保存“微调”的 bert 模型

我正在尝试保存一个经过微调的 bert 模型。我已经正确运行了代码 - 它工作正常，并且在 ipython 控制台中我可以调用 getPrediction 并让它产生结果。

我保存了体重文件（最高的是 model.ckpt-333.data-00000-of-00001

我不知道如何保存模型以供重复使用。

我正在使用 bert-tensorflow。

import json

import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
from datetime import datetime


from sklearn.model_selection import train_test_split
import os

print("tensorflow version : ", tf.__version__)
print("tensorflow_hub version : ", hub.__version__)


#Importing BERT modules
import bert
from bert import run_classifier
from bert import optimization
from bert import tokenization

#set output directory of the model
OUTPUT_DIR = 'model'

#@markdown Whether or not to clear/delete the directory and create a new one
DO_DELETE = False #@param {type:"boolean"}

if DO_DELETE:
  try:
    tf.gfile.DeleteRecursively(OUTPUT_DIR)
  except:
    pass

tf.io.gfile.makedirs(OUTPUT_DIR)
print('***** Model output directory: {} *****'.format(OUTPUT_DIR))


### Load the data
data = pd.read_csv("data/bbc-text.csv")

data.columns = ['category', 'text']
print('*****Data Loaded: {} *****'.format(data.head()))

#check to see if any null values are present.
print('*****Empty Data: {} *****'.format(data[data.isnull().any(axis=1)]))

#encode category variable into numeric
data.category = pd.Categorical(data.category)
data['code'] = data.category.cat.codes

from sklearn.model_selection import train_test_split

train, test = train_test_split(data, test_size=0.2, random_state=200)

## 2 -- Data Visualisation

print(data.code.unique())

import matplotlib.pyplot as plt

train['code'].value_counts().plot(kind = 'bar')
DATA_COLUMN = 'text'
LABEL_COLUMN = 'code'
label_list = [0, 1, 2, 3, 4]
plt.show()

## 2 -- Data Preprocessing

train_InputExamples = train.apply(lambda x: bert.run_classifier.InputExample(guid=None,
                                                                   text_a = x[DATA_COLUMN],
                                                                   text_b = None,
                                                                   label = x[LABEL_COLUMN]), axis = 1)

test_InputExamples = test.apply(lambda x: bert.run_classifier.InputExample(guid=None,
                                                                   text_a = x[DATA_COLUMN],
                                                                   text_b = None,
                                                                   label = x[LABEL_COLUMN]), axis = 1)

# This is a path to an uncased (all lowercase) version of BERT
BERT_MODEL_HUB = "https://tfhub.dev/google/bert_uncased_L-12_H-768_A-12/1"

def create_tokenizer_from_hub_module():
  """Get the vocab file and casing info from the Hub module."""
  with tf.Graph().as_default():
    bert_module = hub.Module(BERT_MODEL_HUB)
    tokenization_info = bert_module(signature="tokenization_info", as_dict=True)
    with tf.compat.v1.Session() as sess:
      vocab_file, do_lower_case = sess.run([tokenization_info["vocab_file"],
                                            tokenization_info["do_lower_case"]])

  return bert.tokenization.FullTokenizer(
      vocab_file=vocab_file, do_lower_case=do_lower_case)

tokenizer = create_tokenizer_from_hub_module()

# We'll set sequences to be at most 128 tokens long.
MAX_SEQ_LENGTH = 128

# Convert our train and validation features to InputFeatures that BERT understands.
train_features = bert.run_classifier.convert_examples_to_features(train_InputExamples, label_list, MAX_SEQ_LENGTH, tokenizer)

test_features = bert.run_classifier.convert_examples_to_features(test_InputExamples, label_list, MAX_SEQ_LENGTH, tokenizer)


#Example on first observation in the training set
print("Example of train[0] as a training set")
print("Sentence : ", train_InputExamples.iloc[0].text_a)
print("-"*30)
print("Tokens : ", tokenizer.tokenize(train_InputExamples.iloc[0].text_a))
print("-"*30)
print("Input IDs : ", train_features[0].input_ids)
print("-"*30)
print("Input Masks : ", train_features[0].input_mask)
print("-"*30)
print("Segment IDs : ", train_features[0].segment_ids)


