tensorflow - 在自定义数据上微调 BERT

Question

我想使用 Bert 训练一个21 类 文本分类模型。但是我的训练数据很少，所以下载了一个类似的数据集，其中包含 5 类 和 200 万个样本。t 并使用 bert 提供的 uncased 预训练模型对下载数据进行微调。 并获得了大约 98% 的验证准确率。 现在，我想将此模型用作我的小型自定义数据的预训练模型。 但是我收到了 shape mismatch with tensor output_bias from checkpoint reader 错误，因为检查点模型有 5 个类，而我的自定义数据有 21 个类。

NFO:tensorflow:Calling model_fn.
INFO:tensorflow:Running train on CPU
INFO:tensorflow:*** Features ***
INFO:tensorflow:  name = input_ids, shape = (32, 128)
INFO:tensorflow:  name = input_mask, shape = (32, 128)
INFO:tensorflow:  name = is_real_example, shape = (32,)
INFO:tensorflow:  name = label_ids, shape = (32, 21)
INFO:tensorflow:  name = segment_ids, shape = (32, 128)
Tensor("IteratorGetNext:3", shape=(32, 21), dtype=int32)
WARNING:tensorflow:From /home/user/Spine_NLP/bert/modeling.py:358: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
WARNING:tensorflow:From /home/user/Spine_NLP/bert/modeling.py:671: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.
Instructions for updating:
Use keras.layers.dense instead.
INFO:tensorflow:num_labels:21;logits:Tensor("loss/BiasAdd:0", shape=(32, 21), dtype=float32);labels:Tensor("loss/Cast:0", shape=(32, 21), dtype=float32)
INFO:tensorflow:Error recorded from training_loop: Shape of variable output_bias:0 ((21,)) doesn't match with shape of tensor output_bias ([5]) from checkpoint reader.

Answer 1

如果您想使用具有 5 个类的预训练模型来微调您自己的模型，您可能需要添加多一层以将 5 个类投影到您的 21 个类中。 p>

您看到的错误是由于您可能没有定义一组新的“output_weights”和“output_bias”，而是将它们重新用于具有 21 个类的新标签。下面我用“final_”为你的新标签“前缀”了中间张量。

代码应该如下所示:

# These are the logits for the 5 classes. Keep them as is.
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)

# You want to create one more layer
final_output_weights = tf.get_variable(
  "final_output_weights", [21, 5],
  initializer=tf.truncated_normal_initializer(stddev=0.02))
final_output_bias = tf.get_variable(
  "final_output_bias", [21], initializer=tf.zeros_initializer())

final_logits = tf.matmul(logits, final_output_weights, transpose_b=True)
final_logits = tf.nn.bias_add(final_logits, final_output_bias)

# Below is for evaluating the classification.
final_probabilities = tf.nn.softmax(final_logits, axis=-1)
final_log_probs = tf.nn.log_softmax(final_logits, axis=-1)

# Note labels below should be the 21 class ids.
final_one_hot_labels = tf.one_hot(labels, depth=21, dtype=tf.float32)
final_per_example_loss = -tf.reduce_sum(final_one_hot_labels * final_log_probs, axis=-1)
final_loss = tf.reduce_mean(final_per_example_loss)