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)

Keypoints layer

Parameters

namestring, optional

Specifies the name of the layer.

actstring, optional

Specifies the activation function. Valid Values: AUTO, IDENTITY, LOGISTIC, SIGMOID, TANH, RECTIFIER, RELU, SOFPLUS, ELU, LEAKY, FCMP Default: AUTO

fcmp_actstring, optional

Specifies the FCMP activation function for the layer.

initstring, optional

Specifies the initialization scheme for the layer. Valid Values: XAVIER, UNIFORM, NORMAL, CAUCHY, XAVIER1, XAVIER2, MSRA, MSRA1, MSRA2 Default: XAVIER

stdfloat, optional

Specifies the standard deviation value when the init parameter is set to NORMAL.

meanfloat, optional

Specifies the mean value when the init parameter is set to NORMAL.

truncation_factorfloat, optional

Specifies the truncation threshold (truncationFactor x std), when the init parameter is set to NORMAL

init_biasfloat, optional

Specifies the initial bias for the layer.

nint, 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_biasbool, optional

Includes bias neurons (default).

target_stdint, optional

Specifies how to standardize the variables in the output layer. Valid Values: MIDRANGE, NONE, STD Default: NONE

src_layersiter-of-Layers, optional

Specifies the layers directed to this layer.

Returns

Keypoints

__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

Attributes

can_be_last_layer
kernel_size
layer_id
num_bias
num_features
num_weights
number_of_instances
output_size
rnn_summary	Return a DataFrame containing the layer information for rnn models
summary	Return a DataFrame containing the layer information
type
type_desc
type_label