save_weights(layers, config['weights_dir'], epoch) np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy', learning_rate.get_value()) save_momentums(vels, config['weights_dir'], epoch) print('Optimization complete.') if __name__ == '__main__': with open('config.yaml', 'r') as f: config = yaml.load(f) with open('spec_1gpu.yaml', 'r') as f: config = dict(config.items() + yaml.load(f).items()) config = proc_configs(config) if config['para_load']: from proc_load import fun_load config['queue_l2t'] = Queue(1) config['queue_t2l'] = Queue(1) train_proc = Process(target=train_net, args=(config, )) load_proc = Process(target=fun_load, args=(config, config['sock_data'])) train_proc.start() load_proc.start() train_proc.join() load_proc.join() else: train_proc = Process(target=train_net, args=(config, ))
print('validation loss %f ' % (this_validation_loss)) return this_validation_error, this_validation_loss ############################################ if __name__ == '__main__': with open('config.yaml', 'r') as f: config = yaml.load(f) with open('spec_1gpu.yaml', 'r') as f: config = dict(config.items() + yaml.load(f).items()) config = proc_configs(config) config['resume_train'] = True config['load_epoch'] = 60 if config['para_load']: from proc_load import fun_load config['queue_l2t'] = Queue(1) config['queue_t2l'] = Queue(1) train_proc = Process(target=validate_performance, args=(config,)) load_proc = Process( target=fun_load, args=(config, config['sock_data'])) train_proc.start() load_proc.start() train_proc.join() load_proc.join()