dlpy.layers.Keypoints¶
-
class
dlpy.layers.Keypoints(name=None, act='AUTO', fcmp_act=None, init=None, std=None, mean=None, truncation_factor=None, init_bias=None, n=0, include_bias=None, target_std=None, src_layers=None, **kwargs)¶ Bases: dlpy.layers.Layer
Keypoints layer
Parameters: - name : string, optional
Specifies the name of the layer.
- act : string, optional
Specifies the activation function.
Valid Values: AUTO, IDENTITY, LOGISTIC, SIGMOID, TANH, RECTIFIER, RELU, SOFPLUS, ELU, LEAKY, FCMP
Default: AUTO- fcmp_act : string, optional
Specifies the FCMP activation function for the layer.
- init : string, optional
Specifies the initialization scheme for the layer.
Valid Values: XAVIER, UNIFORM, NORMAL, CAUCHY, XAVIER1, XAVIER2, MSRA, MSRA1, MSRA2
Default: XAVIER- std : float, optional
Specifies the standard deviation value when the init parameter is set to NORMAL.
- mean : float, optional
Specifies the mean value when the init parameter is set to NORMAL.
- truncation_factor : float, optional
Specifies the truncation threshold (truncationFactor x std), when the init parameter is set to NORMAL
- init_bias : float, optional
Specifies the initial bias for the layer.
- n : int, optional
Specifies the number of neurons in the output layer. By default, the number of neurons is automatically determined when the model training begins. The specified value cannot be smaller than the number of target variable levels.
Default: 0- include_bias : bool, optional
Includes bias neurons (default).
- target_std : int, optional
Specifies how to standardize the variables in the output layer.
Valid Values: MIDRANGE, NONE, STD
Default: NONE- src_layers : iter-of-Layers, optional
Specifies the layers directed to this layer.
Returns: -
__init__(name=None, act='AUTO', fcmp_act=None, init=None, std=None, mean=None, truncation_factor=None, init_bias=None, n=0, include_bias=None, target_std=None, src_layers=None, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__([name, act, fcmp_act, init, std, …]) Initialize self. count_instances() format_name([block_num, local_count]) Format the name of the layer get_number_of_instances() to_model_params() Convert the model configuration to CAS action parameters