178 lines
7.2 KiB
Python
178 lines
7.2 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, summary_fixpoint_percentage
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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, line_chart_fixpoints
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from visualization import plot_3d_self_train
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class MixedSettingExperiment:
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def __init__(self, population_size, net_i_size, net_h_size, net_o_size, learning_rate, train_nets,
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epochs, SA_steps, ST_steps_between_SA, log_step_size, directory_name):
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super().__init__()
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self.population_size = population_size
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self.net_input_size = net_i_size
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self.net_hidden_size = net_h_size
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self.net_out_size = net_o_size
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self.net_learning_rate = learning_rate
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self.train_nets = train_nets
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self.epochs = epochs
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self.SA_steps = SA_steps
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self.ST_steps_between_SA = ST_steps_between_SA
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self.log_step_size = log_step_size
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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.loss_history = []
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self.fixpoint_counters_history = []
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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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self.populate_environment()
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self.fixpoint_percentage()
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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 mixed experiment %s" % i)
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net_name = f"mixed_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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self.nets.append(net)
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loop_epochs = tqdm(range(self.epochs))
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for j in loop_epochs:
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loop_epochs.set_description("Running mixed experiment %s" % j)
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for i in loop_population_size:
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net = self.nets[i]
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if self.train_nets == "before_SA":
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for _ in range(self.ST_steps_between_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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for _ in range(self.ST_steps_between_SA):
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net.self_train(1, self.log_step_size, self.net_learning_rate)
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print(
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f"\nLast weight matrix (epoch: {j}):\n{net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")
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test_for_fixpoints(self.fixpoint_counters, self.nets)
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# Rounding the result not to run into other problems later regarding the exact representation of floating number
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fixpoints_percentage = round((self.fixpoint_counters["fix_zero"] + self.fixpoint_counters[
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"fix_sec"]) / self.population_size, 1)
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self.fixpoint_counters_history.append(fixpoints_percentage)
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# Resetting the fixpoint counter. Last iteration not to be reset - it is important for the bar_chart_fixpoints().
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if j < self.epochs:
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self.reset_fixpoint_counters()
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def weights_evolution_3d_experiment(self):
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exp_name = f"Mixed {str(len(self.nets))}"
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# This batch size is not relevant for mixed settings because during an epoch there are more steps of SA & ST happening
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# and only they need the batch size. To not affect the number of epochs shown in the 3D plot, will send
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# forward the number "1" for batch size with the variable <irrelevant_batch_size>
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irrelevant_batch_size = 1
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plot_3d_self_train(self.nets, exp_name, self.directory_name, irrelevant_batch_size)
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def count_fixpoints(self):
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exp_details = f"SA steps: {self.SA_steps}; ST steps: {self.ST_steps_between_SA}"
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test_for_fixpoints(self.fixpoint_counters, self.nets)
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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 fixpoint_percentage(self):
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line_chart_fixpoints(self.fixpoint_counters_history, self.epochs, self.ST_steps_between_SA,
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self.SA_steps, self.directory_name, self.population_size)
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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 reset_fixpoint_counters(self):
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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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def run_mixed_experiment(population_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate, train_nets,
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epochs, SA_steps, ST_steps_between_SA, batch_size, name_hash, runs, run_name):
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experiments = {}
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fixpoints_percentages = []
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check_folder("mixed")
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# Running the experiments
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for i in range(runs):
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directory_name = f"experiments/mixed/{run_name}_run_{i}_{str(population_size)}_nets_{SA_steps}_SA_{ST_steps_between_SA}_ST_{str(name_hash)}"
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mixed_experiment = MixedSettingExperiment(
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population_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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train_nets,
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epochs,
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SA_steps,
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ST_steps_between_SA,
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batch_size,
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directory_name
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)
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pickle.dump(mixed_experiment, open(f"{directory_name}/full_experiment_pickle.p", "wb"))
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experiments[i] = mixed_experiment
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# Building history of fixpoint percentages for summary
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fixpoint_counters_history = mixed_experiment.fixpoint_counters_history
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if not fixpoints_percentages:
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fixpoints_percentages = mixed_experiment.fixpoint_counters_history
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else:
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# Using list comprehension to make the sum of all the percentages
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fixpoints_percentages = [fixpoints_percentages[i] + fixpoint_counters_history[i] for i in
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range(len(fixpoints_percentages))]
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# Building a summary of all the runs
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directory_name = f"experiments/mixed/summary_{run_name}_{runs}_runs_{str(population_size)}_nets_{str(name_hash)}"
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os.mkdir(directory_name)
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summary_pre_title = "mixed"
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summary_fixpoint_experiment(runs, population_size, epochs, experiments, net_learning_rate, directory_name,
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summary_pre_title)
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summary_fixpoint_percentage(runs, epochs, fixpoints_percentages, ST_steps_between_SA, SA_steps, directory_name,
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population_size)
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if __name__ == '__main__':
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raise NotImplementedError('Test this here!!!')
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