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356 lines
11 KiB
356 lines
11 KiB
# 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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"""
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Before this new package paddle.v2.layer, users would need to use functions
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in paddle.trainer_config_helpers.layers to configure networks.
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The Old Way:
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=========
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This old way requires that the creation of a network be defined in a Python
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function, say network_config, and that this Python function being passed to
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paddle.trainer_config_helpers.parse_network_config for the creation of
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protobuf message description of this network.
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```python
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def network_config():
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img = paddle.trainer_config_helpers.data_layer(name="pixel", size=784)
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inference = paddle.trainer_config_helpers.fc_layer(
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input=img,
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size=10,
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act=paddle.trainer_config_helpers.SoftmaxActivation())
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cost = paddle.trainer_config_helpers.classification_cost(
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input=inference,
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label=paddle.trainer_config_helpers.data_layer(name="label", size=10))
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proto_desc = parse_network_config(network_config)
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```
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When parse_network_config executes network_config, those layer definition
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functions like data_layer and fc_layer would change some Python global variables,
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so that after the execution, parse_network_config could collect information from
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these global variables and generates the protobuf message.
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The New Way:
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=========
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In this PR, we define a function in paddle.v2.layer which creates a Python
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class for each layer creation function in paddle.trainer_config_helpers.layers.
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Users can use create a network as follows:
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```python
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img = paddle.v2.layer.data(name="pixel", size=784)
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inference = paddle.v2.layer.fc(input=img, size=10, act=paddle.v2.layer.Softmax())
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cost = paddle.v2.layer.classification(
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input=inference,
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label=paddle.v2.layer.data(name="label", size=10))
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parameters = paddle.v2.parameters.create(cost)
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```
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This new way doesn't require those invocations to layer definition functions
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to be in a Python function but could be anywhere.
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Also, the creation of a protobuf message is hidden in the invocation of
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paddle.v2.parameters.create, no longer exposed to users.
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"""
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import collections
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import paddle.trainer_config_helpers as conf_helps
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from paddle.trainer_config_helpers.config_parser_utils import \
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parse_network_config as __parse__
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from paddle.trainer_config_helpers.default_decorators import wrap_name_default
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from paddle.trainer_config_helpers.default_decorators import wrap_act_default
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from paddle.trainer_config_helpers.default_decorators import wrap_bias_attr_default
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from paddle.trainer_config_helpers.layers import layer_support
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import data_type
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import activation
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import attr
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__all__ = [
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'parse_network',
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'data',
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'fc',
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'max_id',
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'classification_cost',
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'cross_entropy_cost',
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'cross_entropy_with_selfnorm_cost',
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'regression_cost',
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'multi_binary_label_cross_entropy_cost',
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'rank_cost',
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'lambda_cost',
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'sum_cost',
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'huber_cost'
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'full_matrix_projection',
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'trans_full_matrix_projection',
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'table_projection',
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'identity_projection',
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'scaling_projection',
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'dotmul_projection',
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'context_projection',
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'conv_projection',
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]
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__projection_names__ = filter(lambda x: x.endswith('_projection'),
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dir(conf_helps))
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__all__ += __projection_names__
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__operator_names__ = filter(lambda x: x.endswith('_operator'), dir(conf_helps))
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__all__ += __operator_names__
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def parse_network(*outputs):
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"""
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parse all output layers and then generate a model config proto.
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:param outputs:
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:return:
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"""
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def __real_func__():
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context = dict()
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real_output = [each.to_proto(context=context) for each in outputs]
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conf_helps.outputs(real_output)
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return __parse__(__real_func__)
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class Layer(object):
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def __init__(self, name=None, parent_layers=None):
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assert isinstance(parent_layers, dict)
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self.name = name
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self.__parent_layers__ = parent_layers
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def to_proto(self, context):
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"""
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function to set proto attribute
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"""
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kwargs = dict()
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for layer_name in self.__parent_layers__:
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if not isinstance(self.__parent_layers__[layer_name],
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collections.Sequence):
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v1_layer = self.__parent_layers__[layer_name].to_proto(
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context=context)
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else:
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v1_layer = map(lambda x: x.to_proto(context=context),
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self.__parent_layers__[layer_name])
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kwargs[layer_name] = v1_layer
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if self.name is None:
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return self.to_proto_impl(**kwargs)
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if self.name not in context:
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context[self.name] = self.to_proto_impl(**kwargs)
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return context[self.name]
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def to_proto_impl(self, **kwargs):
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raise NotImplementedError()
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def __convert_to_v2__(method_name, name_prefix=None, parent_names=None):
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if name_prefix is not None:
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wrapper = wrap_name_default(name_prefix=name_prefix)
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else:
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wrapper = None
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class V2LayerImpl(Layer):
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def __init__(self, name=None, **kwargs):
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parent_layers = dict()
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other_kwargs = dict()
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for pname in parent_names:
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if kwargs.has_key(pname):
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parent_layers[pname] = kwargs[pname]
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for key in kwargs.keys():
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if key not in parent_names:
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other_kwargs[key] = kwargs[key]
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super(V2LayerImpl, self).__init__(name, parent_layers)
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self.__other_kwargs__ = other_kwargs
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if wrapper is not None:
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__init__ = wrapper(__init__)
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def to_proto_impl(self, **kwargs):
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args = dict()
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for each in kwargs:
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args[each] = kwargs[each]
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for each in self.__other_kwargs__:
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args[each] = self.__other_kwargs__[each]
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return getattr(conf_helps, method_name)(**args)
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return V2LayerImpl
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"""
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Some layer may need some special config, and can not use __convert_to_v2__ to convert.