## 3. Creating a Multiclass Classifier
def create_model(is_predicting, input_ids, input_mask, segment_ids, labels,
                 num_labels):

  bert_module = hub.Module(
      BERT_MODEL_HUB,
      trainable=True)
  bert_inputs = dict(
      input_ids=input_ids,
      input_mask=input_mask,
      segment_ids=segment_ids)
  bert_outputs = bert_module(
      inputs=bert_inputs,
      signature="tokens",
      as_dict=True)

  # Use "pooled_output" for classification tasks on an entire sentence.
  # Use "sequence_outputs" for token-level output.
  output_layer = bert_outputs["pooled_output"]

  hidden_size = output_layer.shape[-1].value

  # Create our own layer to tune for politeness data.
  output_weights = tf.compat.v1.get_variable(
      "output_weights", [num_labels, hidden_size],
      initializer=tf.truncated_normal_initializer(stddev=0.02))

  output_bias = tf.compat.v1.get_variable(
      "output_bias", [num_labels], initializer=tf.zeros_initializer())

  with tf.compat.v1.variable_scope("loss"):

    # Dropout helps prevent overfitting
    output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)

    logits = tf.matmul(output_layer, output_weights, transpose_b=True)
    logits = tf.nn.bias_add(logits, output_bias)
    log_probs = tf.nn.log_softmax(logits, axis=-1)

    # Convert labels into one-hot encoding
    one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)

    predicted_labels = tf.squeeze(tf.argmax(log_probs, axis=-1, output_type=tf.int32))
    # If we're predicting, we want predicted labels and the probabiltiies.
    if is_predicting:
      return (predicted_labels, log_probs)

    # If we're train/eval, compute loss between predicted and actual label
    per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
    loss = tf.reduce_mean(per_example_loss)
    return (loss, predicted_labels, log_probs)

#A function that adapts our model to work for training, evaluation, and prediction.

# model_fn_builder actually creates our model function
# using the passed parameters for num_labels, learning_rate, etc.
def model_fn_builder(num_labels, learning_rate, num_train_steps,
                     num_warmup_steps):
  """Returns `model_fn` closure for TPUEstimator."""
  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
    """The `model_fn` for TPUEstimator."""

    input_ids = features["input_ids"]
    input_mask = features["input_mask"]
    segment_ids = features["segment_ids"]
    label_ids = features["label_ids"]

    is_predicting = (mode == tf.estimator.ModeKeys.PREDICT)

    # TRAIN and EVAL
    if not is_predicting:

      (loss, predicted_labels, log_probs) = create_model(
        is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)

      train_op = bert.optimization.create_optimizer(
          loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu=False)

      # Calculate evaluation metrics.
      def metric_fn(label_ids, predicted_labels):
        accuracy = tf.compat.v1.metrics.accuracy(label_ids, predicted_labels)
        true_pos = tf.compat.v1.metrics.true_positives(
            label_ids,
            predicted_labels)
        true_neg = tf.compat.v1.metrics.true_negatives(
            label_ids,
            predicted_labels)
        false_pos = tf.compat.v1.metrics.false_positives(
            label_ids,
            predicted_labels)
        false_neg = tf.compat.v1.metrics.false_negatives(
            label_ids,
            predicted_labels)

        return {
            "eval_accuracy": accuracy,
            "true_positives": true_pos,
            "true_negatives": true_neg,
            "false_positives": false_pos,
            "false_negatives": false_neg
            }

      eval_metrics = metric_fn(label_ids, predicted_labels)

      if mode == tf.estimator.ModeKeys.TRAIN:
        return tf.estimator.EstimatorSpec(mode=mode,
          loss=loss,
          train_op=train_op)
      else:
          return tf.estimator.EstimatorSpec(mode=mode,
            loss=loss,
            eval_metric_ops=eval_metrics)
    else:
      (predicted_labels, log_probs) = create_model(
        is_predicting, input_ids, input_mask, segment_ids, label_ids, num_labels)

      predictions = {
          'probabilities': log_probs,
          'labels': predicted_labels
      }
      return tf.estimator.EstimatorSpec(mode, predictions=predictions)

