FL_Part3_5.py

# Preparing the NumPy array of the inputs.
data_inputs = numpy.array([[1, 1],
                           [1, 0],
                           [0, 1],
                           [0, 0]])

# Preparing the NumPy array of the outputs.
data_outputs = numpy.array([0, 
                            1, 
                            1, 
                            0])

model = None

def reply(self, received_data):
    global GANN_instance, data_inputs, data_outputs, model
    if (type(received_data) is dict):
        if (("data" in received_data.keys()) and ("subject" in received_data.keys())):
            subject = received_data["subject"]
            print("Client's Message Subject is {subject}.".format(subject=subject))

            print("Replying to the Client.")
            if subject == "echo":
                try:
                    data = {"subject": "model", "data": GANN_instance}
                    response = pickle.dumps(data)
                except BaseException as e:
                    print("Error Decoding the Client's Data: {msg}.\n".format(msg=e))
            elif subject == "model":
                try:
                    GANN_instance = received_data["data"]
                    best_model_idx = received_data["best_solution_idx"]

                    best_model = GANN_instance.population_networks[best_model_idx]
                    if model is None:
                        model = best_model
                    else:
                        predictions = pygad.nn.predict(last_layer=model, data_inputs=data_inputs)
                        error = numpy.sum(numpy.abs(predictions - data_outputs))

                        # In case a client sent a model to the server despite that the model error is 0.0. In this case, no need to make changes in the model.
                        if error == 0:
                            data = {"subject": "done", "data": None}
                            response = pickle.dumps(data)
                            return

                        self.model_averaging(model, best_model)

                    predictions = pygad.nn.predict(last_layer=model, data_inputs=data_inputs)
                    print("Model Predictions: {predictions}".format(predictions=predictions))

                    error = numpy.sum(numpy.abs(predictions - data_outputs))
                    print("Error = {error}".format(error=error))

                    if error != 0:
                        data = {"subject": "model", "data": GANN_instance}
                        response = pickle.dumps(data)
                    else:
                        data = {"subject": "done", "data": None}
                        response = pickle.dumps(data)

                except BaseException as e:
                    print("Error Decoding the Client's Data: {msg}.\n".format(msg=e))
            else:
                response = pickle.dumps("Response from the Server")
                            
            try:
                self.connection.sendall(response)
            except BaseException as e:
                print("Error Sending Data to the Client: {msg}.\n".format(msg=e))
        else:
            print("The received dictionary from the client must have the 'subject' and 'data' keys available. The existing keys are {d_keys}.".format(d_keys=received_data.keys()))
    else:
        print("A dictionary is expected to be received from the client but {d_type} received.".format(d_type=type(received_data)))