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| [submodule "book"] | ||
| path = book | ||
| url = https://github.com/PaddlePaddle/book.git |
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| # Copyright (c) 2016 PaddlePaddle Authors. 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. | ||
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| import paddle.v2 as paddle | ||
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| __all__ = ['resnet_cifar10'] | ||
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| def conv_bn_layer(input, | ||
| ch_out, | ||
| filter_size, | ||
| stride, | ||
| padding, | ||
| active_type=paddle.activation.Relu(), | ||
| ch_in=None): | ||
| tmp = paddle.layer.img_conv( | ||
| input=input, | ||
| filter_size=filter_size, | ||
| num_channels=ch_in, | ||
| num_filters=ch_out, | ||
| stride=stride, | ||
| padding=padding, | ||
| act=paddle.activation.Linear(), | ||
| bias_attr=False) | ||
| return paddle.layer.batch_norm(input=tmp, act=active_type) | ||
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| def shortcut(ipt, n_in, n_out, stride): | ||
| if n_in != n_out: | ||
| return conv_bn_layer(ipt, n_out, 1, stride, 0, | ||
| paddle.activation.Linear()) | ||
| else: | ||
| return ipt | ||
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| def basicblock(ipt, ch_out, stride): | ||
| ch_in = ch_out * 2 | ||
| tmp = conv_bn_layer(ipt, ch_out, 3, stride, 1) | ||
| tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, paddle.activation.Linear()) | ||
| short = shortcut(ipt, ch_in, ch_out, stride) | ||
| return paddle.layer.addto(input=[tmp, short], act=paddle.activation.Relu()) | ||
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| def layer_warp(block_func, ipt, features, count, stride): | ||
| tmp = block_func(ipt, features, stride) | ||
| for i in range(1, count): | ||
| tmp = block_func(tmp, features, 1) | ||
| return tmp | ||
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| def resnet_cifar10(ipt, depth=32): | ||
| # depth should be one of 20, 32, 44, 56, 110, 1202 | ||
| assert (depth - 2) % 6 == 0 | ||
| n = (depth - 2) / 6 | ||
| nStages = {16, 64, 128} | ||
| conv1 = conv_bn_layer( | ||
| ipt, ch_in=3, ch_out=16, filter_size=3, stride=1, padding=1) | ||
| res1 = layer_warp(basicblock, conv1, 16, n, 1) | ||
| res2 = layer_warp(basicblock, res1, 32, n, 2) | ||
| res3 = layer_warp(basicblock, res2, 64, n, 2) | ||
| pool = paddle.layer.img_pool( | ||
| input=res3, pool_size=8, stride=1, pool_type=paddle.pooling.Avg()) | ||
| return pool |
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| # Copyright (c) 2016 PaddlePaddle Authors. 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 | ||
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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(): | ||
| datadim = 3 * 32 * 32 | ||
| classdim = 10 | ||
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| # PaddlePaddle init | ||
| paddle.init(use_gpu=False, trainer_count=1) | ||
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| image = paddle.layer.data( | ||
| name="image", type=paddle.data_type.dense_vector(datadim)) | ||
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| # Add neural network config | ||
| # option 1. resnet | ||
| # net = resnet_cifar10(image, depth=32) | ||
| # option 2. vgg | ||
| net = vgg_bn_drop(image) | ||
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| out = paddle.layer.fc(input=net, | ||
| size=classdim, | ||
| act=paddle.activation.Softmax()) | ||
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| lbl = paddle.layer.data( | ||
| name="label", type=paddle.data_type.integer_value(classdim)) | ||
| cost = paddle.layer.classification_cost(input=out, label=lbl) | ||
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| # Create parameters | ||
| parameters = paddle.parameters.create(cost) | ||
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| # Create optimizer | ||
| momentum_optimizer = paddle.optimizer.Momentum( | ||
| momentum=0.9, | ||
| regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128), | ||
| learning_rate=0.1 / 128.0, | ||
| learning_rate_decay_a=0.1, | ||
| learning_rate_decay_b=50000 * 100, | ||
| learning_rate_schedule='discexp', | ||
| batch_size=128) | ||
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| # End batch and end pass event handler | ||
| def event_handler(event): | ||
| if isinstance(event, paddle.event.EndIteration): | ||
| if event.batch_id % 100 == 0: | ||
| print "\nPass %d, Batch %d, Cost %f, %s" % ( | ||
| event.pass_id, event.batch_id, event.cost, event.metrics) | ||
| else: | ||
| sys.stdout.write('.') | ||
| sys.stdout.flush() | ||
| if isinstance(event, paddle.event.EndPass): | ||
| result = trainer.test( | ||
| reader=paddle.batch( | ||
| paddle.dataset.cifar.test10(), batch_size=128), | ||
| feeding={'image': 0, | ||
| 'label': 1}) | ||
| print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics) | ||
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| # Create trainer | ||
| trainer = paddle.trainer.SGD(cost=cost, | ||
| parameters=parameters, | ||
| update_equation=momentum_optimizer) | ||
| trainer.train( | ||
| reader=paddle.batch( | ||
| paddle.reader.shuffle( | ||
| paddle.dataset.cifar.train10(), buf_size=50000), | ||
| batch_size=128), | ||
| num_passes=5, | ||
| event_handler=event_handler, | ||
| feeding={'image': 0, | ||
| 'label': 1}) | ||
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| if __name__ == '__main__': | ||
| main() |
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