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tensorflow - 运行 TensorFlow Transformer 教程时出现问题

问题描述

我正在检查 TensorFlow 教程“用于语言理解的转换器模型”，并将代码完全原样复制到我的 Spyder 4 环境中。但是，代码在运行时显示以下错误：

AttributeError: 'RepeatedCompositeFieldContainer' object has no attribute 'append'

我检查了代码，发现错误来自MultiHeadAttention类的调用函数。但是，我不明白问题出在哪里，因为代码在 Colab 笔记本中运行得很好。

class MultiHeadAttention(tf.keras.layers.Layer):


  def __init__(self, d_model, num_heads):
    super(MultiHeadAttention, self).__init__()
    self.num_heads = num_heads
    self.d_model = d_model

    assert d_model % self.num_heads == 0

    self.depth = d_model // self.num_heads

    self.wq = tf.keras.layers.Dense(d_model)
    self.wk = tf.keras.layers.Dense(d_model)
    self.wv = tf.keras.layers.Dense(d_model)

    self.dense = tf.keras.layers.Dense(d_model)



def split_heads(self, x, batch_size):
    """Split the last dimension into (num_heads, depth).
    Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)
    """
    x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
    return tf.transpose(x, perm=[0, 2, 1, 3])

def call(self, v, k, q, mask):
    batch_size = tf.shape(q)[0]

    q = self.wq(q)  # (batch_size, seq_len, d_model)
    k = self.wk(k)  # (batch_size, seq_len, d_model)
    v = self.wv(v)  # (batch_size, seq_len, d_model)

    q = self.split_heads(q, batch_size)  # (batch_size, num_heads, seq_len_q, depth)
    k = self.split_heads(k, batch_size)  # (batch_size, num_heads, seq_len_k, depth)
    v = self.split_heads(v, batch_size)  # (batch_size, num_heads, seq_len_v, depth)

    # scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)
    # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)
    scaled_attention, attention_weights = scaled_dot_product_attention(
        q, k, v, mask)

    scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])  # (batch_size, seq_len_q, 
        num_heads, depth)

    concat_attention = tf.reshape(scaled_attention, 
                              (batch_size, -1, self.d_model))  # (batch_size, seq_len_q, d_model)

    output = self.dense(concat_attention)  # (batch_size, seq_len_q, d_model)

    return output, attention_weights

q = self.wq(q)执行call 函数中的行时显示错误。任何帮助将不胜感激。

提前致谢。

标签： tensorflowattributeerrortransformer

解决方案

我怀疑问题是你的protobufPython 包版本太旧了。应该是>=3.8.0。请参阅此处的故障排除。

我看到了同样的错误信息，升级protobuf被证明是解决方案。在我的情况下，要确保 Python 在旧 Python 安装、虚拟环境和指向旧安装的 PYTHONPATH 的迷宫中找到升级包时会感到额外的痛苦protobuf。

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