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93 lines
3.0 KiB
93 lines
3.0 KiB
# Copyright (c) 2016 PaddlePaddle Authors. 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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import sys
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import paddle.v2 as paddle
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from api_v2_vgg import vgg_bn_drop
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def main():
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datadim = 3 * 32 * 32
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classdim = 10
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# PaddlePaddle init
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paddle.init(use_gpu=False, trainer_count=1)
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image = paddle.layer.data(
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name="image", type=paddle.data_type.dense_vector(datadim))
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# Add neural network config
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# option 1. resnet
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# net = resnet_cifar10(image, depth=32)
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# option 2. vgg
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net = vgg_bn_drop(image)
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out = paddle.layer.fc(input=net,
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size=classdim,
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act=paddle.activation.Softmax())
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lbl = paddle.layer.data(
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name="label", type=paddle.data_type.integer_value(classdim))
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cost = paddle.layer.classification_cost(input=out, label=lbl)
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# Create parameters
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parameters = paddle.parameters.create(cost)
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# Create optimizer
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momentum_optimizer = paddle.optimizer.Momentum(
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momentum=0.9,
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regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128),
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learning_rate=0.1 / 128.0,
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learning_rate_decay_a=0.1,
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learning_rate_decay_b=50000 * 100,
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learning_rate_schedule='discexp',
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batch_size=128)
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# End batch and end pass event handler
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def event_handler(event):
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if isinstance(event, paddle.event.EndIteration):
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if event.batch_id % 100 == 0:
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print "\nPass %d, Batch %d, Cost %f, %s" % (
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event.pass_id, event.batch_id, event.cost, event.metrics)
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else:
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sys.stdout.write('.')
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sys.stdout.flush()
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if isinstance(event, paddle.event.EndPass):
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result = trainer.test(
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reader=paddle.batch(
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paddle.dataset.cifar.test10(), batch_size=128),
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feeding={'image': 0,
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'label': 1})
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print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
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# Create trainer
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trainer = paddle.trainer.SGD(cost=cost,
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parameters=parameters,
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update_equation=momentum_optimizer)
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trainer.train(
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reader=paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.cifar.train10(), buf_size=50000),
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batch_size=128),
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num_passes=5,
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event_handler=event_handler,
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feeding={'image': 0,
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'label': 1})
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if __name__ == '__main__':
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main()
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