Commit 68f0379a authored by 赵威's avatar 赵威

try speed

parent e41c6242
......@@ -92,8 +92,7 @@ def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
@numba.jit(parallel=True)
def _make_example(inputs):
def model_predict(inputs, predict_fn):
time_1 = timeit.default_timer()
int_columns = [
"active_type", "active_days", "card_id", "is_pure_author", "is_have_reply", "is_have_pure_reply", "content_level",
......@@ -107,20 +106,16 @@ def _make_example(inputs):
if col in ["click_label", "conversion_label"]:
pass
elif col in int_columns:
features[col] = _int64_feature(int(value))
features[col] = tf.train.Feature(int64_list=tf.train.Int64List(value=[int(value)]))
elif col in float_columns:
features[col] = _float_feature(float(value))
features[col] = tf.train.Feature(float_list=tf.train.FloatList(value=[float(value)]))
else:
features[col] = _bytes_feature(str(value).encode(encoding="utf-8"))
features[col] = tf.train.Feature(bytes_list=tf.train.BytesList(value=[str(value).encode(encoding="utf-8")]))
example = tf.train.Example(features=tf.train.Features(feature=features))
examples.append(example.SerializeToString())
total_1 = (timeit.default_timer() - time_1)
print("make example cost {:.5f}s".format(total_1))
return examples
def model_predict(inputs, predict_fn):
examples = _make_example(inputs)
time_1 = timeit.default_timer()
predictions = predict_fn({"examples": examples})
total_1 = (timeit.default_timer() - time_1)
......
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