def test_model(dataset_name, context, testdata):
tf.reset_default_graph()
exp_dir = os.path.join(expdir, dataset_name, context)
metamodel = MetaModel(exp_dir)
model_loaded = metamodel.model
metamodel.MakeSessionAndRestore(threads)
total_word_count = 0
total_log_prob = 0
results = []
for idx in range(len(testdata.df) / testdata.batch_size):
feed_dict = testdata.GetFeedDict(model_loaded)
c, words_in_batch, sentence_costs = metamodel.session.run([
model_loaded.avg_loss, model_loaded.words_in_batch,
model_loaded.per_sentence_loss
], feed_dict)
total_word_count += words_in_batch
total_log_prob += float(c * words_in_batch)
print '{0}\t{1:.3f}'.format(idx,
np.exp(total_log_prob / total_word_count))
lens = feed_dict[model_loaded.query_lengths]
for length, sentence_cost in zip(lens, sentence_costs):
data_row = {'length': length, 'cost': sentence_cost}
results.append(data_row)
results = pandas.DataFrame(results)
results.to_csv(os.path.join(exp_dir, 'pplstats.csv'))
idx = len(testdata.df) / testdata.batch_size
print '{0}\t{1:.3f}'.format(idx, np.exp(total_log_prob / total_word_count))
parser = argparse.ArgumentParser()
parser.add_argument('expdir', help='experiment directory')
parser.add_argument('--threads',
type=int,
default=12,
help='how many threads to use in tensorflow')
args = parser.parse_args()
df = pandas.read_csv('/g/ssli/data/LowResourceLM/aol/queries01.dev.txt.gz',
sep='\t',
header=None)
df.columns = ['user', 'query_', 'date']
df['user'] = df.user.apply(lambda x: 's' + str(x))
m = MetaModel(args.expdir) # Load the model
m.MakeSessionAndRestore(args.threads)
for i in range(23000):
row = df.iloc[i]
query_len = len(row.query_)
if query_len <= 3:
continue
prefix_len = GetPrefixLen(row.user, row.query_)
prefix = row.query_[:prefix_len]
b = GetCompletions(['<S>'] + list(prefix),
m.user_vocab[row.user],
m,
branching_factor=4)
qlist = [''.join(q.words[1:-1]) for q in reversed(list(b))]
type=str,
action='append',
dest='data',
default=[data_dir + "queries07.test.txt.gz"],
help='where to load the data')
parser.add_argument('--threads',
type=int,
default=12,
help='how many threads to use in tensorflow')
args = parser.parse_args()
expdir = args.expdir
# 模型加载
metamodel = MetaModel(expdir)
model = metamodel.model
metamodel.MakeSessionAndRestore(args.threads)
# 数据加载
df = LoadData(args.data)
dataset = Dataset(df,
metamodel.char_vocab,
metamodel.user_vocab,
max_len=metamodel.params.max_len)
total_word_count = 0
total_log_prob = 0
print(len(dataset.df), dataset.batch_size) # 20999 24
for idx in range(0, int(len(dataset.df) / dataset.batch_size)):
feed_dict = dataset.GetFeedDict(model)
# 这里的session 是 获取的是 保存后的模型
c, words_in_batch = metamodel.session.run(
[model.avg_loss, model.words_in_batch], feed_dict)