100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
import os
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import time
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import dill
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from tqdm import tqdm
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class Experiment:
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@staticmethod
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def from_dill(path):
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with open(path, "rb") as dill_file:
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return dill.load(dill_file)
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def __init__(self, name=None, ident=None):
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self.experiment_id = ident or time.time()
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self.experiment_name = name or 'unnamed_experiment'
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self.base_dir = self.experiment_name
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self.next_iteration = 0
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self.log_messages = []
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def __enter__(self):
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self.dir = os.path.join(self.base_dir, 'experiments', 'exp-{name}-{id}-{it}'.format(
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name=self.experiment_name, id=self.experiment_id, it=self.next_iteration)
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)
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os.makedirs(self.dir)
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print("** created {dir} **".format(dir=self.dir))
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.save(experiment=self)
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self.save_log()
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self.next_iteration += 1
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def log(self, message, **kwargs):
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self.log_messages.append(message)
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print(message, **kwargs)
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def save_log(self, log_name="log"):
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with open(os.path.join(self.dir, "{name}.txt".format(name=log_name)), "w") as log_file:
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for log_message in self.log_messages:
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print(str(log_message), file=log_file)
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def save(self, **kwargs):
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for name, value in kwargs.items():
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with open(os.path.join(self.dir, "{name}.dill".format(name=name)), "wb") as dill_file:
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dill.dump(value, dill_file)
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class FixpointExperiment(Experiment):
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def __init__(self):
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super().__init__(name=self.__class__.__name__)
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self.counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
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self.interesting_fixpoints = []
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def run_net(self, net, step_limit=100):
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i = 0
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while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
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net.self_attack()
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i += 1
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self.count(net)
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def count(self, net):
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if net.is_diverged():
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self.counters['divergent'] += 1
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elif net.is_fixpoint():
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if net.is_zero():
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self.counters['fix_zero'] += 1
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else:
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self.counters['fix_other'] += 1
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self.interesting_fixpoints.append(net.get_weights())
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elif net.is_fixpoint(2):
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self.counters['fix_sec'] += 1
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else:
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self.counters['other'] += 1
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class MixedFixpointExperiment(FixpointExperiment):
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def run_net(self, net, trains_per_application=100, step_limit=100):
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i = 0
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while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
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net.self_attack()
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with tqdm(postfix=["Loss", dict(value=0)]) as bar:
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for _ in range(trains_per_application):
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loss = net.compiled().train()
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bar.postfix[1]["value"] = loss
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bar.update()
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i += 1
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self.count(net)
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class SoupExperiment(Experiment):
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pass
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class IdentLearningExperiment(Experiment):
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pass
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