Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into add-multiBatch-chunkEval-dev
guosheng
8 years ago
commit
a7fa2051f2
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import ops
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from ops import *
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import nn
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from nn import *
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import io
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from io import *
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import tensor
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from tensor import *
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import control_flow
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from control_flow import *
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__all__ = []
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__all__ += nn.__all__
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__all__ += io.__all__
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__all__ += tensor.__all__
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__all__ += control_flow.__all__
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__all__ += ops.__all__
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Load Diff
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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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main_program=None,
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startup_program=None,
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stop_gradient=True):
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"""
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Data Layer.
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Args:
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name: The name/alias of the function
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shape: Tuple declaring the shape.
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append_batch_size: Whether or not to append the data as a batch.
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dtype: The type of data : float32, float_16, int etc
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type: 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: Name of the main program that calls this
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startup_program: Name of the startup program
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stop_gradient: A boolean that mentions whether gradient should flow.
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This function takes in input and based on whether data has
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to be returned back as a minibatch, it creates the global variable using
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the helper functions. The global variables can be accessed by all the
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following operations and layers 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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"""
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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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from ..registry import register_layer
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__all__ = [
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'mean', 'mul', 'dropout', 'reshape', 'sigmoid', 'scale', 'transpose',
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'sigmoid_cross_entropy_with_logits', 'elementwise_add', 'elementwise_div',
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'elementwise_sub', 'elementwise_mul', 'clip', 'abs'
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]
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for _OP in set(__all__):
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globals()[_OP] = register_layer(_OP)
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from ..layer_helper import LayerHelper
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__all__ = [
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'create_tensor', 'cast', 'concat', 'sums', 'assign',
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'fill_constant_batch_size_like', 'fill_constant', 'ones', 'zeros'
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]
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def create_tensor(dtype, name=None, main_program=None, startup_program=None):
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helper = LayerHelper("create_tensor", **locals())
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return helper.create_variable(name=helper.name, dtype=dtype)
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def cast(x, dtype, main_program=None):
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"""
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This function takes in the input with input_dtype
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and casts it to the output_dtype as the output.
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"""
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helper = LayerHelper('cast', **locals())
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out = helper.create_tmp_variable(dtype=dtype)
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helper.append_op(
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type='cast',
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inputs={'X': [x]},
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outputs={'Out': [out]},
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attrs={'in_dtype': x.dtype,
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'out_dtype': out.dtype})
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return out
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def concat(input, axis, main_program=None, startup_program=None):
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"""
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This function concats the input along the axis mentioned
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and returns that as the output.
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"""
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helper = LayerHelper('concat', **locals())
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out = helper.create_tmp_variable(dtype=helper.input_dtype())
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helper.append_op(
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type='concat',
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inputs={'X': input},
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outputs={'Out': [out]},
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attrs={'axis': axis})
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return out
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def sums(input, out=None, main_program=None, startup_program=None):
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"""
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This function takes in the input and performs the sum operation on it
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and returns that as the output.
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"""
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helper = LayerHelper('sum', **locals())
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if out is None:
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out = helper.create_tmp_variable(dtype=helper.input_dtype())
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helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out})
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return out
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def assign(input, output, main_program=None, startup_program=None):
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helper = LayerHelper('assign', **locals())
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helper.append_op(
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type='scale',
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inputs={'X': [input]},
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outputs={'Out': [output]},
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attrs={'scale': 1.0})
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return output
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def fill_constant(shape,
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dtype,
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value,
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out=None,
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main_program=None,
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startup_program=None):
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"""
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This function creates a tensor , with shape as mentioned in the input and
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specified dtype and fills this up with a constant value that
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comes in the input. It also sets the stop_gradient to be True.
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"""
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helper = LayerHelper("fill_constant", **locals())
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if out is None:
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out = helper.create_tmp_variable(dtype=dtype)
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helper.append_op(
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type='fill_constant',
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inputs={},
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outputs={'Out': [out]},
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attrs={'shape': shape,
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'dtype': out.dtype,
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'value': float(value)})
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out.stop_gradient = True
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return out
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def fill_constant_batch_size_like(input,
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shape,
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dtype,
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value,
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input_dim_idx=0,
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output_dim_idx=0,
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main_program=None,
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startup_program=None):
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helper = LayerHelper("fill_constant_batch_size_like", **locals())
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out = helper.create_tmp_variable(dtype=dtype)
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helper.append_op(
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type='fill_constant_batch_size_like',
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inputs={'Input': input},
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outputs={'Out': [out]},
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attrs={
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'shape': shape,
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'dtype': out.dtype,
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'value': float(value),
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'input_dim_idx': input_dim_idx,
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'output_dim_idx': output_dim_idx
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})
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out.stop_gradient = True
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return out
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def ones(shape, dtype, main_program=None):
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"""
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This function performs the same function as fill_constant() declared above
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with the constant value being 1.0.
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"""
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return fill_constant(value=1.0, **locals())
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def zeros(shape, dtype, main_program=None):
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"""
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This function performs the same function as fill_constant() declared above
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with the constant value being 0.0.
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"""
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return fill_constant(value=0.0, **locals())
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