add config_parser_utils

demo/mnist/api_train.py

@@ -12,14 +12,17 @@ import paddle.trainer.PyDataProvider2 as dp
 import numpy as np
 import random
 from mnist_util import read_from_mnist
-
-import paddle.trainer_config_helpers.config_parser as config_parser
+import paddle.trainer_config_helpers.config_parser_utils as config_parser_utils
 from paddle.trainer_config_helpers import *
 
 
 def optimizer_config():
     settings(
-        learning_rate=1e-4, learning_method=AdamOptimizer(), batch_size=1000)
+        learning_rate=1e-4,
+        learning_method=AdamOptimizer(),
+        batch_size=1000,
+        model_average=ModelAverage(average_window=0.5),
+        regularization=L2Regularization(rate=0.5))
 
 
 def network_config():
@@ -77,13 +80,14 @@ def main():
     # enable_types = [value, gradient, momentum, etc]
     # For each optimizer(SGD, Adam), GradientMachine should enable different
     # buffers.
-    opt_config_proto = config_parser.parse_optimizer_config(optimizer_config)
+    opt_config_proto = config_parser_utils.parse_optimizer_config(
+        optimizer_config)
     opt_config = api.OptimizationConfig.createFromProto(opt_config_proto)
     _temp_optimizer_ = api.ParameterOptimizer.create(opt_config)
     enable_types = _temp_optimizer_.getParameterTypes()
 
     # Create Simple Gradient Machine.
-    model_config = config_parser.parse_network_config(network_config)
+    model_config = config_parser_utils.parse_network_config(network_config)
     m = api.GradientMachine.createFromConfigProto(
         model_config, api.CREATE_MODE_NORMAL, enable_types)
 

demo/mnist/simple_mnist_network.py

-from paddle.trainer_config_helpers import *
-
-settings(
-    learning_rate=1e-4,
-    learning_method=AdamOptimizer(),
-    batch_size=1000,
-    model_average=ModelAverage(average_window=0.5),
-    regularization=L2Regularization(rate=0.5))
-
-imgs = data_layer(name='pixel', size=784)
-
-hidden1 = fc_layer(input=imgs, size=200)
-hidden2 = fc_layer(input=hidden1, size=200)
-
-inference = fc_layer(input=hidden2, size=10, act=SoftmaxActivation())
-
-cost = classification_cost(
-    input=inference, label=data_layer(
-        name='label', size=10))
-
-outputs(cost)

python/paddle/trainer_config_helpers/__init__.py

@@ -20,7 +20,7 @@ from layers import *
 from networks import *
 from optimizers import *
 from attrs import *
-from config_parser import *
+from config_parser_utils import *
 
 # This will enable operator overload for LayerOutput
 import math as layer_math

python/paddle/trainer_config_helpers/config_parser_utils.py

+# 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.
+
+import paddle.trainer.config_parser as config_parser
+'''
+This file is a wrapper of formal config_parser. The main idea of this file is to 
+separete different config logic into different function, such as network configuration
+ and optimizer configuration.
+'''
+
+__all__ = [
+    "parse_trainer_config", "parse_network_config", "parse_optimizer_config"
+]
+
+
+def parse_trainer_config(trainer_conf, config_arg_str):
+    return config_parser.parse_config(trainer_conf, config_arg_str)
+
+
+def parse_network_config(network_conf, config_arg_str=''):
+    config = config_parser.parse_config(network_conf, config_arg_str)
+    return config.model_config
+
+
+def parse_optimizer_config(optimizer_conf, config_arg_str=''):
+    config = config_parser.parse_config(optimizer_conf, config_arg_str)
+    return config.opt_config