title: Simple LSTM ValueError Python
body: <p>I want to LSTM with 3 Dimension data(train_value ) , 1 Dimensiton labeled data(train_label_list).
  But It has Error(ValueError).
  I cant fix it</p>
  <pre><code>from keras.models import Sequential
  from keras.layers import Dense, LSTM, RepeatVector, TimeDistributed
  
  train_value = [[[0.4, 0.1, 0.2],[0.2, 0.1,0.4]], [[0.4, 0.1, 0.2],[0.2, 0.1,0.4]]]
  train_label_list = [1.0, 2.0]
  
  x_train = np.array(train_value)
  y_train = np.array(train_label_list)
  
  x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], x_train.shape[2]))
  y_train = y_train.reshape(y_train.shape[0])
  
  model = Sequential()
  model.add(LSTM(100, activation='relu', input_shape=(2, 3)))
  model.add(RepeatVector(1))
  model.add(LSTM(100, activation='relu', return_sequences=True))
  model.add(TimeDistributed(Dense(1)))
  model.compile(optimizer='adam', loss='mse')
  model.fit(x_train, y_train, epochs=1000, verbose=0)
  
  test_value = [[0.2, 0.1, 0.2],[0.2, 0.3,0.4]]
  
  x_test = np.array(test_value)
  x_test = x_test.reshape((x_test.shape[0], x_test.shape[1], 1))
  
  yhat = model.predict(x_test)
  
  print(yhat)
  </code></pre>
  <p>Error</p>
  <pre><code>ValueError: Input 0 is incompatible with layer sequential: expected shape=(None, None, 3), found shape=[None, 3, 1]
  </code></pre>