TransformerEstimator应用代码

以下是一个使用TransformerEstimator进行文本分类的示例代码：

import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
from official.nlp import bert

# 加载数据集
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')

# 数据预处理
train_input = bert.run_classifier.convert_examples_to_features(train_df['text'], train_df['label'], label_list=['0', '1'], max_seq_length=128)
test_input = bert.run_classifier.convert_examples_to_features(test_df['text'], test_df['label'], label_list=['0', '1'], max_seq_length=128)

# 定义模型输入
def input_fn(input_features, batch_size=32):
    dataset = tf.data.Dataset.from_tensor_slices(({
        'input_word_ids': input_features.input_ids,
        'input_mask': input_features.input_mask,
        'input_type_ids': input_features.segment_ids}, input_features.label))
    return dataset.shuffle(10000).repeat().batch(batch_size)

# 定义模型结构
def model_fn(features, labels, mode, params):
    input_word_ids = features['input_word_ids']
    input_mask = features['input_mask']
    input_type_ids = features['input_type_ids']
    bert_module = hub.Module(params['bert_hub_module'], trainable=True)
    bert_inputs = dict(input_word_ids=input_word_ids, input_mask=input_mask, input_type_ids=input_type_ids)
    bert_outputs = bert_module(inputs=bert_inputs, signature='tokens', as_dict=True)
    output_layer = bert_outputs['pooled_output']
    hidden_layer = tf.layers.dense(output_layer, units=64, activation=tf.nn.relu)
    logits = tf.layers.dense(hidden_layer, params['num_labels'], activation=None)
    predicted_labels = tf.argmax(logits, axis=-1, output_type=tf.int32)
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions={'class_ids': predicted_labels})
    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits)
    mean_loss = tf.reduce_mean(loss)
    if mode == tf.estimator.ModeKeys.TRAIN:
        optimizer = tf.train.AdamOptimizer(learning_rate=params['learning_rate'])
        train_op = optimizer.minimize(loss=mean_loss, global_step=tf.train.get_global_step())
        return tf.estimator.EstimatorSpec(mode=mode, loss=mean_loss, train_op=train_op)
    eval_metric_ops = {'accuracy': tf.metrics.accuracy(labels=labels, predictions=predicted_labels)}
    return tf.estimator.EstimatorSpec(mode=mode, loss=mean_loss, eval_metric_ops=eval_metric_ops)

# 定义参数
params = {
    'bert_hub_module': 'https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1',
    'num_labels': 2,
    'learning_rate': 1e-5
}

# 创建Estimator
estimator = tf.estimator.Estimator(model_fn=model_fn, params=params)

# 训练模型
estimator.train(input_fn=lambda: input_fn(train_input, batch_size=32), steps=1000)

# 评估模型
estimator.evaluate(input_fn=lambda: input_fn(test_input, batch_size=32))

在这个例子中，我们使用了TensorFlow官方提供的Bert模型来作为Transformer模型的核心，使用了TransformerEstimator来构建模型。我们还定义了输入函数和模型函数来指定数据的处理方式和模型的结构。在训练和评估模型时，我们只需要调用Estimator的train和evaluate方法即可。