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predict_sample.py 8.6 KB
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# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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from py_paddle import swig_paddle, DataProviderConverter
from paddle.trainer.PyDataProvider2 import dense_vector
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from paddle.trainer.config_parser import parse_config
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]]]
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def main():
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    conf = parse_config("./mnist_model/trainer_config.py", "")
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    print conf.data_config.load_data_args
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    network = swig_paddle.GradientMachine.createFromConfigProto(
        conf.model_config)
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    assert isinstance(network, swig_paddle.GradientMachine)  # For code hint.
    network.loadParameters("./mnist_model/")
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    converter = DataProviderConverter([dense_vector(784)])
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    inArg = converter(TEST_DATA)
    print network.forwardTest(inArg)


if __name__ == '__main__':
    swig_paddle.initPaddle("--use_gpu=0")
    main()