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python/paddle/fluid/layers/control_flow.py

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def split_lod_tensor(input, mask, level=0):
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"""
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**split_lod_tensor**
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This function takes in an input that contains the complete lod information,
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and takes in a mask which is used to mask certain parts of the input.
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The output is the true branch and the false branch with the mask applied to

python/paddle/fluid/layers/nn.py

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Args:
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input(variable): The input variable which is a LoDTensor.
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act(string, default None): Activation type, linear|relu|prelu|...
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is_test(bool, default False): Used for training or training.
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momentum(float, default 0.9):
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epsilon(float, default 1e-05):
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act(string, Default None): Activation type, linear|relu|prelu|...
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is_test(bool, Default False): Used for training or training.
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momentum(float, Default 0.9):
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epsilon(float, Default 1e-05):
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param_attr(ParamAttr): The parameter attribute for Parameter `scale`.
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bias_attr(ParamAttr): The parameter attribute for Parameter `bias`.
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data_layout(string, default NCHW): NCHW|NHWC
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in_place(bool, default False): Make the input and output of batch norm reuse memory.
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in_place(bool, Default False): Make the input and output of batch norm reuse memory.
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use_mkldnn(bool, Default false): ${use_mkldnn_comment}
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name(string, Default None): A name for this layer(optional). If set None, the layer
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will be named automatically.
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moving_mean_name(string, Default None): The name of moving_mean which store the global Mean.
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moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance.
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do_model_average_for_mean_and_var(bool, Default False):
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do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not.
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Returns:
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The sequence's last step variable which is a Tensor.
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Variable: A tensor variable which is the result after applying batch normalization on the input.
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Examples:
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