import gnumpy as gp
import LNFuncs as lnf
import LNLayer as lnl
import LNNet as lnn
from time import clock as clock
if __name__ == '__main__':
# Load usps data
X = np.load('usps_X_numpy.npy')
Y = np.load('usps_Y_numpy.npy')
X = X - np.reshape(np.mean(X,axis=1), (X.shape[0], 1))
X = X / np.reshape(np.max(np.abs(X),axis=1), (X.shape[0], 1))
# Split into random training/test portions (note: lnf.trte_split() returns
# gp.garrays, i.e. arrays on the GPU)
[Xtr, Ytr, Xte, Yte] = lnf.trte_split(X, Y, 0.8)
# Configure network layer sizes
obs_dim = X.shape[1]
out_dim = Y.shape[1]
hidden_size = 200
layer_sizes = [obs_dim, hidden_size, hidden_size, out_dim]
# Set some training options
opts = lnf.check_opts()
opts['rounds'] = 15000
opts['batch_size'] = 100
opts['start_rate'] = 0.1
opts['momentum'] = 0.9
opts['decay_rate'] = 0.1**(1.0 / opts['rounds'])
opts['dev_reps'] = 2
opts['do_validate'] = 1
opts['Xv'] = Xte
