for i in range(n_iter): for xmb, mmb, ymb in iter_data(*shuffle(trX, trM, trYt, random_state=np.random), n_batch=n_batch_train, truncate=True, verbose=True): cost, _ = sess.run([clf_loss, train], { X_train: xmb, M_train: mmb, Y_train: ymb }) n_updates += 1 if n_updates in [1000, 2000, 4000, 8000, 16000, 32000 ] and n_epochs == 0: log() n_epochs += 1 log() if submit: sess.run([ p.assign(ip) for p, ip in zip( params, joblib.load(os.path.join(save_dir, desc, 'best_params.jl'))) ]) predict() if analysis: rocstories_analysis(data_dir, os.path.join(submission_dir, 'ROCStories.tsv'), os.path.join(log_dir, 'rocstories.jsonl'))
n_updates = 0 n_epochs = 0 if dataset != 'stsb': trYt = trY if submit: path = os.path.join(save_dir, desc, 'best_params') chainer.serializers.save_npz(make_path(path), model) best_score = 0 for i in range(n_iter): print("running epoch", i) run_epoch() n_epochs += 1 log() if submit: path = os.path.join(save_dir, desc, 'best_params') chainer.serializers.load_npz(make_path(path), model) predict() if analysis: if dataset == 'rocstories': rocstories_analysis( data_dir, os.path.join( submission_dir, filenames[dataset]), os.path.join( log_dir, '{}.jsonl'.format(desc))) elif dataset == 'sst': sst_analysis( data_dir, os.path.join( submission_dir, filenames[dataset]), os.path.join( log_dir, '{}.jsonl'.format(desc))) else: raise NotImplementedError
clf_head.to_gpu() n_updates = 0 n_epochs = 0 if dataset != 'stsb': trYt = trY if submit: path = os.path.join(save_dir, desc, 'best_params') chainer.serializers.save_npz(make_path(path), model) best_score = 0 for i in range(n_iter): print("running epoch", i) run_epoch() n_epochs += 1 log() if submit: path = os.path.join(save_dir, desc, 'best_params') chainer.serializers.load_npz(make_path(path), model) predict() if analysis: if dataset == 'rocstories': rocstories_analysis( data_dir, os.path.join(submission_dir, filenames[dataset]), os.path.join(log_dir, '{}.jsonl'.format(desc))) elif dataset == 'sst': sst_analysis(data_dir, os.path.join(submission_dir, filenames[dataset]), os.path.join(log_dir, '{}.jsonl'.format(desc))) else: raise NotImplementedError