TypeError：期望的“可训练”论点是布尔人，但得到了：伯特

Question

实现模型时，我遇到了这个错误。我认为这些错误来自我导入的BERT模型。

def create_text_encoder(
    num_projection_layers, projection_dims, dropout_rate, trainable=False
):
    # Load the BERT preprocessing module.
    preprocess = hub.KerasLayer(
        "https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/2",
        name="text_preprocessing",
    )
    # Load the pre-trained BERT model to be used as the base encoder.
    bert = hub.KerasLayer(
        "https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1",
        "bert",
    )
    # Set the trainability of the base encoder.
    bert.trainable = trainable
    # Receive the text as inputs.
    inputs = layers.Input(shape=(), dtype=tf.string, name="text_input")
    # Preprocess the text.
    bert_inputs = preprocess(inputs)
    # Generate embeddings for the preprocessed text using the BERT model.
    embeddings = bert(bert_inputs)["pooled_output"]
    # Project the embeddings produced by the model.
    outputs = project_embeddings(
        embeddings, num_projection_layers, projection_dims, dropout_rate
    )
    # Create the text encoder model.
    return keras.Model(inputs, outputs, name="text_encoder")

错误显示在下面的代码中，但我认为问题在上方。

num_epochs = 5  # In practice, train for at least 30 epochs
batch_size = 256

vision_encoder = create_vision_encoder(
    num_projection_layers=1, projection_dims=256, dropout_rate=0.1
)
text_encoder = create_text_encoder(
    num_projection_layers=1, projection_dims=256, dropout_rate=0.1
)
dual_encoder = DualEncoder(text_encoder, vision_encoder, temperature=0.05)
dual_encoder.compile(
    optimizer=tfa.optimizers.AdamW(learning_rate=0.001, weight_decay=0.001)
)

谢谢。

Answer 1

在以相同的代码面对此问题后，我再次看到了这个问题，现在我正在写一个答案。

上面的代码中缺少一个关键字名称

 bert = hub.KerasLayer(
    "https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1",
    "bert",
)

我

bert = hub.KerasLayer(
    "https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-4_H-512_A-8/1",
    name = "bert",
)

只需放置name =“ bert”，现在就可以使用它。