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112 lines
3.8 KiB
112 lines
3.8 KiB
9 years ago
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# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from py_paddle import swig_paddle, DataProviderWrapperConverter
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import paddle.trainer.config_parser
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from paddle.trainer.PyDataProviderWrapper import DenseSlot, IndexSlot
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import numpy
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import util
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def init_params(params):
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def init_param(p):
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assert isinstance(p, swig_paddle.Parameter)
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val = p.getBuf(swig_paddle.PARAMETER_VALUE)
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assert isinstance(val, swig_paddle.Vector)
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arr = val.toNumpyArrayInplace()
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for i in xrange(len(arr)):
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arr[i] = numpy.random.uniform(-1.0, 1.0)
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for p in params:
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init_param(p)
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def init_optimizers(opt_conf, params):
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opts = {}
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for param in params:
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param_conf = param.getConfig().toProto()
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opts[param.getID()] = swig_paddle.ParameterOptimizer.create(opt_conf)
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opts[param.getID()].init(param_conf.dims[1], param.getConfig())
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retv_opts = [None for _ in xrange(len(opts))]
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for k in opts:
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assert k < len(retv_opts)
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retv_opts[k] = opts[k]
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return retv_opts
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def main():
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trainer_config = paddle.trainer.config_parser.parse_config(
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"./testTrainConfig.py", "")
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opt_config = trainer_config.opt_config
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print "========Optimization Config ======="
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print opt_config
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print "==================================="
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opt_config = swig_paddle.OptimizationConfig.createFromProto(opt_config)
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_temp_optimizer_ = swig_paddle.ParameterOptimizer.create(opt_config)
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enable_types = _temp_optimizer_.getParameterTypes()
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m = swig_paddle.GradientMachine.createFromConfigProto(
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trainer_config.model_config, swig_paddle.CREATE_MODE_NORMAL,
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enable_types)
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assert m is not None
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assert isinstance(m, swig_paddle.GradientMachine)
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init_params(m.getParameters())
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optimizers = init_optimizers(opt_config, m.getParameters())
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# Train One Pass.
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for optimizer in optimizers:
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optimizer.startPass()
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batch_id = 0
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while True: # Train one batch
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batch_size = 1000
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inArgs, atEnd = util.loadMNISTTrainData(batch_size)
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if atEnd:
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break
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outArgs = swig_paddle.Arguments.createArguments(0)
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for optimizer in optimizers:
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optimizer.startBatch(batch_size)
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def update_callback(param):
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try:
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bufs = list(param.getBufs())
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opt = optimizers[param.getID()]
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opt.update(bufs, param.getConfig())
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callback = opt.needSpecialTraversal(param.getConfig())
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if callback is not None:
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callback(bufs, param.getConfig(), swig_paddle.NO_SPARSE_ID)
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except Exception as e:
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print e
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m.forwardBackward(inArgs, outArgs, swig_paddle.PASS_TRAIN,
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update_callback)
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for optimizer in optimizers:
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optimizer.finishBatch()
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cost_vec = outArgs.getSlotValue(0)
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assert isinstance(cost_vec, swig_paddle.Matrix)
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cost_vec = cost_vec.copyToNumpyMat()
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print 'Finish Batch', batch_id, 'with cost ', cost_vec.sum() / batch_size
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batch_id += 1
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for optimizer in optimizers:
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optimizer.finishPass()
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if __name__ == '__main__':
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swig_paddle.initPaddle("--use_gpu=0", "--trainer_count=1")
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main()
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