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