def train(self): train_data, __ = utils.load_train_data() print train_data['input'].shape print train_data['output'].shape utils.save_data_as_lmdb(const.LMDB_TRAIN_WHITEN_DATA_PATH,train_data, False, True) caffe.set_mode_gpu() solver = caffe.get_solver('alexnet_solver_3.prototxt') solver.solve() pass
def train(self): train_data, __ = utils.load_train_data() print train_data['input'].shape print train_data['output'].shape utils.save_data_as_lmdb(const.LMDB_TRAIN_DATA_PATH, train_data) caffe.set_mode_gpu() solver = caffe.get_solver(const.ALEXNET_SOLVER) solver.solve() pass
def test(self): test_data, __ = utils.load_test_data() utils.save_data_as_lmdb(const.LMDB_TEST_DATA_PATH, test_data, True) result = self.__get_predicted_output('alexnet_result_2.prototxt', 'cifar3_3_iter_100000.caffemodel.h5') res = np.zeros(len(result), dtype=int) for i in xrange(len(result)): res[i] = (np.argmax(result[i])) # print res[i] # print len(res) np.savetxt("results3.csv", res.astype(dtype=int))
def train(self): train_data, __ = utils.load_train_data() print train_data['input'].shape print train_data['output'].shape # train_data['input'] = train_data['input'] / 255.0 utils.save_data_as_lmdb('cifar5_train_data_lmdb', train_data) caffe.set_mode_gpu() solver = caffe.get_solver('alexnet_solver_5.prototxt') solver.solve() pass