InvalidArgumentError: Specified a list with shape [60,9] from a tensor with shape [56,9]

Question

After running my model for one epoch it crashed with following error message:

InvalidArgumentError: Specified a list with shape [60,9] from a tensor with shape [56,9] [[{{node TensorArrayUnstack/TensorListFromTensor}}]] [[sequential_7/lstm_17/PartitionedCall]] [Op:__inference_train_function_29986]

This happened after I changed the LSTM Layer to stateful=True and had to pass the batch_input_shape Argument instead of the input_shape

Below is my code, I'm sure it has something to do with the shape of my data:

test_split = 0.2
history_points = 60
n = int(histories.shape[0] * test_split)

histories_train = histories[:n]
y_train = next_values_normalized[:n]

histories_test = histories[n:]
y_test = next_values_normalized[n:]

next_values_test = next_values[n:]

print(histories_train.shape)
print(y_train.shape)

-->(1421, 60, 9)
-->(1421, 1)

# model architecture

´´´model = Sequential()
model.add(LSTM(units=128, stateful=True,return_sequences=True, batch_input_shape=(60,history_points, 9)))
model.add(LSTM(units=64,stateful=True,return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=32))
model.add(Dropout(0.2))
model.add(Dense(20))
ADAM=keras.optimizers.Adam(0.0005, beta_1=0.9, beta_2=0.999, amsgrad=False)
model.compile(loss='mean_squared_error', optimizer=ADAM)

model.fit(x=histories_train, y=y_train, batch_size=batchsize, epochs=50, shuffle=False, validation_split=0.2,verbose=1)
´´´

Answer 1

For a stateful LSTM, the batch size should be chosen in a way, so that the number of samples is divisible by the batch size. See also here:

Keras: What if the size of data is not divisible by batch_size?

In your case, considering that you take 20% from your training data as a validation set, you have 1136 samples remaining. So you should choose a batch size by which 1136 is divisible.

Additionally, you could for example remove some samples or reuse samples to be able to choose various batch sizes.