}
print '== training =='
# A symbolic input representing your minibatch.
minibatch = tensor.matrix()
# Allocate a denoising autoencoder with binomial noise corruption.
corruptor = GaussianCorruptor(corruption_level=conf['corruption_level'])
da = DenoisingAutoencoder(corruptor, conf['nvis'], conf['nhid'],
conf['act_enc'], conf['act_dec'],
tied_weights=conf['tied_weights'],
irange=conf['irange'])
# Allocate an optimizer, which tells us how to update our model.
# TODO: build the cost another way
cost = SquaredError(da)(minibatch, da.reconstruct(minibatch)).mean()
trainer = SGDOptimizer(da.params(), conf['base_lr'], conf['anneal_start'])
# Finally, build a Theano function out of all this.
train_fn = theano.function([minibatch], cost,
updates=trainer.cost_updates(cost))
# Suppose we want minibatches of size 10
batchsize = 10
# Here's a manual training loop. I hope to have some classes that
# automate this a litle bit.
for epoch in xrange(10):
for offset in xrange(0, train_data.shape[0], batchsize):
minibatch_err = train_fn(train_data[offset:(offset + batchsize)])
#print "epoch %d, batch %d-%d: %f" % \
#(epoch, offset, offset + batchsize - 1, minibatch_err)
