hope that over a large number of samples that come from the same distribution, we will converge to a value that is optimal for all of them.

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