Device, data structure, and computer implemented method for configuring a model

A device for configuring a model, comprising:

• at least one processor;
• and
• at least one non-transitory memory, wherein the at least one non-transitory memory includes instructions that are executable by the at least one processor, and that, when executed by the at least one processor cause the device to execute a method for configuring a model, the method including the following steps:
• providing the model, the model being configured to determine an output of the model depending on an output of a layer of the model, wherein the layer is configured to map a multidimensional input of the layer depending on trained weights to a multidimensional output of the layer,
• iteratively arranging the trained weights in vectors of a first matrix,
• determining a second matrix by removing at least one vector from the first matrix,
• determining an input of reduced dimensions by removing from the multidimensional input a dimension that corresponds or dimensions that correspond to the at least one vector,
• configuring the model with a layer of reduced dimensions that is configured to map the input of reduced dimensions depending on weights from the second matrix to an output of reduced dimensions,
• configuring the model for determining the output of the model depending on the output of reduced dimensions,
• providing training data,
• training weights of the layer of reduced dimensions on the training data, and
• providing the trained weights of the layer of reduced dimensions for the first matrix.

A non-transitory computer-readable medium on which is stored a computer program including instructions for configuring a model, the instructions, when executed by a computer, causing the computer to perform the following steps comprising:

• providing the model, the model being configured to determine an output of the model depending on an output of a layer of the model, wherein the layer is configured to map a multidimensional input of the layer depending on trained weights to a multidimensional output of the layer;
• iteratively arranging the trained weights in vectors of a first matrix;
• determining a second matrix by removing at least one vector from the first matrix;
• determining an input of reduced dimensions by removing from the multidimensional input a dimension that corresponds or dimensions that correspond to the at least one vector;
• configuring the model with a layer of reduced dimensions that is configured to map the input of reduced dimensions depending on weights from the second matrix to an output of reduced dimensions;
• configuring the model for determining the output of the model depending on the output of reduced dimensions;
• providing training data;
• training weights of the layer of reduced dimensions on the training data;
• and
• providing the trained weights of the layer of reduced dimensions for the first matrix.

A computer implemented data structure, for configuring a model, the data structing comprising:

• at least one data field for the model, the model being configured to determine an output of the model depending on an output of a layer of the model, wherein the layer is configured to map a multidimensional input of the layer depending on trained weights to a multidimensional output of the layer′
• at least one data field for iteratively arranging the trained weights in vectors of a first matrix;
• at least one data field for a second matrix determined by removing at least one vector from the first matrix;
• at least one data field for an input of reduced dimensions determined by removing from the multidimensional input a dimension that corresponds or dimensions that correspond to the at least one vector;
• at least one data field for the configured model, wherein the model is configured with a layer of reduced dimensions that is configured to map the input of reduced dimensions depending on weights from the second matrix to an output of reduced dimensions, and wherein the model is configured for determining the output of the model depending on the output of reduced dimensions;
• and
• at least one data field for training data.