vgg_16_cifar.py

# Copyright (c) 2016 Baidu, Inc. 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 *

is_predict = get_config_arg("is_predict", bool, False)

####################Data Configuration ##################
if not is_predict:
  data_dir='data/cifar-out/batches/'
  meta_path=data_dir+'batches.meta'

  args = {'meta':meta_path,'mean_img_size': 32,
          'img_size': 32,'num_classes': 10,
          'use_jpeg': 1,'color': "color"}

  define_py_data_sources2(train_list="train.list",
                          test_list="train.list",
                          module='image_provider',
                          obj='processData',
                          args=args)

######################Algorithm Configuration #############
settings(
    batch_size = 128,
    learning_rate = 0.1 / 128.0,
    learning_method = MomentumOptimizer(0.9),
    regularization = L2Regularization(0.0005 * 128)
)

#######################Network Configuration #############
data_size=3*32*32
label_size=10
img = data_layer(name='image',
                 size=data_size)
# small_vgg is predefined in trainer_config_helpers.networks
predict = small_vgg(input_image=img,
                    num_channels=3,
                    num_classes=label_size)

if not is_predict:
    lbl = data_layer(name="label", size=label_size)
    outputs(classification_cost(input=predict, label=lbl))
else:
    outputs(predict)