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So we also need to implement some special LayerV2.
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"""
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class DataLayerV2(Layer):
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def __init__(self, name, type, **kwargs):
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assert isinstance(type, data_type.InputType)
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self.type = type
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self.__method_name__ = 'data_layer'
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self.__kwargs__ = kwargs
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super(DataLayerV2, self).__init__(name=name, parent_layers=dict())
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def to_proto_impl(self, **kwargs):
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args = dict()
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args['size'] = self.type.dim
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for each in kwargs:
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args[each] = kwargs[each]
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for each in self.__kwargs__:
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args[each] = self.__kwargs__[each]
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return getattr(conf_helps, self.__method_name__)(name=self.name, **args)
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class MixedLayerV2(Layer):
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"""
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This class is use to support `with` grammar. If not, the following code
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could convert mixed_layer simply.
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mixed = __convert_to_v2__(
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'mixed_layer', name_prefix='mixed', parent_names=['input'])
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"""
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class AddToSealedMixedLayerExceptionV2(Exception):
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pass
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def __init__(self,
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size=0,
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input=None,
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name=None,
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act=None,
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bias_attr=None,
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layer_attr=None):
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self.__method_name__ = 'mixed_layer'
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self.finalized = False
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self.__parent_layers__ = dict()
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other_kwargs = dict()
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self.input_name = 'input'
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self.__parent_layers__[self.input_name] = []
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if input is not None:
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self.__parent_layers__[self.input_name] = input
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self.name = name
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other_kwargs['size'] = size
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other_kwargs['act'] = act
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other_kwargs['bias_attr'] = bias_attr
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other_kwargs['layer_attr'] = layer_attr
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Layer.__init__(self, name, self.__parent_layers__)
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self.__other_kwargs__ = other_kwargs
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def __iadd__(self, other):
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if not self.finalized:
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self.__parent_layers__[self.input_name].append(other)
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return self
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else:
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raise MixedLayerTypeV2.AddToSealedMixedLayerExceptionV2()
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def __enter__(self):
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assert len(self.__parent_layers__[self.input_name]) == 0
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return self
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def __exit__(self, *args, **kwargs):
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self.finalized = True
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def to_proto_impl(self, **kwargs):
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args = dict()
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for each in kwargs:
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args[each] = kwargs[each]
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for each in self.__other_kwargs__:
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args[each] = self.__other_kwargs__[each]
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return getattr(conf_helps, self.__method_name__)(name=self.name, **args)
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@wrap_name_default("mixed")
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@wrap_act_default(act=activation.Linear())
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@wrap_bias_attr_default(has_bias=False)
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@layer_support(conf_helps.layers.ERROR_CLIPPING, conf_helps.layers.DROPOUT)
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def mixed(size=0,
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name=None,
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input=None,
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act=None,
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bias_attr=False,
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layer_attr=None):
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return MixedLayerV2(size, input, name, act, bias_attr, layer_attr)
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data = DataLayerV2
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fc = __convert_to_v2__('fc_layer', name_prefix='fc', parent_names=['input'])
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max_id = __convert_to_v2__(
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'maxid_layer', name_prefix='maxid', parent_names=['input'])
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classification_cost = __convert_to_v2__(
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'classification_cost',
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name_prefix='classification_cost',
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parent_names=['input', 'label', 'weight'])
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regression_cost = __convert_to_v2__(
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'regression_cost',
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name_prefix='regression_cost',
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parent_names=['input', 'label', 'weight'])
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cross_entropy_cost = __convert_to_v2__(
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'cross_entropy',
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name_prefix='cross_entropy',
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parent_names=['input', 'label'])
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cross_entropy_with_selfnorm_cost = __convert_to_v2__(
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'cross_entropy_with_selfnorm',
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name_prefix='cross_entropy_with_selfnorm',
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parent_names=['input', 'label'])
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multi_binary_label_cross_entropy_cost = __convert_to_v2__(
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'multi_binary_label_cross_entropy',
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name_prefix='multi_binary_label_cross_entropy',
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parent_names=['input', 'label'])
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rank_cost = __convert_to_v2__(
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'rank_cost',
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name_prefix='rank_cost',
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parent_names=['left', 'right', 'label', 'weight'])
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lambda_cost = __convert_to_v2__(
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'lambda_cost', name_prefix='lambda_cost', parent_names=['input', 'score'])
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sum_cost = __convert_to_v2__(
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'sum_cost', name_prefix='sum_cost', parent_names=['input'])
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huber_cost = __convert_to_v2__(
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'huber_cost', name_prefix='huber_cost', parent_names=['input', 'label'])
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# convert projection
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projection_list = [
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# [V1_method_name], all the parent_names is `input`
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'full_matrix_projection',
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'trans_full_matrix_projection',
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'table_projection',
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'scaling_projection',
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'dotmul_projection',
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'context_projection',
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'conv_projection',
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'identity_projection',
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]
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for prj in projection_list:
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globals()[prj] = __convert_to_v2__(prj, parent_names=['input'])
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# convert operator
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operator_list = [
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# [V1_method_name, parent_names],
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['dotmul_operator', ['a', 'b']],
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['conv_operator', ['img', 'filter']]
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]
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for op in operator_list:
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globals()[op[0]] = __convert_to_v2__(op[0], parent_names=op[1])
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