运行Tensorflow Transformer教程时遇到的问题
我正在查看TensorFlow教程“语言理解的变压器模型”,并将代码完全复制到Spyder 4环境中。但是,代码在运行时显示以下错误:运行Tensorflow Transformer教程时遇到的问题,tensorflow,attributeerror,transformer,Tensorflow,Attributeerror,Transformer,我正在查看TensorFlow教程“语言理解的变压器模型”,并将代码完全复制到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。然而,我不明白问题是什么,因为代码在Colab笔记本中运行得很好 class MultiHeadAttention(tf
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。然而,我不明白问题是什么,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
在调用函数中执行q=self.wq(q)
行时显示错误。任何帮助都将不胜感激
提前感谢。我怀疑问题在于您的
protobuf
Python包版本太旧。它应该是=3.8.0
我看到了相同的错误消息,并且。在我的例子中,确保Python在旧Python安装、虚拟环境和PYTHONPATH的残余迷宫中找到升级包是一件非常痛苦的事情,PYTHONPATH指向一个带有旧protobuf
的旧安装