238 lines
8.4 KiB
Python
238 lines
8.4 KiB
Python
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#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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 __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid.param_attr import ParamAttr
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__all__ = ['GoogLeNet']
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class GoogLeNet():
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def __init__(self):
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pass
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def conv_layer(self,
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input,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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name=None):
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channels = input.shape[1]
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stdv = (3.0 / (filter_size**2 * channels))**0.5
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param_attr = ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name=name + "_weights")
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conv = fluid.layers.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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act=act,
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param_attr=param_attr,
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bias_attr=False,
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name=name)
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return conv
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def xavier(self, channels, filter_size, name):
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stdv = (3.0 / (filter_size**2 * channels))**0.5
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param_attr = ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv),
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name=name + "_weights")
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return param_attr
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def inception(self,
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input,
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channels,
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filter1,
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filter3R,
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filter3,
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filter5R,
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filter5,
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proj,
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name=None):
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conv1 = self.conv_layer(
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input=input,
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num_filters=filter1,
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filter_size=1,
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stride=1,
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act=None,
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name="inception_" + name + "_1x1")
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conv3r = self.conv_layer(
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input=input,
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num_filters=filter3R,
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filter_size=1,
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stride=1,
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act=None,
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name="inception_" + name + "_3x3_reduce")
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conv3 = self.conv_layer(
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input=conv3r,
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num_filters=filter3,
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filter_size=3,
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stride=1,
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act=None,
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name="inception_" + name + "_3x3")
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conv5r = self.conv_layer(
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input=input,
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num_filters=filter5R,
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filter_size=1,
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stride=1,
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act=None,
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name="inception_" + name + "_5x5_reduce")
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conv5 = self.conv_layer(
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input=conv5r,
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num_filters=filter5,
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filter_size=5,
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stride=1,
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act=None,
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name="inception_" + name + "_5x5")
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pool = fluid.layers.pool2d(
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input=input,
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pool_size=3,
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pool_stride=1,
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pool_padding=1,
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pool_type='max')
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convprj = fluid.layers.conv2d(
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input=pool,
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filter_size=1,
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num_filters=proj,
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stride=1,
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padding=0,
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name="inception_" + name + "_3x3_proj",
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param_attr=ParamAttr(
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name="inception_" + name + "_3x3_proj_weights"),
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bias_attr=False)
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cat = fluid.layers.concat(input=[conv1, conv3, conv5, convprj], axis=1)
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cat = fluid.layers.relu(cat)
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return cat
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def net(self, input, class_dim=1000):
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conv = self.conv_layer(
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input=input,
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num_filters=64,
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filter_size=7,
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stride=2,
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act=None,
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name="conv1")
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pool = fluid.layers.pool2d(
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input=conv, pool_size=3, pool_type='max', pool_stride=2)
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conv = self.conv_layer(
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input=pool,
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num_filters=64,
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filter_size=1,
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stride=1,
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act=None,
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name="conv2_1x1")
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conv = self.conv_layer(
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input=conv,
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num_filters=192,
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filter_size=3,
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stride=1,
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act=None,
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name="conv2_3x3")
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pool = fluid.layers.pool2d(
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input=conv, pool_size=3, pool_type='max', pool_stride=2)
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ince3a = self.inception(pool, 192, 64, 96, 128, 16, 32, 32, "ince3a")
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ince3b = self.inception(ince3a, 256, 128, 128, 192, 32, 96, 64,
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"ince3b")
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pool3 = fluid.layers.pool2d(
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input=ince3b, pool_size=3, pool_type='max', pool_stride=2)
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ince4a = self.inception(pool3, 480, 192, 96, 208, 16, 48, 64, "ince4a")
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ince4b = self.inception(ince4a, 512, 160, 112, 224, 24, 64, 64,
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"ince4b")
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ince4c = self.inception(ince4b, 512, 128, 128, 256, 24, 64, 64,
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"ince4c")
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ince4d = self.inception(ince4c, 512, 112, 144, 288, 32, 64, 64,
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"ince4d")
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ince4e = self.inception(ince4d, 528, 256, 160, 320, 32, 128, 128,
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"ince4e")
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pool4 = fluid.layers.pool2d(
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input=ince4e, pool_size=3, pool_type='max', pool_stride=2)
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ince5a = self.inception(pool4, 832, 256, 160, 320, 32, 128, 128,
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"ince5a")
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ince5b = self.inception(ince5a, 832, 384, 192, 384, 48, 128, 128,
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"ince5b")
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pool5 = fluid.layers.pool2d(
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input=ince5b, pool_size=7, pool_type='avg', pool_stride=7)
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dropout = fluid.layers.dropout(x=pool5, dropout_prob=0.4)
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out = fluid.layers.fc(input=dropout,
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size=class_dim,
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act='softmax',
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param_attr=self.xavier(1024, 1, "out"),
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name="out",
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bias_attr=ParamAttr(name="out_offset"))
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pool_o1 = fluid.layers.pool2d(
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input=ince4a, pool_size=5, pool_type='avg', pool_stride=3)
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conv_o1 = self.conv_layer(
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input=pool_o1,
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num_filters=128,
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filter_size=1,
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stride=1,
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act=None,
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name="conv_o1")
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fc_o1 = fluid.layers.fc(input=conv_o1,
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size=1024,
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act='relu',
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param_attr=self.xavier(2048, 1, "fc_o1"),
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name="fc_o1",
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bias_attr=ParamAttr(name="fc_o1_offset"))
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dropout_o1 = fluid.layers.dropout(x=fc_o1, dropout_prob=0.7)
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out1 = fluid.layers.fc(input=dropout_o1,
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size=class_dim,
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act='softmax',
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param_attr=self.xavier(1024, 1, "out1"),
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name="out1",
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bias_attr=ParamAttr(name="out1_offset"))
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pool_o2 = fluid.layers.pool2d(
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input=ince4d, pool_size=5, pool_type='avg', pool_stride=3)
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conv_o2 = self.conv_layer(
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input=pool_o2,
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num_filters=128,
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filter_size=1,
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stride=1,
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act=None,
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name="conv_o2")
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fc_o2 = fluid.layers.fc(input=conv_o2,
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size=1024,
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act='relu',
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param_attr=self.xavier(2048, 1, "fc_o2"),
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name="fc_o2",
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bias_attr=ParamAttr(name="fc_o2_offset"))
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dropout_o2 = fluid.layers.dropout(x=fc_o2, dropout_prob=0.7)
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out2 = fluid.layers.fc(input=dropout_o2,
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size=class_dim,
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act='softmax',
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param_attr=self.xavier(1024, 1, "out2"),
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name="out2",
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bias_attr=ParamAttr(name="out2_offset"))
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# last fc layer is "out"
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return [out, out1, out2]
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