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80 lines
2.7 KiB
80 lines
2.7 KiB
8 years ago
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# 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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from paddle.trainer_config_helpers import *
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is_predict = get_config_arg("is_predict", bool, False)
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####################Data Configuration ##################
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if not is_predict:
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data_dir = './data/'
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define_py_data_sources2(
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train_list=data_dir + 'train.list',
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test_list=data_dir + 'test.list',
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module='mnist_provider',
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obj='process')
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######################Algorithm Configuration #############
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settings(batch_size=50, learning_rate=0.001, learning_method=AdamOptimizer())
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#######################Network Configuration #############
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data_size = 1 * 28 * 28
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label_size = 10
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img = data_layer(name='pixel', size=data_size)
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# light cnn
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# A shallower cnn model: [CNN, BN, ReLU, Max-Pooling] x4 + FC x1
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# Easier to train for mnist dataset and quite efficient
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# Final performance is close to deeper ones on tasks such as digital and character classification
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def light_cnn(input_image, num_channels, num_classes):
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def __light__(ipt,
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num_filter=128,
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times=1,
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conv_filter_size=3,
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dropouts=0,
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num_channels_=None):
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return img_conv_group(
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input=ipt,
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num_channels=num_channels_,
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pool_size=2,
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pool_stride=2,
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conv_padding=0,
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conv_num_filter=[num_filter] * times,
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conv_filter_size=conv_filter_size,
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conv_act=ReluActivation(),
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conv_with_batchnorm=True,
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conv_batchnorm_drop_rate=dropouts,
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pool_type=MaxPooling())
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tmp = __light__(input_image, num_filter=128, num_channels_=num_channels)
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tmp = __light__(tmp, num_filter=128)
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tmp = __light__(tmp, num_filter=128)
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tmp = __light__(tmp, num_filter=128, conv_filter_size=1)
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tmp = fc_layer(input=tmp, size=num_classes, act=SoftmaxActivation())
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return tmp
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predict = light_cnn(input_image=img, num_channels=1, num_classes=label_size)
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if not is_predict:
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lbl = data_layer(name="label", size=label_size)
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inputs(img, lbl)
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outputs(classification_cost(input=predict, label=lbl))
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else:
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outputs(predict)
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