predict_sample.py

# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
#
# 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.

from py_paddle import swig_paddle, DataProviderConverter
from paddle.trainer.PyDataProvider2 import dense_vector
from paddle.trainer.config_parser import parse_config

TEST_DATA = [[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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               0.992157, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.070588, 0.886275,
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               0.956863, 0.062745, 0, 0, 0, 0, 0, 0, 0, 0, 0.011765, 0.666667, 0.992157, 0.992157, 0.992157, 0.992157,
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def main():
    conf = parse_config("./mnist_model/trainer_config.py", "")
    print conf.data_config.load_data_args
    network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
    assert isinstance(network, swig_paddle.GradientMachine)  # For code hint.
    network.loadParameters("./mnist_model/")
    converter = DataProviderConverter([dense_vector(784)])
    inArg = converter(TEST_DATA)
    print network.forwardTest(inArg)


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