我正在研究TensorFlow 2.0和Transformer，键入时标题出现错误
value = Embedding(tf.shape(vocals).numpy()[0], d_model=512)
vocals是形状为（100，45）的张量。该层的代码是：
def positional_encoding(length, d_model):
encoded_vec = tf.Variable([pos/tf.pow(10000, 2*i/d_model) for pos in range(length) for i in range(d_model)],
dtype=tf.float32)
encoded_vec[::2] = tf.sin(encoded_vec[::2])
encoded_vec[1::2] = tf.cos(encoded_vec[1::2])
return encoded_vec.reshape([sentence_length, dim])
class Embedding(tf.keras.layers.Layer):
def __init__(self, vocab_size, d_model, dropout=0.1):
super().__init__()
self.d_model = d_model
self.token_embedding = tf.keras.layers.Embedding(vocab_size, d_model)
self.positional_encoding = positional_encoding(vocab_size, d_model)
self.dropout = tf.keras.layers.Dropout(dropout)
def call(self, x):
seq_len = tf.shape(x)[1]
x = self.token_embedding(x)
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x += self.positional_encoding[:, :seq_len, :]
x = self.dropout(x)
return x
更改
encoded_vec = tf.Variable([pos/tf.pow(10000, 2*i/d_model) for pos in range(length) for i in range(d_model)],
dtype=tf.float32)
至
encoded_vec = np.array([pos/10000 ** (2*i/d_model) for pos in range(length) for i in range(d_model)],
dtype=tf.float32)
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