82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
import copy
|
|
from typing import Dict, List
|
|
import numpy as np
|
|
from torch import Tensor
|
|
from network import Net
|
|
|
|
|
|
def is_divergent(network: Net) -> bool:
|
|
for i in network.input_weight_matrix():
|
|
weight_value = i[0].item()
|
|
|
|
if np.isnan(weight_value).all() or np.isinf(weight_value).all():
|
|
return True
|
|
return False
|
|
|
|
|
|
def is_identity_function(network: Net, epsilon=pow(10, -5)) -> bool:
|
|
|
|
input_data = network.input_weight_matrix()
|
|
target_data = network.create_target_weights(input_data)
|
|
predicted_values = network(input_data)
|
|
|
|
return np.allclose(target_data.detach().numpy(), predicted_values.detach().numpy(),
|
|
rtol=0, atol=epsilon)
|
|
|
|
|
|
def is_zero_fixpoint(network: Net) -> bool:
|
|
result = bool(len(np.nonzero(network.create_target_weights(network.input_weight_matrix()))))
|
|
return result
|
|
|
|
|
|
def is_secondary_fixpoint(network: Net, epsilon: float = pow(10, -5)) -> bool:
|
|
""" Secondary fixpoint check is done like this: compare first INPUT with second OUTPUT.
|
|
If they are within the boundaries, then is secondary fixpoint. """
|
|
|
|
input_data = network.input_weight_matrix()
|
|
target_data = network.create_target_weights(input_data)
|
|
|
|
# Calculating first output
|
|
first_output = network(input_data)
|
|
|
|
# Getting the second output by initializing a new net with the weights of the original net.
|
|
net_copy = copy.deepcopy(network)
|
|
net_copy.apply_weights(first_output)
|
|
input_data_2 = net_copy.input_weight_matrix()
|
|
|
|
# Calculating second output
|
|
second_output = network(input_data_2)
|
|
|
|
# Perform the Check: all(epsilon > abs(input_data - second_output))
|
|
check_abs_within_epsilon = np.allclose(target_data.detach().numpy(), second_output.detach().numpy(),
|
|
rtol=0, atol=epsilon)
|
|
return check_abs_within_epsilon
|
|
|
|
|
|
def test_for_fixpoints(fixpoint_counter: Dict, nets: List, id_functions=None):
|
|
id_functions = id_functions or None
|
|
|
|
for i in range(len(nets)):
|
|
net = nets[i]
|
|
if is_divergent(nets[i]):
|
|
fixpoint_counter["divergent"] += 1
|
|
nets[i].is_fixpoint = "divergent"
|
|
elif is_identity_function(nets[i]): # is default value
|
|
fixpoint_counter["identity_func"] += 1
|
|
nets[i].is_fixpoint = "identity_func"
|
|
id_functions.append(nets[i])
|
|
elif is_zero_fixpoint(nets[i]):
|
|
fixpoint_counter["fix_zero"] += 1
|
|
nets[i].is_fixpoint = "fix_zero"
|
|
elif is_secondary_fixpoint(nets[i]):
|
|
fixpoint_counter["fix_sec"] += 1
|
|
nets[i].is_fixpoint = "fix_sec"
|
|
else:
|
|
fixpoint_counter["other_func"] += 1
|
|
nets[i].is_fixpoint = "other_func"
|
|
return id_functions
|
|
|
|
|
|
def changing_rate(x_new, x_old):
|
|
return x_new - x_old
|