dlpy.layers.InputLayer

class dlpy.layers.InputLayer(n_channels=None, width=None, height=None, name=None, nominals=None, std=None, scale=None, offsets=None, dropout=None, random_flip=None, random_crop=None, random_mutation=None, norm_stds=None, **kwargs)

Input layer

Parameters

namestring

Specifies the name of the input layer.

nominalsstring-list, optional

Specifies the nominal input variables to use in the analysis.

stdstring, optional

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

n_channelsint, optional for rnn required for cnn

Specifies the depth of the input data, used if data is image.

widthint, optional for rnn required for cnn

Specifies the width of the input data, used if data is image.

heightint, optional

Specifies the height of the input data, used if data is image. Note: Required for CNN.

scalefloat, optional,

Specifies the scale to be used to scale the input data.

offsetsint-list, optional

Specifies the values to be subtracted from the pixel values of the input data, used if the data is image.

dropoutfloat, optional

Specifies the dropout rate.

random_flipint, optional

Specifies the type of the random flip to be applied to the input data, used if data is image. Valid Values: NONE, H, V, HV Default: NONE

random_cropint, optional

Specifies the type of the random crop to be applied to the input data, used if data is image. Valid Values: NONE, UNIQUE Default: NONE

random_mutationint, optional

Specifies the type of the random mutation to be applied to the input data, used if data is image. Valid Values: NONE, RANDOM Default: NONE

norm_stdsfloat-list, optional

Specifies a standard deviation for each channel in the input data. The final input data is normalized with specified means and standard deviations. Default: NONE

Returns

InputLayer

__init__(n_channels=None, width=None, height=None, name=None, nominals=None, std=None, scale=None, offsets=None, dropout=None, random_flip=None, random_crop=None, random_mutation=None, norm_stds=None, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([n_channels, width, height, name, …])	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_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