基于this stackoverflow post和this one，我正在尝试构建RNN LSTM模型来预测回归问题。
我的数据是25个批次的2720个样本，每个样本具有16个特征，有些批次用-10值填充。我建立了以下模型：
model = Sequential()
opt = Adam(learning_rate=0.0001, clipnorm=1)
num_samples = train_x.shape[1]
num_features = train_x.shape[2]
# Masking -10 rows
model.add(Masking(mask_value=-10., input_shape=(num_samples, num_features)))
model.add(LSTM(32, return_sequences=True, stateful=False activation='tanh'))
model.add(Dropout(0.3))
#this is the last LSTM layer, use return_sequences=False
model.add(LSTM(16, return_sequences=True, stateful=False, activation='tanh'))
model.add(Dropout(0.3))
model.add(Dense(16, activation='tanh'))
model.add(Dense(8, activation='tanh'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='mse', optimizer='adam' ,metrics=[metrics.mean_absolute_error, metrics.mean_squared_error])
摘要：
Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
masking_4 (Masking) (None, 2720, 16) 0
_________________________________________________________________
lstm_8 (LSTM) (None, 2720, 32) 6272
_________________________________________________________________
dropout_8 (Dropout) (None, 2720, 32) 0
_________________________________________________________________
lstm_9 (LSTM) (None, 2720, 16) 3136
_________________________________________________________________
dropout_9 (Dropout) (None, 2720, 16) 0
_________________________________________________________________
dense_12 (Dense) (None, 2720, 16) 272
_________________________________________________________________
dense_13 (Dense) (None, 2720, 8) 136
_________________________________________________________________
dense_14 (Dense) (None, 2720, 1) 9
=================================================================
Total params: 9,825
Trainable params: 9,825
Non-trainable params: 0
_________________________________________________________________
在训练时，模型不是有状态的，并且在预测要建立相同的模型时，这次是有状态的模型，该模型没有遮罩层，批处理大小为1：
s_model = Sequential()
...
s_model.add(LSTM(32, return_sequences=True, stateful=True, activation='tanh',batch_input_shape=(1, num_samples, num_features)))
...
s_model.add(LSTM(16, return_sequences=True, stateful=True, activation='tanh'))
...
s_model.summary()
摘要：
Model: "sequential_23"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_41 (LSTM) (1, 2720, 32) 6272
_________________________________________________________________
dropout_31 (Dropout) (1, 2720, 32) 0
_________________________________________________________________
lstm_42 (LSTM) (1, 2720, 16) 3136
_________________________________________________________________
dropout_32 (Dropout) (1, 2720, 16) 0
_________________________________________________________________
dense_39 (Dense) (1, 2720, 16) 272
_________________________________________________________________
dense_40 (Dense) (1, 2720, 8) 136
_________________________________________________________________
dense_41 (Dense) (1, 2720, 1) 9
=================================================================
Total params: 9,825
Trainable params: 9,825
Non-trainable params: 0
_________________________________________________________________
我将权重加载到状态模型中，并尝试通过样本进行预测（过滤-10个样本并在每个序列后重置，如下所示：
#loading weights from trained model
s_model.set_weights(model.get_weights())
for sequence in test_x:
for sample in sequence:
#filtering padded samples
if sample[0] is not -10:
score = s_model.predict_on_batch([[[sample]]])
print(score)
print("-----------------------------------------------------")
s_model.reset_states()
但是，我的代码失败，并且出现以下错误：
ValueError: Error when checking input: expected lstm_39_input to have shape (2720, 16) but got array with shape (1, 16)
任何帮助将不胜感激