# from __future__ import annotations
import copy
from typing import Union
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch import optim, Tensor
class Net(nn.Module):
@staticmethod
def create_target_weights(input_weight_matrix: Tensor) -> Tensor:
""" Outputting a tensor with the target weights. """
target_weight_matrix = np.arange(len(input_weight_matrix)).reshape(len(input_weight_matrix), 1).astype("f")
for i in range(len(input_weight_matrix)):
target_weight_matrix[i] = input_weight_matrix[i][0]
return torch.from_numpy(target_weight_matrix)
@staticmethod
def are_weights_diverged(network_weights):
""" Testing if the weights are eiter converging to infinity or -infinity. """
for layer_id, layer in enumerate(network_weights):
for cell_id, cell in enumerate(layer):
for weight_id, weight in enumerate(cell):
if np.isnan(weight):
return True
if np.isinf(weight):
return True
return False
def apply_weights(self, new_weights: Tensor):
""" Changing the weights of a network to new given values. """
i = 0
for layer_id, layer_name in enumerate(self.state_dict()):
for line_id, line_values in enumerate(self.state_dict()[layer_name]):
for weight_id, weight_value in enumerate(self.state_dict()[layer_name][line_id]):
self.state_dict()[layer_name][line_id][weight_id] = new_weights[i]
i += 1
return self
def __init__(self, i_size: int, h_size: int, o_size: int, name=None, start_time=1) -> None:
super().__init__()
self.start_time = start_time
self.name = name
self.input_size = i_size
self.hidden_size = h_size
self.out_size = o_size
self.no_weights = h_size * (i_size + h_size * (h_size - 1) + o_size)
""" Data saved in self.s_train_weights_history & self.s_application_weights_history is used for experiments. """
self.s_train_weights_history = []
self.s_application_weights_history = []
self.loss_history = []
self.trained = False
self.number_trained = 0
self.is_fixpoint = ""
self.fc1 = nn.Linear(i_size, h_size, False)
self.fc2 = nn.Linear(h_size, h_size, False)
self.fc3 = nn.Linear(h_size, o_size, False)
def forward(self, x):
x = self.fc1(x)
x = self.fc2(x)
x = self.fc3(x)
return x
def normalize(self, value, norm):
""" Normalizing the values >= 1 and adding pow(10, -8) to the values equal to 0 """
if norm > 1:
return float(value) / float(norm)
else:
return float(value)
def input_weight_matrix(self) -> Tensor:
""" Calculating the input tensor formed from the weights of the net """
# The "4" represents the weightwise coordinates used for the matrix: <value><layer_id><cell_id><positional_id>
weight_matrix = np.arange(self.no_weights * 4).reshape(self.no_weights, 4).astype("f")
i = 0
max_layer_id = len(self.state_dict()) - 1
for layer_id, layer_name in enumerate(self.state_dict()):
max_cell_id = len(self.state_dict()[layer_name]) - 1
for line_id, line_values in enumerate(self.state_dict()[layer_name]):
max_weight_id = len(line_values) - 1
for weight_id, weight_value in enumerate(self.state_dict()[layer_name][line_id]):
weight_matrix[i] = weight_value.item(), self.normalize(layer_id, max_layer_id), self.normalize(line_id, max_cell_id), self.normalize(weight_id, max_weight_id)
i += 1
return torch.from_numpy(weight_matrix)
def self_train(self, training_steps: int, log_step_size: int, learning_rate: float) -> (np.ndarray, Tensor, list):
""" Training a network to predict its own weights in order to self-replicate. """
optimizer = optim.SGD(self.parameters(), lr=learning_rate, momentum=0.9)
self.trained = True
for training_step in range(training_steps):
self.number_trained += 1
optimizer.zero_grad()
input_data = self.input_weight_matrix()
target_data = self.create_target_weights(input_data)
output = self(input_data)
loss = F.mse_loss(output, target_data)
loss.backward()
optimizer.step()
# Saving the history of the weights after a certain amount of steps (aka log_step_size) for research.
# If it is a soup/mixed env. save weights only at the end of all training steps (aka a soup/mixed epoch)
if "soup" not in self.name and "mixed" not in self.name:
weights = self.create_target_weights(self.input_weight_matrix())
# If self-training steps are lower than 10, then append weight history after each ST step.
if self.number_trained < 10:
self.s_train_weights_history.append(weights.T.detach().numpy())
self.loss_history.append(loss.detach().numpy().item())
else:
if self.number_trained % log_step_size == 0:
self.s_train_weights_history.append(weights.T.detach().numpy())
self.loss_history.append(loss.detach().numpy().item())
weights = self.create_target_weights(self.input_weight_matrix())
# Saving weights only at the end of a soup/mixed exp. epoch.
if "soup" in self.name or "mixed" in self.name:
self.s_train_weights_history.append(weights.T.detach().numpy())
self.loss_history.append(loss.detach().numpy().item())
return weights.detach().numpy(), loss, self.loss_history
def self_application(self, SA_steps: int, log_step_size: Union[int, None] = None):
""" Inputting the weights of a network to itself for a number of steps, without backpropagation. """
for i in range(SA_steps):
output = self(self.input_weight_matrix())
# Saving the weights history after a certain amount of steps (aka log_step_size) for research purposes.
# If self-application steps are lower than 10, then append weight history after each SA step.
if SA_steps < 10:
weights = self.create_target_weights(self.input_weight_matrix())
self.s_application_weights_history.append(weights.T.detach().numpy())
else:
weights = self.create_target_weights(self.input_weight_matrix())
if i % log_step_size == 0:
self.s_application_weights_history.append(weights.T.detach().numpy())
""" See after how many steps of SA is the output not changing anymore: """
# print(f"Self-app. step {i+1}: {Experiment.changing_rate(output2, output)}")
self = self.apply_weights(output)
return self
def attack(self, other_net):
other_net_weights = other_net.input_weight_matrix()
my_evaluation = self(other_net_weights)
SA_steps = 1
return other_net.apply_weights(my_evaluation)