  # Return the actual model function in the closure
  return model_fn

# Compute train and warmup steps from batch size
# These hyperparameters are copied from this colab notebook (https://colab.sandbox.google.com/github/tensorflow/tpu/blob/master/tools/colab/bert_finetuning_with_cloud_tpus.ipynb)
BATCH_SIZE = 16
LEARNING_RATE = 2e-5
NUM_TRAIN_EPOCHS = 3.0
# Warmup is a period of time where the learning rate is small and gradually increases--usually helps training.
WARMUP_PROPORTION = 0.1
# Model configs
SAVE_CHECKPOINTS_STEPS = 300
SAVE_SUMMARY_STEPS = 100

# Compute train and warmup steps from batch size
num_train_steps = int(len(train_features) / BATCH_SIZE * NUM_TRAIN_EPOCHS)
num_warmup_steps = int(num_train_steps * WARMUP_PROPORTION)

# Specify output directory and number of checkpoint steps to save
run_config = tf.estimator.RunConfig(
    model_dir=OUTPUT_DIR,
    save_summary_steps=SAVE_SUMMARY_STEPS,
    save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS)

# Specify output directory and number of checkpoint steps to save
run_config = tf.estimator.RunConfig(
    model_dir=OUTPUT_DIR,
    save_summary_steps=SAVE_SUMMARY_STEPS,
    save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS)


#Initializing the model and the estimator
model_fn = model_fn_builder(
  num_labels=len(label_list),
  learning_rate=LEARNING_RATE,
  num_train_steps=num_train_steps,
  num_warmup_steps=num_warmup_steps)

estimator = tf.estimator.Estimator(
  model_fn=model_fn,
  config=run_config,
  params={"batch_size": BATCH_SIZE})

# Create an input function for training. drop_remainder = True for using TPUs.
train_input_fn = bert.run_classifier.input_fn_builder(
    features=train_features,
    seq_length=MAX_SEQ_LENGTH,
    is_training=True,
    drop_remainder=False)

# Create an input function for validating. drop_remainder = True for using TPUs.
test_input_fn = run_classifier.input_fn_builder(
    features=test_features,
    seq_length=MAX_SEQ_LENGTH,
    is_training=False,
    drop_remainder=False)


# #Training the model
print(f'Beginning Training!')
current_time = datetime.now()
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
print("Training took time ", datetime.now() - current_time)

#Evaluating the model with Validation set
accuracy = estimator.evaluate(input_fn=test_input_fn, steps=None)


# A method to get predictions
def getPrediction(in_sentences):
    # A list to map the actual labels to the predictions
    labels = ["business", "entertainment", "politics", "sports", "tech"]


    # Transforming the test data into BERT accepted form
    input_examples = [run_classifier.InputExample(guid="", text_a=x, text_b=None, label=0) for x in in_sentences]

    # Creating input features for Test data
    input_features = run_classifier.convert_examples_to_features(input_examples, label_list, MAX_SEQ_LENGTH, tokenizer)

    # Predicting the classes
    predict_input_fn = run_classifier.input_fn_builder(features=input_features, seq_length=MAX_SEQ_LENGTH,
                                                       is_training=False, drop_remainder=False)
    predictions = estimator.predict(predict_input_fn)
    return [(sentence, prediction['probabilities'], prediction['labels'], labels[prediction['labels']]) for
            sentence, prediction in zip(in_sentences, predictions)]
pred_sentences = list(test['text'])

predictions = getPrediction(pred_sentences)

enc_labels = []
act_labels = []
for i in range(len(predictions)):
  enc_labels.append(predictions[i][2])
  act_labels.append(predictions[i][3])

pd.DataFrame(enc_labels, columns = ['category']).to_excel('data/submission_bert.xlsx', index = False)

## Random tester
#Classifying random sentences
tests = getPrediction(['Mr.Modi is the Indian Prime Minister',
                       'Gaming machines are powered by efficient micro processores and GPUs',
                       'That HBO TV series is really good',
                       'A trillion dollar economy '
                       ])

正如问题明确指出要保存模型，它的工作原理如下：

import torch
torch.save(model, 'path/to/model')

saved_model = torch.load('path/to/model')