Neural network method of generating food formulas

One or more non-transitory computer-readable storage media storing one or more instructions programmed, when executed by one or more computing devices, cause:

• for each combination of a plurality of combinations of ingredients, representing a respective combination as a digitally stored ingredients vector, and representing features of the respective combination as a digitally stored feature vector;
• training a neural network based on a plurality of digitally stored feature vectors and a plurality of digitally stored ingredients vectors associated with the combinations of ingredients to learn a trained neural network;
• representing features of a target food item as a digitally stored feature vector;
• using the trained neural network to predict a formula for a target food item, the formula comprising a set of ingredients and proportions thereof.

A computing system comprising:

• one or more computer systems comprising one or more hardware processors and storage media;
• and
• instructions stored in the storage media and which, when executed by the computing system, cause the computing system to perform:
• representing features of a target food item as a digitally stored feature vector;
• using a neural network to predict a formula for a target food item, the formula comprising a set of ingredients and proportions thereof;
• wherein the neural network is trained by:
• for each combination of a plurality of combinations of ingredients,
• representing a respective combination as a digitally stored ingredients vector, and
• representing features of the respective combination as a digitally stored feature vector;
• training the neural network based on a plurality of digitally stored feature vectors and a plurality of digitally stored ingredients vectors associated with the combinations of ingredients to learn a trained neural network;
• wherein the trained neural network comprises N hidden layers, wherein the N hidden layers include a first hidden layer and one or more subsequent hidden layers after the first hidden layer, wherein a size of each subsequent hidden layer is a factor of a size of its previous layer.