Question

我试图在tensorflow 2.0中训练bert模型并遇到错误（当我点击model.fit时）。任何人都知道这种错误是什么！预先感谢。


!wget --quiet https://raw.githubusercontent.com/tensorflow/models/master/official/nlp/bert/tokenization.py


import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import ModelCheckpoint
import tensorflow_hub as hub

import tokenization

%%time
module_url = "https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/1"
bert_layer = hub.KerasLayer(module_url, trainable=True)


vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()
do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()
tokenizer = tokenization.FullTokenizer(vocab_file, do_lower_case)



def bert_encode(texts, tokenizer, max_len=512):
    all_tokens = []
    all_masks = []
    all_segments = []
    
    for text in texts:
        text = tokenizer.tokenize(text)
            
        text = text[:max_len-2]
        input_sequence = ["[CLS]"] + text + ["[SEP]"]
        pad_len = max_len - len(input_sequence)
        
        tokens = tokenizer.convert_tokens_to_ids(input_sequence)
        tokens += [0] * pad_len
        pad_masks = [1] * len(input_sequence) + [0] * pad_len
        segment_ids = [0] * max_len
        
        all_tokens.append(tokens)
        all_masks.append(pad_masks)
        all_segments.append(segment_ids)
    
    return np.array(all_tokens), np.array(all_masks), np.array(all_segments)

train_input = bert_encode(train.text.values, tokenizer, max_len=160)
test_input = bert_encode(test.text.values, tokenizer, max_len=160)
train_labels = train.target.values


def build_model(bert_layer, max_len=512):
    input_word_ids = Input(shape=(max_len,), dtype=tf.int32, name="input_word_ids")
    input_mask = Input(shape=(max_len,), dtype=tf.int32, name="input_mask")
    segment_ids = Input(shape=(max_len,), dtype=tf.int32, name="segment_ids")

    pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids])
    clf_output = sequence_output[:, 0, :]
    out = Dense(1, activation='sigmoid')(clf_output)
    
    model = Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=out)
    model.compile(Adam(lr=2e-6), loss='binary_crossentropy', metrics=['accuracy'])
    
    return model



model = build_model(bert_layer, max_len=160)
model.summary()


train_history = model.fit(
    train_input, train_labels,
    validation_split=0.3,
    epochs=3,
    batch_size=12
)

**错误：

UnboundLocalError Traceback（最近一次通话）在 3validation_split = 0.3， 4个纪元= 3， ----> 5 batch_size = 12 6） _method_wrapper中的/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py（self，* args，** kwargs） 64 def _method_wrapper（self，* args，** kwargs）： 65如果不是self._in_multi_worker_mode（）：＃pylint：disable = protected-access ---> 66个返回方法（self，* args，** kwargs） 67 68＃已经在run_distribute_coordinator内部运行。 /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit（self，x，y，batch_size，epochs，verbose，callbacks，validate_split，validation_data，shuffle，class_weight ，sample_weight，initial_epoch，steps_per_epoch，validation_steps，validation_batch_size，validation_freq，max_queue_size，worker，use_multiprocessing） 854日志= tmp_logs＃没有错误，现在可以安全地分配给日志了。 855个callbacks.on_train_batch_end（步骤，日志） -> 856个epoch_logs = copy.copy（logs） 857 858＃运行验证。

UnboundLocalError：分配前已引用局部变量“日志” **

在tensorflow 2.0上训练bert模型时出现错误

**错误：

0 个答案: