ronnathaniel

# Perceptron Diagram: https://datascientest.com/en/wp-content/uploads/sites/9/2021/03/perceptron-formule.png
# 1 Neuron: 1 inputs -> 1 output
# Perceptron will be trained to learn XOR boolean operation.
# Input will be given as Vectors of length 2 in binary,
# and Output is expected as a XOR of the Input.
# What started out as a perceptron was randomly given a hidden layer, just for fun.
# Showcases the different combinations or strategies possible for Architecture.
# PyTorch and NumPy used for the model.
# Ron Nathaniel

ronnathaniel

# Perceptron Diagram: https://datascientest.com/en/wp-content/uploads/sites/9/2021/03/perceptron-formule.png
# 1 Neuron: 1 inputs -> 1 output
# Perceptron will be trained to scale numbers by a factor. 
# Input will be given as integers, and Output is expected as a scaling (in this case 2x) of the Input.
# Perceptron should produce 2x the given input.
# No Third Party modules have been used.
# Ron Nathaniel


class Perceptron:

ronnathaniel

type handler struct {
    config *epsagon.Config
}


func (h *handler) ServeHTTP(w http.ResponseWriter, r *http.Request) {
    epsagon.GoWrapper(h.config, epsagonHandler) (w, r)
}

func epsagonHandler(w http.ResponseWriter, r *http.Request) {

ronnathaniel

func post (c *gin.Context) {
	epsagon_config := newEpsagonConfig()
	epsagon.GoWrapper(epsagon_config, postEpsagon) (c)
}

func postEpsagon (c *gin.Context) {
	c.JSON(200, gin.H {
		"message": "pong back from Epsagon",
		"name": c.Param("name"),
	})