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MetaNetworks Debugged II

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Steffen Illium
2022-02-01 18:17:11 +01:00
parent 246d825bb4
commit 1b7581e656
4 changed files with 105 additions and 61 deletions
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24
functionalities_test.py
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@ -1,16 +1,13 @@
import copy
from typing import Dict, List
import numpy as np
import torch
from tqdm import tqdm
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).any() or np.isinf(weight_value).any():
return True
return False
return network.input_weight_matrix().isinf().any().item() or network.input_weight_matrix().isnan().any().item()
def is_identity_function(network: Net, epsilon=pow(10, -5)) -> bool:
@ -19,13 +16,14 @@ def is_identity_function(network: Net, epsilon=pow(10, -5)) -> bool:
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)
return torch.allclose(target_data.detach(), predicted_values.detach(),
rtol=0, atol=epsilon)
def is_zero_fixpoint(network: Net, epsilon=pow(10, -5)) -> bool:
target_data = network.create_target_weights(network.input_weight_matrix().detach())
result = np.allclose(target_data, np.zeros_like(target_data), rtol=0, atol=epsilon)
result = torch.allclose(target_data, torch.zeros_like(target_data), rtol=0, atol=epsilon)
# result = bool(len(np.nonzero(network.create_target_weights(network.input_weight_matrix()))))
return result
@ -49,15 +47,15 @@ def is_secondary_fixpoint(network: Net, epsilon: float = pow(10, -5)) -> bool:
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)
check_abs_within_epsilon = torch.allclose(target_data.detach(), second_output.detach(),
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 list()
for net in nets:
for net in tqdm(nets, desc='Fixpoint Tester', total=len(nets)):
if is_divergent(net):
fixpoint_counter["divergent"] += 1
net.is_fixpoint = "divergent"