You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
69 lines
2.2 KiB
69 lines
2.2 KiB
# Copyright (c) 2017 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.
|
|
|
|
from paddle.trainer_config_helpers import *
|
|
|
|
################################### Data Configuration ###################################
|
|
TrainData(ProtoData(files = "trainer/tests/mnist.list"))
|
|
################################### Algorithm Configuration ###################################
|
|
settings(batch_size = 128,
|
|
learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
|
|
################################### Network Configuration ###################################
|
|
data = data_layer(name ="input", size=784)
|
|
|
|
tmp = img_conv_layer(input=data,
|
|
num_channels=1,
|
|
filter_size=3,
|
|
num_filters=32,
|
|
padding=1,
|
|
shared_biases=True,
|
|
act=ReluActivation())
|
|
|
|
tmp = img_pool_layer(input=tmp,
|
|
pool_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
pool_type=AvgPooling())
|
|
|
|
tmp = img_conv_layer(input=tmp,
|
|
filter_size=3,
|
|
num_filters=32,
|
|
padding=1,
|
|
shared_biases=True,
|
|
act=LinearActivation(),
|
|
bias_attr=False)
|
|
|
|
tmp = batch_norm_layer(input=tmp,
|
|
use_global_stats=False,
|
|
act=ReluActivation())
|
|
|
|
tmp = img_pool_layer(input=tmp,
|
|
pool_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
pool_type=MaxPooling())
|
|
|
|
tmp = fc_layer(input=tmp, size=64,
|
|
bias_attr=True,
|
|
act=ReluActivation())
|
|
|
|
output = fc_layer(input=tmp, size=10,
|
|
bias_attr=True,
|
|
act=SoftmaxActivation())
|
|
|
|
lbl = data_layer(name ="label", size=10)
|
|
|
|
cost = classification_cost(input=output, label=lbl)
|
|
outputs(cost)
|