我对 TensorFlow 和 LSTM 架构还很陌生。我在计算数据集的输入和输出(x_train、x_test、y_train、y_test)时遇到问题。
我输入的原始形状:
- 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 是我想用来预测股票价格的四个特征。
# Splitting the training and testing data
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']
#Conversion in 3D array for LSTM INPUT
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)
# Compile model
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'],
)
model.fit(X_train,
Y_train,
epochs=3,
validation_data=(X_test, Y_test))
这是产生的错误:
> --------------------------------------------------------------------------- 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)
请您参考如下方法:
您的代码几乎没问题。
您的 y_test 和 y_train 应该是一个只有一个元素的数组或形状为 (1,1) 的数组，这并不重要。
虽然你的输入形状是错误的，第一个 LSTM 应该是:
model.add(LSTM(128, input_shape=(None,4), activation='relu', return_sequences=True))
注意 None，因为您的测试和训练序列长度不同，您不能指定它(并且 Keras 接受未指定的第一维)。错误是由于长度分别为 366 和 104。如果您想对 RNN 使用批处理，您应该使用 keras.preprocessing.sequence.pad_sequences 执行零填充。
无需为批处理指定 input_shape，网络的其余部分应该没问题。
如果您正在执行回归，而不是分类，可能是这种情况，您应该执行@Ankish Bansal 编写的最后两个步骤，例如将损失更改为 均方误差 并使最后一层输出 1 个值而不是 10。
