118 lines
4.3 KiB
Python
118 lines
4.3 KiB
Python
import os.path
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import pickle
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from pathlib import Path
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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 plot_loss, bar_chart_fixpoints
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from visualization import plot_3d_self_train
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class SelfTrainExperiment:
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def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
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epochs, directory_name) -> 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.epochs = epochs
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self.loss_history = []
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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.directory_name = directory_name
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os.mkdir(self.directory_name)
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self.nets = []
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# Create population:
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self.populate_environment()
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self.weights_evolution_3d_experiment()
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self.count_fixpoints()
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self.visualize_loss()
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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 ST experiment %s" % i)
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net_name = f"ST_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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for _ in range(self.epochs):
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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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net.self_train(1, self.log_step_size, self.net_learning_rate)
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print(f"\nLast weight matrix (epoch: {self.epochs}):\n{net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")
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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"ST_{str(len(self.nets))}_nets_3d_weights_PCA"
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return plot_3d_self_train(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-train for {self.epochs} epochs"
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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 visualize_loss(self):
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for i in range(len(self.nets)):
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net_loss_history = self.nets[i].loss_history
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self.loss_history.append(net_loss_history)
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plot_loss(self.loss_history, self.directory_name)
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def run_ST_experiment(population_size, batch_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
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epochs, runs, run_name, name_hash):
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experiments = {}
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logging_directory = Path('output') / 'self_training'
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logging_directory.mkdir(parents=True, exist_ok=True)
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# Running the experiments
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for i in range(runs):
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experiment_name = f"{run_name}_run_{i}_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
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this_exp_directory = logging_directory / experiment_name
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ST_experiment = SelfTrainExperiment(
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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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epochs,
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this_exp_directory
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)
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with (this_exp_directory / 'full_experiment_pickle.p').open('wb') as f:
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pickle.dump(ST_experiment, f)
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experiments[i] = ST_experiment
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# Building a summary of all the runs
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summary_name = f"/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{epochs}_epochs_{str(name_hash)}"
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summary_directory_name = logging_directory / summary_name
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summary_directory_name.mkdir(parents=True, exist_ok=True)
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summary_pre_title = "ST"
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summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, summary_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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