steffen/self-replicating-neural-networks
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

journal linspace basins

Browse Source
This commit is contained in:
steffen-illium
2021-06-25 10:25:25 +02:00
parent cf6eec639f
commit 14d9a533cb
8 changed files with 69 additions and 100 deletions
Download Patch File Download Diff File Expand all files Collapse all files

31
journal_soup_basins.py
View File

@ -1,14 +1,12 @@
import os
from pathlib import Path
import pickle
from torch import mean
from tqdm import tqdm
import random
import copy
from functionalities_test import is_identity_function, test_status, test_for_fixpoints, is_zero_fixpoint, is_divergent, is_secondary_fixpoint
from functionalities_test import is_identity_function, test_status, is_zero_fixpoint, is_divergent, is_secondary_fixpoint
from network import Net
from visualization import plot_3d_self_train, plot_loss, plot_3d_soup
from visualization import plot_loss, plot_3d_soup
import numpy as np
from tabulate import tabulate
from sklearn.metrics import mean_absolute_error as MAE
@ -18,10 +16,6 @@ import seaborn as sns
from matplotlib import pyplot as plt
def prng():
return random.random()
def l1(tup):
a, b = tup
return abs(a - b)
@ -88,20 +82,6 @@ def distance_from_parent(nets, distance="MIM", print_it=True):
class SoupSpawnExperiment:
@staticmethod
def apply_noise(network, noise: int):
""" Changing the weights of a network to values + noise """
for layer_id, layer_name in enumerate(network.state_dict()):
for line_id, line_values in enumerate(network.state_dict()[layer_name]):
for weight_id, weight_value in enumerate(network.state_dict()[layer_name][line_id]):
# network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
if prng() < 0.5:
network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
else:
network.state_dict()[layer_name][line_id][weight_id] = weight_value - noise
return network
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
epochs, st_steps, attack_chance, nr_clones, noise, directory) -> None:
@ -220,8 +200,7 @@ class SoupSpawnExperiment:
clone = Net(net.input_size, net.hidden_size, net.out_size,
f"net_{str(i)}_clone_{str(j)}", start_time=self.ST_steps)
clone.load_state_dict(copy.deepcopy(net.state_dict()))
rand_noise = prng() * self.noise
clone = self.apply_noise(clone, rand_noise)
clone = clone.apply_noise(self.noise)
clone.s_train_weights_history = copy.deepcopy(net.s_train_weights_history)
clone.number_trained = copy.deepcopy(net.number_trained)
@ -262,9 +241,9 @@ class SoupSpawnExperiment:
f"\nMSE({i},{j}): {MSE_post}"
f"\nMAE({i},{j}): {MAE_post}"
f"\nMIM({i},{j}): {MIM_post}\n")
self.parents_clones_id_functions.append(clone):
self.parents_clones_id_functions.append(clone)
df.loc[df.name==clone.name, ["MAE_post", "MSE_post", "MIM_post", "status_post"]] = [MAE_post, MSE_post, MIM_post, clone.is_fixpoint]
df.loc[df.name == clone.name, ["MAE_post", "MSE_post", "MIM_post", "status_post"]] = [MAE_post, MSE_post, MIM_post, clone.is_fixpoint]
# Finally take parent net {i} and finish it's training for comparison to clone development.
for _ in range(self.epochs - 1):