Error when checking target: expected dense_3 to have 2 dimension

Question

问题描述

使用Tensorflow的keras来处理一个文本情感多分类问题的，但是在构建模型的时候出现了一些莫名其妙的问题？
Error when checking target: expected dense_3 to have 2 dimensions, but got array with shape (4500, 4, 4)

问题出现的环境背景及自己尝试过哪些方法

Layer (type)                 Output Shape              Param #   
=================================================================
lstm_9 (LSTM)                (150, 1, 32)              772224    
_________________________________________________________________
lstm_10 (LSTM)               (150, 1, 32)              8320      
_________________________________________________________________
lstm_11 (LSTM)               (150, 32)                 8320      
_________________________________________________________________
dense_3 (Dense)              (150, 4)                  132       
=================================================================
Total params: 788,996
Trainable params: 788,996
Non-trainable params: 0

很奇怪，dense_3上一层明明输出的是2 dimensions，为什么这里还会报dim不符合的错误呢？

相关代码

// 请把代码文本粘贴到下方（请勿用图片代替代码）

timesteps = 1
data_dim = 6000
num_classes = 4
batch_size = 150

x_train = train_x_array
x_val = val_x_array

x_train = keras.preprocessing.sequence.pad_sequences(x_train, maxlen=6000, value=0)
x_val = keras.preprocessing.sequence.pad_sequences(x_val, maxlen=6000, value=0)
print('x_train shape: {}'.format(x_train.shape))
print('x_val shape: {}'.format(x_val.shape))

x_train = x_train.reshape([batch_size * 30, timesteps, data_dim])
x_val = x_val.reshape([batch_size * 10, timesteps, data_dim])
print('x_train shape: {}'.format(x_train.shape))
print('x_val shape: {}'.format(x_val.shape))

// x_train shape: (4500, 6000)
// x_val shape: (1500, 6000)
// x_train shape: (4500, 1, 6000)
// x_val shape: (1500, 1, 6000)

model = Sequential()
model.add(LSTM(32, return_sequences=True, stateful=True,
               batch_input_shape=(batch_size, timesteps, data_dim)))
model.add(LSTM(32, return_sequences=True, stateful=True))
model.add(LSTM(32, stateful=True))
model.add(Dense(4, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model.summary()

model.fit(x_train, y_train,
          batch_size=batch_size, epochs=3, shuffle=False,
          validation_data=(x_val, y_val))

请问如何解决？

Answer 1

老哥解决了吗？我有同样的问题