本文介绍了Bert嵌入层使用BiLSTM引发+:“无类型"和"int"的类型错误:不支持的操作数类型的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我在将Bert嵌入层集成到BiLSTM模型中以解决词义消歧任务时遇到了问题，
I've problems integrating Bert Embedding Layer in a BiLSTM model for word sense disambiguation task,
Windows 10
Python 3.6.4
TenorFlow 1.12
Keras 2.2.4
No virtual environments were used
PyCharm Professional 2019.2
整个剧本
The whole script
import os
import yaml
import numpy as np
from argparse import ArgumentParser
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras.layers import (LSTM, Add, Bidirectional, Dense, Input, TimeDistributed, Embedding)
from tensorflow.keras.preprocessing.sequence import pad_sequences
try:
from bert.tokenization import FullTokenizer
except ModuleNotFoundError:
os.system('pip install bert-tensorflow')
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K
from tqdm import tqdm
from keras_bert import BertEmbeddingLayer
from model_utils import visualize_plot_mdl
from parsing_dataset import load_dataset
from utilities import configure_tf, initialize_logger
def parse_args():
parser = ArgumentParser(description="WSD")
parser.add_argument("--model_type", default='baseline', type=str,
help="""Choose the model: baseline: BiLSTM Model.
attention: Attention Stacked BiLSTM Model.
seq2seq: Seq2Seq Attention.""")
return vars(parser.parse_args())
def train_model(mdl, data, epochs=1, batch_size=32):
[train_input_ids, train_input_masks, train_segment_ids], train_labels = data
history = mdl.fit([train_input_ids, train_input_masks, train_segment_ids],
train_labels, epochs=epochs, batch_size=batch_size)
return history
def baseline_model(output_size):
hidden_size = 128
max_seq_len = 64
in_id = Input(shape=(None,), name="input_ids")
in_mask = Input(shape=(None,), name="input_masks")
in_segment = Input(shape=(None,), name="segment_ids")
bert_inputs = [in_id, in_mask, in_segment]
bert_embedding = BertEmbeddingLayer()(bert_inputs)
embedding_size = 768
bilstm = Bidirectional(LSTM(hidden_size, dropout=0.2,
recurrent_dropout=0.2,
return_sequences=True
)
)(bert_embedding)
output = TimeDistributed(Dense(output_size, activation="softmax"))(bilstm)
mdl = Model(inputs=bert_inputs, outputs=output, name="Bert_BiLSTM")
mdl.compile(loss="sparse_categorical_crossentropy",
optimizer='adadelta', metrics=["acc"])
return mdl
def initialize_vars(sess):
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
sess.run(tf.tables_initializer())
K.set_session(sess)
class PaddingInputExample(object):
"""Fake example so the num input examples is a multiple of the batch size.
When running eval/predict on the TPU, we need to pad the number of examples
to be a multiple of the batch size, because the TPU requires a fixed batch
size. The alternative is to drop the last batch, which is bad because it means
the entire output data won't be generated.
We use this class instead of `None` because treating `None` as padding
batches could cause silent errors.
"""
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text_a, text_b=None, label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The un-tokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The un-tokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
def create_tokenizer_from_hub_module(bert_path="https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1"):
"""Get the vocab file and casing info from the Hub module."""
bert_module = hub.Module(bert_path)
tokenization_info = bert_module(signature="tokenization_info", as_dict=True)
vocab_file, do_lower_case = sess.run(
[
tokenization_info["vocab_file"],
tokenization_info["do_lower_case"],
]
)
return FullTokenizer(vocab_file=vocab_file, do_lower_case=do_lower_case)
def convert_single_example(tokenizer, example, max_seq_length=256):
"""Converts a single `InputExample` into a single `InputFeatures`."""
if isinstance(example, PaddingInputExample):
input_ids = [0] * max_seq_length
input_mask = [0] * max_seq_length
segment_ids = [0] * max_seq_length
label = [0] * max_seq_length
return input_ids, input_mask, segment_ids, label
tokens_a = tokenizer.tokenize(example.text_a)
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0: (max_seq_length - 2)]
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
example.label.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
example.label.append(0)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
example.label.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
return input_ids, input_mask, segment_ids, example.label
def convert_examples_to_features(tokenizer, examples, max_seq_length=256):
"""Convert a set of `InputExample`s to a list of `InputFeatures`."""
