提交 dbc6102e 编写于 作者: K Kexin Zhao

simplify label_sementic_example

上级 d4c21642
......@@ -116,29 +116,6 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
return feature_out
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def create_random_lodtensor(lod, place, low, high):
data = np.random.random_integers(low, high, [lod[-1], 1]).astype("int64")
res = fluid.LoDTensor()
res.set(data, place)
res.set_lod([lod])
return res
def train(use_cuda, save_dirname=None, is_local=True):
# define network topology
word = fluid.layers.data(
......@@ -271,23 +248,33 @@ def infer(use_cuda, save_dirname=None):
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
lod = [0, 4, 10]
word = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
pred = create_random_lodtensor(
lod, place, low=0, high=pred_dict_len - 1)
ctx_n2 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_n1 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_0 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_p1 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
ctx_p2 = create_random_lodtensor(
lod, place, low=0, high=word_dict_len - 1)
mark = create_random_lodtensor(
lod, place, low=0, high=mark_dict_len - 1)
# Setup inputs by creating LoDTensors to represent sequences of words.
# Here each word is the basic element of these LoDTensors and the shape of
# each word (base_shape) should be [1] since it is simply an index to
# look up for the corresponding word vector.
# Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
# which has only one lod level. Then the created LoDTensors will have only
# one higher level structure (sequence of words, or sentence) than the basic
# element (word). Hence the LoDTensor will hold data for three sentences of
# length 3, 4 and 2, respectively.
lod = [[3, 4, 2]]
base_shape = [1]
word = fluid.create_random_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
pred = fluid.create_random_lodtensor(
lod, base_shape, place, low=0, high=pred_dict_len - 1)
ctx_n2 = fluid.create_random_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_n1 = fluid.create_random_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_0 = fluid.create_random_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p1 = fluid.create_random_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
ctx_p2 = fluid.create_random_lodtensor(
lod, base_shape, place, low=0, high=word_dict_len - 1)
mark = fluid.create_random_lodtensor(
lod, base_shape, place, low=0, high=mark_dict_len - 1)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
......
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