Created by: wangmengzhi

我照着demo写了一个小网络，将26个数直接传到全连接层训练得到0或1，训练没问题，用CPU预测也没问题，但用GPU预测的时候执行到network.forwardTest时出现

wmz@ubuntu:~/文档/GitHub/medical$ python prediction.py 
I1217 10:56:02.716523 24392 Util.cpp:155] commandline:  --use_gpu=1 
I1217 10:56:03.042131 24392 Util.cpp:130] Calling runInitFunctions
I1217 10:56:03.042256 24392 Util.cpp:143] Call runInitFunctions done.
[INFO 2016-12-17 10:56:03,051 networks.py:1466] The input order is [data]
[INFO 2016-12-17 10:56:03,051 networks.py:1472] The output order is [__fc_layer_0__]

I1217 10:56:03.051918 24392 GradientMachine.cpp:123] Loading parameters from output_sex/pass-00029
F1217 10:56:03.052052 24392 Matrix.cpp:527] Not supported
*** Check failure stack trace: ***
    @     0x7f7b35b39daa  (unknown)
    @     0x7f7b35b39ce4  (unknown)
    @     0x7f7b35b396e6  (unknown)
    @     0x7f7b35b3c687  (unknown)
    @     0x7f7b36d5c157  paddle::GpuMatrix::mul()
    @     0x7f7b36c72600  paddle::FullyConnectedLayer::forward()
    @     0x7f7b36cd946e  paddle::NeuralNetwork::forward()
    @     0x7f7b36bb9fe5  _wrap_GradientMachine_forward
    @           0x49c4d9  (unknown)
    @           0x4a090c  (unknown)
    @           0x49ab45  (unknown)
    @           0x4a090c  (unknown)
    @           0x49ab45  (unknown)
    @           0x499ef2  (unknown)
    @           0x4a1634  (unknown)
    @           0x44e4a5  (unknown)
    @           0x44ec9f  (unknown)
    @           0x44f904  (unknown)
    @     0x7f7b389dff45  (unknown)
    @           0x578c4e  (unknown)
    @              (nil)  (unknown)
Aborted (core dumped)

网络配置文件trainer_config_sex.py是

# -*- coding: UTF-8 -*-
from paddle.trainer_config_helpers import *

is_predict = get_config_arg('is_predict', bool, False)
if not is_predict:
    define_py_data_sources2(
    	train_list='train.list',
    	test_list='test.list',
    	module="dataprovider",
    	obj='process_sex')

settings(
    batch_size=128,
    learning_rate=2e-3,
    learning_method=AdamOptimizer(),
    regularization=L2Regularization(8e-4))

data = data_layer(name="data", size=26)
output = fc_layer(input=data, size=2, act=SoftmaxActivation())
if is_predict:
    outputs(output)
else:
    label = data_layer(name="label", size=2)
    outputs(classification_cost(input=output, label=label))

预测文件prediction.py是

# -*- coding: UTF-8 -*-
from py_paddle import swig_paddle, DataProviderConverter
from paddle.trainer.PyDataProvider2 import dense_vector
from paddle.trainer.config_parser import parse_config
import numpy as np

TEST_DATA = [[[1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6,7,8,9,0,1,2,3,4,5,6]]]

def main():
    swig_paddle.initPaddle("--use_gpu=1")
    conf = parse_config("trainer_config_sex.py", "is_predict=1")
    print conf.data_config.load_data_args
    network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
    network.loadParameters("output_sex/pass-00029")
    converter = DataProviderConverter([dense_vector(26)])
    inArg = converter(TEST_DATA)
    network.forwardTest(inArg)
    output = network.getLayerOutputs("__fc_layer_0__")
    prob = output["__fc_layer_0__"].mean(0)
    lab = np.argsort(-prob)
    print lab[0]

if __name__ == '__main__':
    main()

请问错在哪？