AbstractNNLayer¶

class gurobi_ml.modeling.neuralnet.layers.AbstractNNLayer(gp_model, output_vars, input_vars, activation_function, **kwargs)¶

Abstract class for NN layers.

Attributes:

Methods

`get_error`([eps])	Returns error in Gurobi's solution with respect to prediction from input.
`print_stats`([abbrev, file])	Print statistics about submodel created.
`remove`()	Remove from gp_model everything that was added to embed predictor.

get_error(eps=None)¶

Returns error in Gurobi’s solution with respect to prediction from input.

Returns:: error – Assuming that we have a solution for the input and output variables x, y. Returns the absolute value of the differences between predictor.predict(x) and y. Where predictor is the regression model represented by this object.
Return type:: ndarray of same shape as gurobi_ml.modeling.base_predictor_constr.AbstractPredictorConstr.output
Raises:: NoSolution – If the Gurobi model has no solution (either was not optimized or is infeasible).

print_stats(abbrev=False, file=None)¶

Print statistics about submodel created.

Parameters:: file (None, optional) – Text stream to which output should be redirected. By default sys.stdout.

remove()¶: Remove from gp_model everything that was added to embed predictor.

property default_name¶: Default base name base used for automatic name generation.

property input¶

Input variables of embedded predictor.

property input_values¶

Values for the input variables if a solution is known.

Returns:: output_value
Return type:: ndarray or pandas dataframe with values
Raises:: NoSolution – If the Gurobi model has no solution (either was not optimized or is infeasible).

property output¶

Output variables of embedded predictor.

property output_values¶

Values for the output variables if a solution is known.

Returns:: output_value
Return type:: ndarray or pandas dataframe with values
Raises:: NoSolution – If the Gurobi model has no solution (either was not optimized or is infeasible).