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我得到了 keras 尺寸错误

输入形状是这样的

print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)

结果

(5739, 1, 8) (5739,) (1435, 1, 8) (1435,)

型号如下

batch_size=128
epochs=20
from keras_self_attention import SeqSelfAttention
from keras.layers import Flatten
model = keras.models.Sequential()
model.add(keras.layers.LSTM(epochs, input_shape=(train_X.shape[0], train_X.shape[2]), return_sequences=True))
model.add(Flatten())
model.add(keras.layers.Dense(units=1))
model.compile(loss='mse', optimizer='adam')
model.summary()

结果

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_33 (LSTM)               (None, 5739, 20)          2320      
_________________________________________________________________
seq_self_attention_35 (SeqSe (None, 5739, 20)          1345      
_________________________________________________________________
flatten_8 (Flatten)          (None, 114780)            0         
_________________________________________________________________
dense_33 (Dense)             (None, 1)                 114781    
=================================================================
Total params: 118,446
Trainable params: 118,446
Non-trainable params: 0
_________________________________________________________________

但我在合适的步骤中有错误

history = model.fit(train_X, train_y, epochs=epochs, batch_size=batch_size, validation_data=(test_X, test_y), verbose=2, shuffle=False)

错误

ValueError: Error when checking input: expected lstm_33_input to have shape (5739, 8) but got array with shape (1, 8)

但我打印输入形状是（5739,8），我不明白（1,8）来自哪里。以及如何解决它。

input_shape=(train_X.shape[0], train_X.shape[2])
print(input_shape)
(5739, 8)

是 test_X、test_Y 还是 train 中输入形状的问题？我应该如何解决它？

4

1 回答 1

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Keras 中的 LSTM 层需要一批形状为 的数据(n_timesteps, n_features)。您正在使用错误的尺寸构建图层。

首先，将您的训练数据重塑为形状n_data_points, n_timesteps, n_features：

train_X_ = np.swapaxes(train_X, 1, 2)
train_X_.shape # now of shape (5739, 8, 1)

然后使用正确的尺寸指定您的模型：

model = keras.models.Sequential()
# input shape for the LSTM layer will be (8,1). No need to specify the batch shape.
model.add(keras.layers.LSTM(20, input_shape=(train_X_.shape[1], train_X_.shape[2]), return_sequences=True)) 
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(1))

model.compile(optimizer='adam', loss='mse')

这将正常工作：

model.fit(train_X_, train_y)
于 2018-12-17T13:02:36.847 回答