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@ -58,18 +58,25 @@ def full_like(input,
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**full_like**
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This function creates a tensor filled with `fill_value` which has identical shape and dtype
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with `input`.
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Args:
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input(Variable): The input tensor which specifies shape and dtype.
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fill_value: The value to fill the tensor with. Data type can be bool, float32, float64, int32, int64. Default value is 0.
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out(Variable): The output tensor.
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input(Variable): The input tensor which specifies shape and data type. The data type can be bool, float16, float32, float64, int32, int64.
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fill_value(bool|float|int): The value to fill the tensor with. Default value is 0. Note: this value shouldn't exceed the range of the output data type.
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out(Variable, optional): Optional output which can be any created Variable that meets the requirements to store the result of operation. If out is None, a new Varibale will be create to store the result. Default value is None.
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dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type of output. The default value is None, which means the output data type is the same as input.
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device (string, optional): Which device to run the operator. The :attr:`device` must be None, 'cpu', 'gpu'. If :attr:`device` is None, it will be the device that the user set in the paddle program. Default value is None.
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stop_gradient(bool, optional): Indicating if we stop gradient from current(out) Variable. Default value is True.
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name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
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Returns:
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out(Variable): The tensor variable storing the output.
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out(Variable): The Tensor variable storing the output.
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Examples:
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.. code-block:: python
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import paddle
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import paddle.fluid as fluid
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import numpy as np
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input = fluid.data(name='input', dtype='float32', shape=[2, 3])
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output = paddle.full_like(input, 2.0)
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exe = fluid.Executor(fluid.CPUPlace())
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@ -80,18 +87,24 @@ def full_like(input,
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"""
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helper = LayerHelper("full_like", **locals())
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var_dtype = None
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if dtype is None:
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dtype = 'float32'
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check_dtype(dtype, 'dtype',
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['bool', 'float16', 'float32', 'int32', 'int64'], 'full_like')
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var_dtype = input.dtype
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else:
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check_dtype(
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dtype, 'dtype',
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['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
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'full_like')
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var_dtype = convert_np_dtype_to_dtype_(dtype)
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if out is None:
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out = helper.create_variable_for_type_inference(dtype=dtype)
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helper.append_op(
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type='fill_any_like',
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inputs={'X': [input]},
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attrs={'value': fill_value},
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attrs={'value': fill_value,
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"dtype": var_dtype},
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outputs={'Out': [out]})
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out.stop_gradient = stop_gradient
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