编译keras模型后“无”是什么意思？

Question

我正在尝试使用 keras 层实现二进制文本分类模型。编译模型后，总而言之，我在底部得到 None，我不完全理解它的含义？

这是我正在使用的代码。

max_words = 10000
max_len = 500
tok = Tokenizer(num_words=max_words)
tok.fit_on_texts(X_train)
sequences = tok.texts_to_sequences(X_train)
sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)

model = Sequential()
model.add(Embedding(max_words, 50, input_length=max_len))
model.add(LSTM(64))
model.add(Dense(256,name='FC1',activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics= 
             ['acc'])
print(model.summary())

这是模型摘要，在底部显示无。

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None, 500, 50)           500000    
_________________________________________________________________
lstm_1 (LSTM)                (None, 64)                29440     
_________________________________________________________________
FC1 (Dense)                  (None, 256)               16640     
_________________________________________________________________
dropout_1 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 257       
=================================================================
Total params: 546,337
Trainable params: 546,337
Non-trainable params: 0
_________________________________________________________________
None

Answer 1

model.summary() 不返回任何内容 (None)，并且您正在打印它的返回值。 model.summary() 已经在内部进行了打印，无需与手动打印混淆，所以只需：

model.summary()