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"""
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Show a weight matrix.
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:param w: the weight matrix.
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:param w_title: the title of the weight matrix
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:return: None.
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"""
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# show w_z matrix of update gate.
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axes_w = plt.gca()
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plt.imshow(w)
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plt.colorbar()
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# plt.colorbar(orientation="horizontal")
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plt.xlabel("$w_{1}$")
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plt.ylabel("$w_{2}$")
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axes_w.set_xticks([])
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axes_w.set_yticks([])
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matrix_size = "$:\ %d \\times\ %d$" % (len(w[0]), len(w))
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w_title += matrix_size
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plt.title(w_title)
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# show the matrix.
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plt.show()
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if __name__ == "__main__":
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w = np.random.random((8, 10))
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title = " "
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show(w, title)
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# <FILESEP>
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"""
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Example usage: CUDA_VISIBLE_DEVICES=1, python train.py --settings_file "config/settings_DDD17.yaml"
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"""
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import argparse
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import wandb
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from config.settings import Settings
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from training.ess_trainer import ESSModel
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from training.ess_supervised_trainer import ESSSupervisedModel
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import numpy as np
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import torch
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import random
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import os
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# random seed
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seed_value = 6
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np.random.seed(seed_value)
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random.seed(seed_value)
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os.environ['PYTHONHASHSEED'] = str(seed_value)
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torch.manual_seed(seed_value)
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torch.cuda.manual_seed(seed_value)
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torch.cuda.manual_seed_all(seed_value)
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torch.backends.cudnn.deterministic = True
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def main():
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parser = argparse.ArgumentParser(description='Train network.')
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parser.add_argument('--settings_file', help='Path to settings yaml', required=True)
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args = parser.parse_args()
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settings_filepath = args.settings_file
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settings = Settings(settings_filepath, generate_log=True)
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wandb.init(name=(settings.dataset_name_b.split("_")[0] + '_' + settings.timestr), project="zhaoning_sun_semester_thesis", entity="zhasun", sync_tensorboard=True)
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if settings.model_name == 'ess':
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trainer = ESSModel(settings)
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elif settings.model_name == 'ess_supervised':
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trainer = ESSSupervisedModel(settings)
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else:
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raise ValueError('Model name %s specified in the settings file is not implemented' % settings.model_name)
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wandb.config = {
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"random_seed": seed_value,
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"lr_front": settings.lr_front,
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"lr_back": settings.lr_back,
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"batch_size_a": settings.batch_size_a,
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"batch_size_b": settings.batch_size_b
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}
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trainer.train()
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if __name__ == "__main__":
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main()
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# <FILESEP>
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#!/usr/bin/env python3
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import os
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import json
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import random
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import argparse
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import webbrowser
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import concurrent.futures
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from time import sleep
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from core.utils import getNew
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from core.utils import ranker
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