我对TensorFlow和LSTM架构还比较陌生。我在处理数据集的输入和输出（x_train, x_test, y_train, y_test）时遇到了问题。

我的输入的原始形状如下：

• x_train: (366,4)
• x_test: (104,4)
• y_train: (366,)
• y_test: (104,)

y_train和y_test是一系列股票价格。x_train和x_test是我用来预测股票价格的四个特征。

# 分割训练和测试数据
train_start_date = '2010-01-08'
train_end_date = '2017-01-06'
test_start_date = '2017-01-13'
test_end_date = '2019-01-04'
train = df.ix[train_start_date : train_end_date]
test = df.ix[test_start_date:test_end_date]
X_test = sentimentScorer(test)
X_train = sentimentScorer(train)
Y_test = test['prices'] 
Y_train = train['prices']
# 转换为LSTM输入的3D数组
X_test = X_test.reshape(1, 104, 4)
X_train = X_train.reshape(1, 366, 4)
model = Sequential()
model.add(LSTM(128, input_shape=(366,4), activation='relu', return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(128, activation='relu'))
model.add(Dropout(0.1))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(10, activation='softmax'))
opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)
# 编译模型
model.compile(    
    loss='sparse_categorical_crossentropy',    
    optimizer=opt,    
    metrics=['accuracy'],
)
model.fit(X_train,          
          Y_train,          
          epochs=3,          
          validation_data=(X_test, Y_test))

这是生成的错误：

> --------------------------------------------------------------------------- ValueError                                Traceback (most recent call> last) <ipython-input-101-fd4099583529> in <module>>      65           Y_train,>      66           epochs=3,> ---> 67           validation_data=(X_test, Y_test))> > c:\users\talal\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\keras\engine\training.py> in fit(self, x, y, batch_size, epochs, verbose, callbacks,> validation_split, validation_data, shuffle, class_weight,> sample_weight, initial_epoch, steps_per_epoch, validation_steps,> **kwargs)    1507         steps_name='steps_per_epoch',    1508         steps=steps_per_epoch,> -> 1509         validation_split=validation_split)    1510     1511     # Prepare validation data.> > c:\users\talal\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\keras\engine\training.py> in _standardize_user_data(self, x, y, sample_weight, class_weight,> batch_size, check_steps, steps_name, steps, validation_split)>     991       x, y = next_element>     992     x, y, sample_weights = self._standardize_weights(x, y, sample_weight,> --> 993                                                      class_weight, batch_size)>     994     return x, y, sample_weights>     995 > > c:\users\talal\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\keras\engine\training.py> in _standardize_weights(self, x, y, sample_weight, class_weight,> batch_size)    1110         feed_input_shapes,    1111        > check_batch_axis=False,  # Don't enforce the batch size.> -> 1112         exception_prefix='input')    1113     1114     if y is not None:> > c:\users\talal\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\keras\engine\training_utils.py> in standardize_input_data(data, names, shapes, check_batch_axis,> exception_prefix)>     314                            ': expected ' + names[i] + ' to have ' +>     315                            str(len(shape)) + ' dimensions, but got array '> --> 316                            'with shape ' + str(data_shape))>     317         if not check_batch_axis:>     318           data_shape = data_shape[1:]> > ValueError: Error when checking input: expected lstm_18_input to have> 3 dimensions, but got array with shape (366, 4)

回答：

你的代码基本上是正确的。

你的y_test和y_train应该是一个元素的数组或形状为(1,1)的数组，这没有关系。

不过你的输入形状是错误的，第一个LSTM应该这样设置：

model.add(LSTM(128, input_shape=(None,4), activation='relu', return_sequences=True))

注意None，因为你的测试和训练序列长度不同，你不能指定它（Keras接受第一个维度未指定）。错误是由于长度分别为366和104引起的。如果你想在RNN中使用批处理，你应该使用keras.preprocessing.sequence.pad_sequences进行零填充。

不需要在批处理中指定input_shape，网络的其他部分应该没问题。

如果你在进行回归，而不是分类，可能是这种情况，你应该执行@Ankish Bansal提到的最后两个步骤，例如将损失函数改为mean squared error，并将最后一层的输出值改为1而不是10。