def main():
set_backend("gnumpy")
#set_gradcheck_mode(True)
######################################################
# Load MNIST dataset
tic()
data = load_mnist(digits=range(10),
split=[85,0,15],
#split=[30,0,0] # for faster training when debugging
)
print ("Data loaded in %.1fs" % toc())
######################################################
# Create a neural network with matching input/output dimensions
cfg = NeuralNetCfg(L1=1e-7*0,init_scale=0.1**2)
cfg.input(data.Xshape,dropout=0.2)
cfg.hidden(800,"logistic",dropout=0.5,maxnorm=4.0)
cfg.hidden(800,"logistic",dropout=0.5,maxnorm=4.0)
cfg.output(data.Yshape,"softmax",maxnorm=4.0)
model = NeuralNet(cfg)
######################################################
# Rescale the data to match the network's domain/range
data.rescale(model.ideal_domain(),model.ideal_range())
######################################################
# Train the network
report_args = { 'verbose' : True,
'interval' : 10, # how many epochs between progress reports (larger is faster)
'visualize' : True}
trainer = TrainingRun(model,data,report_args,
learn_rate=10,
learn_rate_decay=.998,
momentum=[(1,.5),(400,0.99)],
batchsize=128)
tic()
trainer.train(3000)
print ("Training took %.1fs" % toc())
#####################################################
if get_gradcheck_mode():
model.gradcheck(data.train)
raw_input()
