Dynamically managing artificial neural networks

A computer implemented method for managing a plurality of artificial neural networks in a machine learning system, comprising:

• receiving input data for a customized input data layer for the plurality of artificial neural networks, the input data having identified physical,
• organizational or chemical properties, and processing the input data by at least one convolutional layer to produce convolutionally processed input data;
• confirming a range of the identified physical, organizational or chemical properties of the input data;
• confirming that the convolutionally processed input data fits with the customized input layer;
• responsive to an indication that the convolutionally processed input data fits with the customized input layer:
• converting properties of the customized input layer into an input socket layer of the plurality of artificial neural networks, placing the properties of the customized input layer into the input socket layer of the plurality of artificial neural networks, and proceeding to prepare an output prediction using the plurality of artificial neural networks;
• and
• responsive to an indication that the convolutionally processed input data does not fit with the customized input layer, requesting at least one of an additional convolutional layer process or a training of a new neural network model, and proceeding to prepare an output prediction,
• wherein the input socket layer is configured to automatically and dynamically initiate changes to the machine learning system to accommodate the input data.

A machine learning system having a plurality of artificial neural networks and using customized layers, comprising;

• a processor coupled to memory, including a plurality of trained neural network models;
• a customized input layer coupled to an input socket layer, wherein the input socket layer is configured to provide input data to a plurality of fully connected layers of the plurality of trained neural network models, wherein the customized input layer is configured to receive the input data processed by at least one input convolutional layer;
• a customized output layer coupled to an output socket layer, wherein the output socket layer is configured to receive output data from the plurality of fully connected layers of the plurality of trained neural network models, wherein the customized output layer is configured to send the output data to at least one output convolutional layer configured to generate output data;
• and
• input fit logic operable by the processor configured to initiate automatic and dynamic changes to the machine learning system when the customized input layer is identified as not being a sufficient fit with the plurality of trained neural network models.