4b5c36f6c0
- Reformated net.self_x functions (sa, st) - corrected robustness_exp.py - NO DEBUGGING DONE!!!!!
121 lines
4.5 KiB
Python
121 lines
4.5 KiB
Python
import os.path
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import pickle
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from tqdm import tqdm
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from experiments.helpers import check_folder, summary_fixpoint_experiment
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from functionalities_test import test_for_fixpoints
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from network import Net
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from visualization import bar_chart_fixpoints
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from visualization import plot_3d_self_application
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class SelfApplicationExperiment:
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def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size,
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net_learning_rate, application_steps, train_nets, directory_name, training_steps
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) -> None:
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self.population_size = population_size
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self.log_step_size = log_step_size
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self.net_input_size = net_input_size
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self.net_hidden_size = net_hidden_size
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self.net_out_size = net_out_size
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self.net_learning_rate = net_learning_rate
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self.SA_steps = application_steps #
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self.train_nets = train_nets
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self.ST_steps = training_steps
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self.directory_name = directory_name
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os.mkdir(self.directory_name)
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""" Creating the nets & making the SA steps & (maybe) also training the networks. """
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self.nets = []
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# Create population:
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self.populate_environment()
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self.fixpoint_counters = {
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"identity_func": 0,
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"divergent": 0,
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"fix_zero": 0,
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"fix_weak": 0,
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"fix_sec": 0,
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"other_func": 0
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}
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self.weights_evolution_3d_experiment()
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self.count_fixpoints()
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def populate_environment(self):
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loop_population_size = tqdm(range(self.population_size))
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for i in loop_population_size:
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loop_population_size.set_description("Populating SA experiment %s" % i)
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net_name = f"SA_net_{str(i)}"
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net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name
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)
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for _ in range(self.SA_steps):
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input_data = net.input_weight_matrix()
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target_data = net.create_target_weights(input_data)
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if self.train_nets == "before_SA":
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net.self_train(1, self.log_step_size, self.net_learning_rate)
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net.self_application(self.SA_steps, self.log_step_size)
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elif self.train_nets == "after_SA":
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net.self_application(self.SA_steps, self.log_step_size)
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net.self_train(1, self.log_step_size, self.net_learning_rate)
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else:
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net.self_application(self.SA_steps, self.log_step_size)
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self.nets.append(net)
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def weights_evolution_3d_experiment(self):
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exp_name = f"SA_{str(len(self.nets))}_nets_3d_weights_PCA"
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plot_3d_self_application(self.nets, exp_name, self.directory_name, self.log_step_size)
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def count_fixpoints(self):
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test_for_fixpoints(self.fixpoint_counters, self.nets)
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exp_details = f"{self.SA_steps} SA steps"
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bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory_name, self.net_learning_rate,
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exp_details)
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def run_SA_experiment(population_size, batch_size, net_input_size, net_hidden_size, net_out_size,
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net_learning_rate, runs, run_name, name_hash, application_steps, train_nets, training_steps):
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experiments = {}
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check_folder("self_application")
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# Running the experiments
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for i in range(runs):
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directory_name = f"experiments/self_application/{run_name}_run_{i}_{str(population_size)}_nets_{application_steps}_SA_{str(name_hash)}"
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SA_experiment = SelfApplicationExperiment(
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population_size,
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batch_size,
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net_input_size,
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net_hidden_size,
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net_out_size,
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net_learning_rate,
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application_steps,
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train_nets,
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directory_name,
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training_steps
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)
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pickle.dump(SA_experiment, open(f"{directory_name}/full_experiment_pickle.p", "wb"))
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experiments[i] = SA_experiment
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# Building a summary of all the runs
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directory_name = f"experiments/self_application/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{application_steps}_SA_{str(name_hash)}"
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os.mkdir(directory_name)
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summary_pre_title = "SA"
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summary_fixpoint_experiment(runs, population_size, application_steps, experiments, net_learning_rate,
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directory_name,
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summary_pre_title)
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if __name__ == '__main__':
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raise NotImplementedError('Test this here!!!')
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