hl1 = 4
hl2 = 4
hl3 = 4
hl4 = 4
hidden_architecture = np.array([[hl1, sigmoid], [hl2, sigmoid], [hl3, sigmoid],
[hl4, sigmoid]])
n_weights = X.shape[1] * hl1 * hl2 * hl3 * hl4 * len(set(y))
# create ann
ann_i = ANN(hidden_architecture, sigmoid, accuracy_score, (X, y), random_state,
0, (0, 1))
#++++++++++++++++++++++++++
# THE PROBLEM INSTANCE
#++++++++++++++++++++++++++
ann_op_i = ANNOP(search_space=(-2, 2, n_weights),
fitness_function=uls.parametrized_ann(ann_i),
minimization=False)
#++++++++++++++++++++++++++
# THE OPTIMIZATION
#++++++++++++++++++++++++++
n_gen = 100
ps = 50
p_c = 0.8
p_m = 0.3
radius = 0.2
ga = GeneticAlgorithm3(ann_op_i, random_state, ps,
uls.parametrized_tournament_selection(0.1),
uls.two_point_crossover, p_c,
uls.parametrized_ball_mutation(radius), p_c)
ga.initialize()
# ann's ingridients
hl1 = 10
hl2 = 10
hidden_architecture = np.array([[hl1, sigmoid], [hl2, sigmoid]])
n_weights = X_train.shape[1] * hl1 * hl2 * len(digits.target_names)
validation_p = 0.2
# create ann
ann_i = ANN(hidden_architecture, softmax, accuracy_score, (X_train, y_train),
random_state, validation_p, digits.target_names)
# ++++++++++++++++++++++++++
# THE PROBLEM INSTANCE
# ++++++++++++++++++++++++++
validation_threshold = 0.07
ann_op_i = ANNOP(search_space=(-2, 2, n_weights),
fitness_function=ann_i.stimulate,
minimization=False,
validation_threshold=validation_threshold)
# ++++++++++++++++++++++++++
# THE OPTIMIZATION
# restrictions:
# - 5000 f.e./run
# - 50 f.e./generation
# - use at least 5 runs for benchmarks
# ++++++++++++++++++++++++++
n_gen = 100
ps = 50
p_c = .7
p_m = 0.5
radius = .4
pressure = .2
import os
import datetime
import logging
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import utils as uls
from problems.ANNOP import ANNOP
from ANN.ANN import ANN, softmax, sigmoid
from algorithms.genetic_algorithm_Elitism import GeneticAlgorithm
# setup logger
file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "LogFiles/" + (str(datetime.datetime.now().date()) + "-" + str(datetime.datetime.now().hour) + \
"_" + str(datetime.datetime.now().minute) + "_log.csv"))
logging.basicConfig(filename=file_path,
level=logging.DEBUG,
format='%(name)s,%(message)s')
#++++++++++++++++++++++++++
# THE DATA
# restrictions:
# - MNIST digits (8*8)
# - 33% for testing
# - flattened input images
#++++++++++++++++++++++++++
# import data
digits = datasets.load_digits()
flat_images = np.array([image.flatten() for image in digits.images])
print(flat_images.shape)
print(digits.target_names)