input_ids, input_masks, segment_ids, labels = [], [], [], []
for example in tqdm(examples, desc="Converting examples to features"):
input_id, input_mask, segment_id, label = convert_single_example(tokenizer, example, max_seq_length)
input_ids.append(np.array(input_id))
input_masks.append(np.array(input_mask))
segment_ids.append(np.array(segment_id))
labels.append(np.array(label))
return np.array(input_ids), np.array(input_masks), np.array(segment_ids), np.array(labels).reshape(-1, 1)
def convert_text_to_examples(texts, labels):
"""Create InputExamples"""
InputExamples = []
for text, label in zip(texts, labels):
InputExamples.append(
InputExample(guid=None, text_a=" ".join(text), text_b=None, label=label)
)
return InputExamples
# Initialize session
sess = tf.Session()
params = parse_args()
initialize_logger()
configure_tf()
# Load our config file
config_file_path = os.path.join(os.getcwd(), "config.yaml")
config_file = open(config_file_path)
config_params = yaml.load(config_file)
# This parameter allow that train_x to be in form of words, to allow using of your keras-elmo layer
elmo = config_params["use_elmo"]
dataset = load_dataset(elmo=elmo)
vocabulary_size = dataset.get("vocabulary_size")
output_size = dataset.get("output_size")
# Parse data in Bert format
max_seq_length = 64
train_x = dataset.get("train_x")
train_text = [' '.join(x) for x in train_x]
train_text = [' '.join(t.split()[0:max_seq_length]) for t in train_text]
train_text = np.array(train_text, dtype=object)[:, np.newaxis]
# print(train_text.shape) # (37184, 1)
train_labels = dataset.get("train_y")
# Instantiate tokenizer
tokenizer = create_tokenizer_from_hub_module()
# Convert data to InputExample format
train_examples = convert_text_to_examples(train_text, train_labels)
# Extract features
(train_input_ids, train_input_masks, train_segment_ids, train_labels) = convert_examples_to_features(tokenizer, train_examples, max_seq_length=max_seq_length)
bert_inputs = [train_input_ids, train_input_masks, train_segment_ids]
data = bert_inputs, train_labels
del dataset
model = baseline_model(output_size)
# Instantiate variables
initialize_vars(sess)
history = train_model(model, data)
图层BertEmbeddingLayer()是从 strongio/keras- bert ，以及遵循文件中的方法来集成我的工作，但是我始终会遇到此错误，请检查下面的追溯(在构建模型时会引发异常)
The layer BertEmbeddingLayer() is imported from strongio/keras-bert, as well as following the approach in the file to integrate my work however I always have this error, please check the traceback below (exception is raised when building the model)
Traceback (most recent call last):
File "code/prova_bert.py", line 230, in <module>
model = baseline_model(output_size, max_seq_len, visualize=True)
File "code/prova_bert.py", line 165, in baseline_model
)(bert_embeddings)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\keras\layers\wrappers.py", line 473, in __call__
return super(Bidirectional, self).__call__(inputs, **kwargs)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 746, in __call__
self.build(input_shapes)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\keras\layers\wrappers.py", line 612, in build
self.forward_layer.build(input_shape)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\keras\utils\tf_utils.py", line 149, in wrapper
output_shape = fn(instance, input_shape)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\keras\layers\recurrent.py", line 552, in build
self.cell.build(step_input_shape)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\keras\utils\tf_utils.py", line 149, in wrapper
output_shape = fn(instance, input_shape)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\keras\layers\recurrent.py", line 1934, in build
constraint=self.kernel_constraint)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 609, in add_weight
aggregation=aggregation)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\training\checkpointable\base.py", line 639, in _add_variable_with_custom_getter
**kwargs_for_getter)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1977, in make_variable
aggregation=aggregation)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\ops\variables.py", line 183, in __call__
return cls._variable_v1_call(*args, **kwargs)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\ops\variables.py", line 146, in _variable_v1_call
aggregation=aggregation)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\ops\variables.py", line 125, in <lambda>
previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\ops\variable_scope.py", line 2437, in default_variable_creator
import_scope=import_scope)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\ops\variables.py", line 187, in __call__
return super(VariableMetaclass, cls).__call__(*args, **kwargs)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 297, in __init__
constraint=constraint)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 409, in _init_from_args
initial_value() if init_from_fn else initial_value,
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1959, in <lambda>
shape, dtype=dtype, partition_info=partition_info)
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\ops\init_ops.py", line 473, in __call__
scale /= max(1., (fan_in + fan_out) / 2.)
TypeError: unsupported operand type(s) for +: 'NoneType' and 'int'
Exception ignored in: <bound method BaseSession.__del__ of <tensorflow.python.client.session.Session object at 0x0000026396AD0630>>
Traceback (most recent call last):
File "C:\Users\Sheikh\AppData\Local\Programs\Python\Python36\Lib\site-packages\tensorflow\python\client\session.py", line 738, in __del__
TypeError: 'NoneType' object is not callable
请参阅我的问题上的回购以及有关数据示例的信息馈入模型，请检查此问题
Please refer to my issue on their repo and for data examples being fed to the model please check this issue
推荐答案
首先，"mean"或"first"池的结果并不适用于所有标记，因此您必须在call()函数中进行更改:
First of all, the results by "mean" or "first" pooling is not for all the tokens, so you got to change in call() function:
elif self.pooling == "mean":
result = self.bert(inputs=bert_inputs, signature="tokens", as_dict=True)["sequence_output" ]
pooled = result
在build_model中，更改为:
In build_model, change to:
embedding_size = 768
in_id = Input(shape=(max_seq_length,), name="input_ids")
in_mask = Input(shape=(max_seq_length,), name="input_masks")
in_segment = Input(shape=(max_seq_length,), name="segment_ids")
bert_inputs = [in_id, in_mask, in_segment]
bert_output = BertLayer(n_fine_tune_layers=12, pooling="mean")(bert_inputs)
bert_output = Reshape((max_seq_length, embedding_size))(bert_output)
bilstm = Bidirectional(LSTM(128, dropout=0.2,recurrent_dropout=0.2,return_sequences=True))(bert_output)
output = Dense(output_size, activation="softmax")(bilstm)
这篇关于Bert嵌入层使用BiLSTM引发+:“无类型"和"int"的类型错误:不支持的操作数类型的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！