def epoch_callback(round_stats, epoch): elapsed = get_elapsed() log("Begin epoch callback for epoch {0}".format(epoch)) if validation_skip > 0 and (epoch + 1) % validation_skip == 0: DLs = [('normal, L2', None, embedding_dst)] set_dicts = output_dumps.full_stats(round_stats, sets, DLs, model, sample_size=sample_size, log=lambda *_: None, show_multiple=show_multiple) else: set_dicts = round_stats s = {'epoch': epoch, 'time': elapsed, 'sets': set_dicts} stat.info("{0},".format(s)) # take snapshot if snapshot_prefix and snapshot_skip > 0 and (epoch + 1) % snapshot_skip == 0: snapshot_name = "{0}.{1}".format(snapshot_prefix, epoch) log("Exporting snapshot weights for epoch {0} to {1}".format(epoch, snapshot_name)) helpers.export_weights(model, snapshot_name) log("Exported snapshot weights for epoch {0}".format(epoch)) log("End epoch callback for epoch {0}".format(epoch)) return s
def epoch_callback(round_stats, epoch): elapsed = get_elapsed() log("Begin epoch callback for epoch {0}".format(epoch)) if validation_skip > 0 and (epoch + 1) % validation_skip == 0: DLs = [('normal, L2', None, embedding_dst)] set_dicts = output_dumps.full_stats(round_stats, sets, DLs, model, sample_size=sample_size, log=lambda *_: None, show_multiple=show_multiple) else: set_dicts = round_stats s = {'epoch': epoch, 'time': elapsed, 'sets': set_dicts} stat.info("{0},".format(s)) # take snapshot if snapshot_prefix and snapshot_skip > 0 and (epoch + 1) % snapshot_skip == 0: snapshot_name = "{0}.{1}".format(snapshot_prefix, epoch) log("Exporting snapshot weights for epoch {0} to {1}".format( epoch, snapshot_name)) helpers.export_weights(model, snapshot_name) log("Exported snapshot weights for epoch {0}".format(epoch)) log("End epoch callback for epoch {0}".format(epoch)) return s
def h_test(cache, args): sets = {} req = ['X_emb', 'Y_tokens'] show_multiple = s2b(get_required_arg(args, 'show_multiple')) # load embeddings embedding_src = get_embedding(cache, args, 'embedding_src') embedding_dst = get_embedding(cache, args, 'embedding_dst') # load dataset test_src = get_required_arg(args, 'test_src') test_dst = get_required_arg(args, 'test_dst') maxlen = get_required_arg(args, 'maxlen') sets['test'] = helpers.load_datasets(req, embedding_src, embedding_dst, test_src, test_dst, maxlen) # load model log('loading model') model = get_fitted_model(cache, args) log('done loading model') sample_size = get_required_arg(args, 'sample_size') # compute test round_stats = {'test': {}} DLs = [('normal, L2', None, embedding_dst)] set_dicts = output_dumps.full_stats(round_stats, sets, DLs, model, sample_size=sample_size, log=lambda *_: None, show_multiple=show_multiple) print set_dicts log("done test")
def h_test(cache, args): sets = {} req = ['X_emb', 'Y_tokens'] show_multiple = s2b(get_required_arg(args, 'show_multiple')) # load embeddings embedding_src = get_embedding(cache, args, 'embedding_src') embedding_dst = get_embedding(cache, args, 'embedding_dst') # load dataset test_src = get_required_arg(args, 'test_src') test_dst = get_required_arg(args, 'test_dst') maxlen = get_required_arg(args, 'maxlen') sets['test'] = helpers.load_datasets(req, embedding_src, embedding_dst, test_src, test_dst, maxlen) # load model log('loading model') model = get_fitted_model(cache, args) log('done loading model') sample_size = get_required_arg(args, 'sample_size') # compute test round_stats = {'test':{}} DLs = [('normal, L2', None, embedding_dst)] set_dicts = output_dumps.full_stats(round_stats, sets, DLs, model, sample_size=sample_size, log=lambda *_: None, show_multiple=show_multiple) print set_dicts log("done test")