Tin Theethawat Savastham

♻️ Split Function for Implement and Model Directory

4ec8622 over 1 year ago

3.25 kB

import numpy as np


# https://medium.com/towards-data-science/math-neural-network-from-scratch-in-python-d6da9f29ce65
class Network:
    def __init__(self):
        self.layers = []
        self.loss = None
        self.loss_prime = None
        self.weight_list = []

    def add(self, layer):
        self.layers.append(layer)

    def use(self, loss, loss_prime):
        self.loss = loss
        self.loss_prime = loss_prime

    def fit(self, x_train, y_train, epochs, learning_rate):
        samples = len(x_train)

        # Train epoch times
        for i in range(epochs):
            err = 0

            # train for all samples
            for j in range(samples):
                input = x_train[j]
                output = None

                # predict data at all layer
                for layer in self.layers:
                    output = layer.forward_propagation(input)
                    # output as input of next layer
                    input = output

                # Find loss for display
                err += self.loss(y_train[j], output)

                # Find Error of output dE/dY using derivation of MSE
                error = self.loss_prime(y_train[j], output)
                # Update Weight for next data
                # By find gradient of weight in each layer and update
                for layer in reversed(self.layers):
                    error = layer.backward_propagation(error, learning_rate)

            # Find Average Error per sample
            err /= samples
            print("Epoch %d/%d calculate with error = %f" %
                  (i + 1, epochs, err))
            print(f"Update weight to {layer.get_weight()} ")
            print(f"Update Bias to {layer.get_bias()}")
            print("")

    def fit_on_sample(self):
        raise NotImplementedError

    def get_weight_list(self):
        return self.weight_list

    def get_weights(self):
        raise NotImplementedError

    def get_biases(self):
        result = []
        for layer in self.layers:
            result.append(layer.get_bias())
        return result

    def predict_sample(self, first_input, second_input):
        output = None

        # predict data at all layer
        for layer in self.layers:
            output = layer.forward_propagation(first_input, second_input)
            # output as input of next layer
            first_input = output

        return output

    def back_propagate(self, error, learning_rate):
        # weight = []
        for layer in reversed(self.layers):
            error = layer.backward_propagation(error, learning_rate)
            # weight.append(layer.get_weight())
        # print(f'Epoch calculate with error = {err}')

    # self.weight_list = np.append(self.weight_list, [weight])

    def predict(self, first_input, second_input):
        output = None

        # predict data at all layer
        for layer in self.layers:
            output = layer.predict(first_input, second_input)
            # output as input of next layer
            first_input = output

        return output

    def check_type(self):
        print(
            "It is in the Initial Class Network Please call this function on the child class"
        )
        return False

	import numpy as np


	# https://medium.com/towards-data-science/math-neural-network-from-scratch-in-python-d6da9f29ce65
	class Network:
	def __init__(self):
	self.layers = []
	self.loss = None
	self.loss_prime = None
	self.weight_list = []

	def add(self, layer):
	self.layers.append(layer)

	def use(self, loss, loss_prime):
	self.loss = loss
	self.loss_prime = loss_prime

	def fit(self, x_train, y_train, epochs, learning_rate):
	samples = len(x_train)

	# Train epoch times
	for i in range(epochs):
	err = 0

	# train for all samples
	for j in range(samples):
	input = x_train[j]
	output = None

	# predict data at all layer
	for layer in self.layers:
	output = layer.forward_propagation(input)
	# output as input of next layer
	input = output

	# Find loss for display
	err += self.loss(y_train[j], output)

	# Find Error of output dE/dY using derivation of MSE
	error = self.loss_prime(y_train[j], output)
	# Update Weight for next data
	# By find gradient of weight in each layer and update
	for layer in reversed(self.layers):
	error = layer.backward_propagation(error, learning_rate)

	# Find Average Error per sample
	err /= samples
	print("Epoch %d/%d calculate with error = %f" %
	(i + 1, epochs, err))
	print(f"Update weight to {layer.get_weight()} ")
	print(f"Update Bias to {layer.get_bias()}")
	print("")

	def fit_on_sample(self):
	raise NotImplementedError

	def get_weight_list(self):
	return self.weight_list

	def get_weights(self):
	raise NotImplementedError

	def get_biases(self):
	result = []
	for layer in self.layers:
	result.append(layer.get_bias())
	return result

	def predict_sample(self, first_input, second_input):
	output = None

	# predict data at all layer
	for layer in self.layers:
	output = layer.forward_propagation(first_input, second_input)
	# output as input of next layer
	first_input = output

	return output

	def back_propagate(self, error, learning_rate):
	# weight = []
	for layer in reversed(self.layers):
	error = layer.backward_propagation(error, learning_rate)
	# weight.append(layer.get_weight())
	# print(f'Epoch calculate with error = {err}')

	# self.weight_list = np.append(self.weight_list, [weight])

	def predict(self, first_input, second_input):
	output = None

	# predict data at all layer
	for layer in self.layers:
	output = layer.predict(first_input, second_input)
	# output as input of next layer
	first_input = output

	return output

	def check_type(self):
	print(
	"It is in the Initial Class Network Please call this function on the child class"
	)
	return False