我正在使用Keras功能API创建一个神经网络，该网络将单词嵌入层作为句子分类任务的输入。但是我的代码在连接输入层和嵌入层的开头就中断了。在https://medium.com/tensorflow/predicting-the-price-of-wine-with-the-keras-functional-api-and-tensorflow-a95d1c2c1b03上的教程之后，我的代码如下：
max_seq_length=100 #i.e., sentence has a max of 100 words
word_weight_matrix = ... #this has a shape of 9825, 300, i.e., the vocabulary has 9825 words and each is a 300 dimension vector
deep_inputs = Input(shape=(max_seq_length,))
embedding = Embedding(9825, 300, input_length=max_seq_length,
weights=word_weight_matrix, trainable=False)(deep_inputs) # line A
hidden = Dense(targets, activation="softmax")(embedding)
model = Model(inputs=deep_inputs, outputs=hidden)
然后，A行会导致以下错误：
ValueError: You called `set_weights(weights)` on layer "embedding_1" with a weight list of length 9825, but the layer was expecting 1 weights. Provided weights: [[-0.04057981 0.05743935 0.0109863 ..., 0.0072...
而且我真的不明白错误的含义是什么...
似乎输入层没有正确定义...以前，当我使用顺序模型和嵌入层定义完全相同时，一切正常。但是，当我切换到功能API时，会出现此错误。
任何帮助，不胜感激，在此先感谢
试试这个更新的代码：您必须len(vocabulary) + 1在嵌入层中使用！和weights=[word_weight_matrix]
max_seq_length=100 #i.e., sentence has a max of 100 words
word_weight_matrix = ... #this has a shape of 9825, 300, i.e., the vocabulary has 9825 words and each is a 300 dimension vector
deep_inputs = Input(shape=(max_seq_length,))
embedding = Embedding(9826, 300, input_length=max_seq_length,
weights=[word_weight_matrix], trainable=False)(deep_inputs) # line A
hidden = Dense(targets, activation="softmax")(embedding)
model = Model(inputs=deep_inputs, outputs=hidden)
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