train_labels=strain.y,valid_labels=valid.y,
learning_rate=0.001,
batch_size=configs['batch_size'],
epochs=epochs,wait_for=20,
epsylon=0.0001,
aug=1.01)
X = T.matrix()
f = theano.function([X],model.fprop(X))
Y_hat = f(strain.X[:10])
Y = strain.y[:10]
for y_hat,y in zip(Y_hat,Y):
print y_hat[:2], y[:2]
y = np.vstack(sgd.iterate([testset.X],[f],configs['batch_size'])[0])
print "results shape"
print y.shape
out_path = save_path+"/test.csv"
submission = []
with open('submissionFileFormat.csv', 'rb') as cvsTemplate:
reader = csv.reader(cvsTemplate)
for row in reader:
submission.append(row)
mapping = dict(zip(['left_eye_center_x',
'left_eye_center_y',
'right_eye_center_x',
models = []
h_in, h_out = zip([trtrainset.shape[1]]+configs['hid'],configs['hid'])[i]
print h_in,h_out
model = cA.cA(numpy_rng=numpy_rng, theano_rng=theano_rng,
numvis=h_in, numhid=h_out,
activation=T.tanh,
vistype="real", contraction=configs['contract'][i])
sgd.train(trtrainset,trvalidset,model,
batch_size=configs['batch_size'],
wait_for=20,
learning_rate=configs['lr'][i],
epochs=training_epochs,
epsylon=configs['epsylon'][i],
aug=1.01)
X = T.matrix()
encoding = model.hiddens(X)
f = theano.function([X],encoding)
trtrainset = np.vstack(sgd.iterate([trtrainset],[f],configs['batch_size'])[0])
trvalidset = np.vstack(sgd.iterate([trvalidset],[f],configs['batch_size'])[0])
f = None
print "save params"
for param in model.params[:2]:
print param.name
np.save(save_path+"/layer%d%s.npy" % (i,param.name),param.get_value())
np.save(save_path+"/trainset%d.npy" % i,trtrainset)
np.save(save_path+"/validset%d.npy" % i,trvalidset)
