def test(args):
[theta, bias] = pickle.load(open(args.model_file))
nnet = FFNeuralNetwork()
nnet.set_activation_func(args.actv)
nnet.set_output_func('softmax')
nnet.initialize(theta, bias)
data = pickle.load(open(args.test_file))
print 'Error :',nnet.test(data['X'], data['Y'])
def train(args):
tr_data = pickle.load(open(args.train_file))
vd_data = pickle.load(open(args.validation_file))
[theta, bias] = pickle.load(open(args.init_wt_file))
perf_db = DatabaseAccessor(Perf, args.model_perf_db)
perf_db.create_table()
debug_db = DatabaseAccessor(Distribution, args.debug_db)
debug_db.create_table()
nnet = FFNeuralNetwork()
nnet.set_activation_func(args.actv)
nnet.set_output_func('softmax')
nnet.initialize(theta, bias)
if args.train_layers:
nnet.set_train_layers(args.train_layers)
nnet.set_perf_writer(perf_db)
nnet.set_debug_writer(debug_db)
btheta, bbias = nnet.train(tr_data['X'], tr_data['Y'], vd_data['X'],
vd_data['Y'], args.mini_batch_size, args.epochs,
args.validation_freq)
pickle.dump([btheta,bbias], open(args.model_file, 'wb'))