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77 lines
2.6 KiB
77 lines
2.6 KiB
# Copyright (c) 2018 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 .. import core
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from ..layer_helper import LayerHelper
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__all__ = ['data']
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def data(name,
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shape,
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append_batch_size=True,
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dtype='float32',
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lod_level=0,
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type=core.VarDesc.VarType.LOD_TENSOR,
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stop_gradient=True):
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"""
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**Data Layer**
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This function takes in the input and based on whether data has
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to be returned back as a minibatch, it creates the global variable by using
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the helper functions. The global variables can be accessed by all the
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following operators in the graph.
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All the input variables of this function are passed in as local variables
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to the LayerHelper constructor.
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Args:
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name(str): The name/alias of the function
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shape(list): Tuple declaring the shape.
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append_batch_size(bool): Whether or not to append the data as a batch.
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dtype(int|float): The type of data : float32, float_16, int etc
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type(VarType): The output type. By default it is LOD_TENSOR.
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lod_level(int): The LoD Level. 0 means the input data is not a sequence.
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main_program(Program): Name of the main program that calls this
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startup_program(Program): Name of the startup program
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stop_gradient(bool): A boolean that mentions whether gradient should flow.
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Returns:
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Variable: The global variable that gives access to the data.
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Examples:
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.. code-block:: python
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data = fluid.layers.data(name='x', shape=[784], dtype='float32')
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"""
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helper = LayerHelper('data', **locals())
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shape = list(shape)
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for i in xrange(len(shape)):
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if shape[i] is None:
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shape[i] = -1
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append_batch_size = False
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elif shape[i] < 0:
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append_batch_size = False
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if append_batch_size:
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shape = [-1] + shape # append batch size as -1
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return helper.create_global_variable(
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name=name,
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shape=shape,
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dtype=dtype,
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type=type,
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stop_gradient=stop_gradient,
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lod_level=lod_level)
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