b1472479cb
Questions for functionalities_test.py corrected some fixes Redo and implementation of everything path related now using pathlib.Path
112 lines
3.6 KiB
Python
112 lines
3.6 KiB
Python
import copy
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from typing import Dict, List
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import numpy as np
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from torch import Tensor
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from network import Net
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def overall_fixpoint_test(network: Net, epsilon: float, input_data) -> bool:
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predicted_values = network(input_data)
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check_smaller_epsilon = all(epsilon > predicted_values)
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check_greater_epsilon = all(-epsilon < predicted_values)
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if check_smaller_epsilon and check_greater_epsilon:
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return True
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else:
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return False
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def is_divergent(network: Net) -> bool:
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for i in network.input_weight_matrix():
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weight_value = i[0].item()
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if np.isnan(weight_value) or np.isinf(weight_value):
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return True
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return False
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def is_identity_function(network: Net, epsilon=pow(10, -5)) -> bool:
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input_data = network.input_weight_matrix()
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target_data = network.create_target_weights(input_data)
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predicted_values = network(input_data)
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return np.allclose(target_data.detach().numpy(), predicted_values.detach().numpy(), 0, epsilon)
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def is_zero_fixpoint(network: Net, input_data: Tensor, epsilon=pow(10, -5)) -> bool:
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# FIXME: Is the the correct test?
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raise NotImplementedError
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result = overall_fixpoint_test(network, epsilon, input_data)
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return result
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def is_secondary_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> bool:
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""" Secondary fixpoint check is done like this: compare first INPUT with second OUTPUT.
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If they are within the boundaries, then is secondary fixpoint. """
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# Calculating first output
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first_output = network(input_data)
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# Getting the second output by initializing a new net with the weights of the original net.
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# FixMe: Is this correct? I Think it should be the same function thus the same network
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net_copy = copy.deepcopy(network)
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net_copy.apply_weights(first_output)
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input_data_2 = net_copy.input_weight_matrix()
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# Calculating second output
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second_output = network(input_data_2)
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# Perform the Check:
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check_abs_within_epsilon = all(epsilon > abs(input_data - second_output))
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# FIXME: This is wrong, is it?
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# check_smaller_epsilon = all(epsilon > second_output)
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# check_greater_epsilon = all(-epsilon < second_output)
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return True if check_abs_within_epsilon else False
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def is_weak_fixpoint(network: Net, input_data: Tensor, epsilon: float) -> bool:
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result = overall_fixpoint_test(network, epsilon, input_data)
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return result
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def test_for_fixpoints(fixpoint_counter: Dict, nets: List, id_functions=None):
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id_functions = id_functions or None
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zero_epsilon = pow(10, -5)
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epsilon = pow(10, -3)
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for i in range(len(nets)):
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net = nets[i]
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input_data = net.input_weight_matrix()
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if is_divergent(nets[i]):
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fixpoint_counter["divergent"] += 1
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nets[i].is_fixpoint = "divergent"
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elif is_identity_function(nets[i], zero_epsilon):
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fixpoint_counter["identity_func"] += 1
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nets[i].is_fixpoint = "identity_func"
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id_functions.append(nets[i])
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elif is_zero_fixpoint(nets[i], input_data, zero_epsilon):
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fixpoint_counter["fix_zero"] += 1
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nets[i].is_fixpoint = "fix_zero"
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elif is_weak_fixpoint(nets[i], input_data, epsilon):
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fixpoint_counter["fix_weak"] += 1
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nets[i].is_fixpoint = "fix_weak"
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elif is_secondary_fixpoint(nets[i], input_data, zero_epsilon):
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fixpoint_counter["fix_sec"] += 1
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nets[i].is_fixpoint = "fix_sec"
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else:
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fixpoint_counter["other_func"] += 1
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nets[i].is_fixpoint = "other_func"
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return id_functions
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def changing_rate(x_new, x_old):
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return x_new - x_old
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