pymoose.predictors.multilayer_perceptron_predictor module#

class pymoose.predictors.multilayer_perceptron_predictor.Activation(value)[source]#

Bases: enum.Enum

An enumeration.

IDENTITY = 1#
RELU = 3#
SIGMOID = 2#
class pymoose.predictors.multilayer_perceptron_predictor.MLPClassifier(weights, biases, activation)[source]#

Bases: pymoose.predictors.multilayer_perceptron_predictor.MLPPredictor

post_transform(y, fixedpoint_dtype)[source]#
class pymoose.predictors.multilayer_perceptron_predictor.MLPPredictor(weights, biases, activation)[source]#

Bases: pymoose.predictors.predictor.Predictor

activation_fn(z)[source]#
apply_layer(input, num_hidden_layers, i, fixedpoint_dtype)[source]#
classmethod from_onnx(model_proto)[source]#
neural_predictor_fn(x, fixedpoint_dtype)[source]#
abstract post_transform(y, fixedpoint_dtype)[source]#
class pymoose.predictors.multilayer_perceptron_predictor.MLPRegressor(weights, biases, activation)[source]#

Bases: pymoose.predictors.multilayer_perceptron_predictor.MLPPredictor

post_transform(y, fixedpoint_dtype)[source]#