def pretrainingFunctions(self, train_set_x, batch_size, k):
"""Generates a list of functions, for performing one step of
gradient descent at a given layer. The function will require
as inputs the minibatch index, and to train an RBM you just
need to iterate, calling the corresponding function on all
minibatch indexes.
:type train_set_x: theano.tensor.TensorType
:param train_set_x: Shared var. that contains all datapoints used
for training the RBM
:type batch_size: int
:param batch_size: size of a [mini]batch
:param k: number of Gibbs steps to do in CD-k / PCD-k
"""
# index to a [mini]batch
index = T.lscalar('index') # index to a minibatch
learning_rate = T.scalar('lr') # learning rate to use
# begining of a batch, given `index`
batch_begin = index * batch_size
# ending of a batch given `index`
batch_end = batch_begin + batch_size
pretrain_fns = []
for rbm in self.rbm_layers:
# get the cost and the updates list
# using CD-k here (persisent=None) for training each RBM.
# TODO: change cost function to reconstruction error
cost, updates, gparams = rbm.getCostUpdates(learning_rate,
persistent=None, k=k)
# compile the theano function
fn = theano.function(
inputs=[index, theano.In(learning_rate, value=0.1)],
outputs=[cost]+gparams,
updates=updates,
givens={self.x: train_set_x[batch_begin:batch_end]}
)
# append `fn` to the list of functions
pretrain_fns.append(fn)
return pretrain_fns
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