title: InvalidArgumentError: Specified a list with shape [60,9] from a tensor with shape [56,9]
body: <p>After running my model for one epoch it crashed with following error message:</p>
  <p><em>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]</em></p>
  <p>This happened after I changed the LSTM Layer to <code>stateful=True</code> and had to pass
  the <code>batch_input_shape</code> Argument instead of the <code>input_shape</code></p>
  <p>Below is my code, I'm sure it has something to do with the shape of my data:</p>
  <pre><code>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)
  
  --&gt;(1421, 60, 9)
  --&gt;(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)
  ´´´
  </code></pre>