ValueError: Error when checking target: expected dense_22 to have 3 dimensions, but got array with shape (1600, 2)

Question

我是 DL 的新手，我一直在尝试使用 seq2seq 模型从这个 repo 中对文本（情感分析）进行分类。我使用的数据集是 amazon review polarity（前 2000 行）。数据集基本上由标签和相应的文本组成。我的模型如下：

sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') #MAX_SEQUENCE_LENGTH = 1000
embedded_sequences = embedding_layer(sequence_input)
l_gru = Bidirectional(GRU(100, return_sequences=True))(embedded_sequences)
l_att = AttLayer()(l_gru)
preds = Dense(2, activation='softmax')(l_att)
model = Model(sequence_input, preds)

model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy',metrics=['accuracy'])
print("model fitting - attention GRU network")
model.summary()
model.fit(x_train, y_train, validation_data=(x_val, y_val),
          epochs=5,verbose = 1, batch_size=50)

model.save('s2s.h5')

输出：

model fitting - attention GRU network
Model: "model_23"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_24 (InputLayer)        (None, 1000)              0         
_________________________________________________________________
embedding_11 (Embedding)     (None, 1000, 100)         1276800   
_________________________________________________________________
bidirectional_24 (Bidirectio (None, 1000, 200)         120600    
_________________________________________________________________
att_layer_24 (AttLayer)      (None, 1000, 200)         200       
_________________________________________________________________
dense_23 (Dense)             (None, 1000, 2)           402       
=================================================================
Total params: 1,398,002
Trainable params: 1,398,002
Non-trainable params: 0

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-112-ec37b13f1d7e> in <module>()
      1 model.fit(x_train, y_train, validation_data=(x_val, y_val),
----> 2           epochs=5,verbose = 1, batch_size=50)
      3 
      4 model.save('s2s.h5')

2 frames

/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    129                         ': expected ' + names[i] + ' to have ' +
    130                         str(len(shape)) + ' dimensions, but got array '
--> 131                         'with shape ' + str(data_shape))
    132                 if not check_batch_axis:
    133                     data_shape = data_shape[1:]

ValueError: Error when checking target: expected dense_22 to have 3 dimensions, but got array with shape (1600, 2)

测试和验证数据集的维度：

print(x_train.shape)
print(x_val.shape)
print(y_train.shape)
print(y_val.shape)

输出：

(1600, 1000)
(400, 1000)
(1600, 2)
(400, 2)

我还提到了其他类似的问题，例如：。但是找不到任何 leads.I 如果这些还不够，我准备提供有关我的实施的更多详细信息。提前致谢。

Answer 1

经过一些试验后，我意识到我一直在尝试使用 2D 输入，而实际代码使用的是 3D 输入。我提到了个问题，它有一个几乎相似的查询和我的查询的解决方案。

ValueError: Error when checking target: expected dense_22 to have 3 dimensions, but got array with shape (1600, 2)

ValueError: Error when checking target: expected dense_22 to have 3 dimensions, but got array with shape (1600, 2)

python

deep-learning

keras

tensorflow

seq2seq