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from math import exp
import numpy as np
import random
import matplotlib.pyplot as plt
import time
from timeit import timeit
def sigmoid(x):
return 1 / (1 + exp(-x))
sigmoid = np.vectorize(sigmoid)
def d_sigmoid(x):
return sigmoid(x) * (1 - sigmoid(x))
d_sigmoid = np.vectorize(d_sigmoid)
learning_rate = 0.001
class Layer():
def __init__(self, input_size, output_size, weights=None, bias=None):
self.weights = np.random.randn(output_size, input_size)
# self.bias = np.random.randn(output_size)
self.input = np.zeros(input_size)
self.output = np.zeros(output_size)
self.inter = np.zeros(output_size)
self.grad_output = np.zeros(output_size)
self.grad_inter = np.zeros(output_size)
self.grad_weights = np.zeros((output_size, input_size))
# Bias
def __repr__(self):
return "<Layer {} -> {}>".format(self.weights.shape[1], self.weights.shape[0])
def compute(self, input):
self.input = input
self.inter = self.weights @ input #+ self.bias
self.output = sigmoid(self.inter)
return self.output
def compute_gradient(self, input=None, next_layer=None, expected=None):
if expected is not None:
self.grad_output = self.output - expected
else:
self.grad_output = next_layer.weights.T @ next_layer.grad_inter
self.grad_inter = d_sigmoid(self.inter) * self.grad_output
if input is None:
input = self.input
self.grad_weights = np.outer(self.grad_inter, input)
class MultiLayerPerceptron():
layers = []
def __init__(self, layer_sizes):
for k in range(len(layer_sizes)-1):
self.layers.append(Layer(layer_sizes[k], layer_sizes[k+1]))
def __repr__(self):
return "<MultiLayerPerceptron {}>".format(self.layers)
@staticmethod
def load(filename):
array = np.load(open(filename, 'rb'))
sizes = [array[0].shape[1]]
for layer in array:
sizes.append(layer.shape[0])
mlp = MultiLayerPerceptron(sizes)
for k, layer in enumerate(array):
mlp.layers[k].weights = layer
return mlp
def save(self, filename):
array = [layer.weights for layer in self.layers]
np.save(open(filename, 'wb'), array)
def error(self, output, expected):
return 1/2 * np.sum((expected - output)**2)
def frontprop(self, input) -> np.ndarray:
for layer in self.layers:
input = layer.compute(input)
return input
def backprop(self, input: np.ndarray, expected):
self.frontprop(input)
self.layers[-1].compute_gradient(expected=expected)
for i in range(len(self.layers)-2, 0, -1):
self.layers[i].compute_gradient(next_layer=self.layers[i+1])
self.layers[0].compute_gradient(input=input, next_layer=self.layers[1])
def fit(self):
for layer in self.layers:
layer.weights -= learning_rate * layer.grad_weights
def test(self, test_space, test_iterations=100):
errors = []
success = 0
for j in range(test_iterations):
input, expected = random.choice(test_space)
output = self.frontprop(input)
errors.append(self.error(output, expected))
if np.argmax(output) == np.argmax(expected):
success += 1
return sum(errors)/100, success/100
def learn(self, space, test_space, iterations=100, test_iterations=100, epochs=100, save=None):
mean_errors = []
mean_accuracy = []
begin = time.time()
for i in range(epochs):
if save: self.save(save)
# Print ETA
elapsed = time.time() - begin
total = int(epochs/i * elapsed) if i > 0 else 0
remaining = int(total - elapsed)
text = "\r[Learning] {:3.2f}% ~ ert ".format(i*100/epochs)
# if remaining//3600 > 0:
text += "{:2d} h ".format(remaining//3600)
remaining = remaining % 3600
# if remaining//60 > 0:
text += "{:2d} m ".format(remaining//60)
remaining = remaining % 60
text += "{:2d} s".format(remaining)
print(text, end="")
# Train
for j in range(iterations):
input, expected = random.choice(space)
self.backprop(input, expected)
self.fit()
# Test
error, accuracy = self.test(test_space, test_iterations)
mean_errors.append(error)
mean_accuracy.append(accuracy)
return mean_errors, mean_accuracy
if __name__ == "__main__":
# XOR
# mlp = MultiLayerPerceptron([2, 4, 4, 1])
# space = [
# (np.array([0, 0]), np.array([0])),
# (np.array([0, 1]), np.array([1])),
# (np.array([1, 0]), np.array([1])),
# (np.array([1, 1]), np.array([0])),
# ]
# mean_errors = []
# begin = time.time()
# for i in range(100):
# errors = []
# elapsed = time.time() - begin
# remaining = int(100/i * elapsed - elapsed) if i> 0 else 0
# print(i, "{}% ~ {} min {} sec left".format(i, remaining//60, remaining%60), end='\r')
# for j in range(1600):
# input, expected = random.choice(space)
# mlp.backprop(input, expected)
# mlp.fit()
# for input, expected in space:
# errors.append(mlp.error(mlp.frontprop(input), expected))
# mean_errors.append(sum(errors, 0)/4)
# for input, expected in space:
# print(mlp.frontprop(input), expected)
# plt.plot(mean_errors)
# plt.show()
# MNIST
print("[Initializing] Perceptron: ", end="")
# mlp = MultiLayerPerceptron([28*28, 32, 16, 10])
mlp = MultiLayerPerceptron.load('mnist.npy')
print("OK")
print("[Loading] Training set: ", end="")
space = np.load(open("data/train.npy", "rb"))
space = [(input.reshape(28*28), expected) for input, expected in space]
print("OK Testing set: ", end="")
test_space = np.load(open("data/test.npy", "rb"))
test_space = [(input.reshape(28*28), expected) for input, expected in test_space]
print("OK")
mean_errors, mean_accuracy = mlp.learn(space, test_space, iterations=1000, epochs=100, save='mnist.npy')
image, expected = random.choice(space)
input = np.reshape(image, 28*28)
print(mlp.frontprop(input), expected)
print(np.argmax(mlp.frontprop(input)), "=>", np.argmax(expected))
plt.figure(1)
plt.subplot(211)
plt.plot(mean_errors)
plt.ylabel("error")
plt.subplot(212)
plt.plot(mean_accuracy)
plt.ylabel("accuracy")
plt.axis([0, len(mean_accuracy), 0, 1])
plt.show()
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