text: Title: LSTM with more than one layer fails with "Cannot feed value ..."
  Body: I'm having an issue where I'm able to train a single layer LSTM without problem but adding a second layer results in a ValueError:
  
  Single layer example:
  ```
  net = tflearn.input_data([None,1000,4])
  net = tflearn.lstm(net,128,dynamic=True,return_seq=False)
  net = tflearn.fully_connected(net,1,activation='linear')
  adam_opt = tflearn.Adam(learning_rate=0.001,epsilon=0.0001)
  net = tflearn.regression(net,optimizer=adam_opt,loss='mean_square')
  model =tflearn.DNN(net,tensorboard_verbose=3)
  model.fit(ss[:-int(N/10)],pirs[:,:-int(N/10)].T,validation_set=(ss[-int(N/10):],pirs[:,-int(N/10):].T),show_metric=True,batch_size=256)
  ```
  
  If I try adding another LSTM layer like this:
  
  ```
  net = tflearn.input_data([None,1000,4])
  net = tflearn.lstm(net,128,return_seq=True,dynamic=True)
  net = tflearn.lstm(net,128,dynamic=True,return_seq=False)
  net = tflearn.fully_connected(net,1,activation='linear')
  adam_opt = tflearn.Adam(learning_rate=0.001,epsilon=0.0001)
  net = tflearn.regression(net,optimizer=adam_opt,loss='mean_square')
  model =tflearn.DNN(net,tensorboard_verbose=3)
  model.fit(ss[:-int(N/10)],pirs[:,:-int(N/10)].T,validation_set=(ss[-int(N/10):],pirs[:,-int(N/10):].T),show_metric=True,batch_size=256)
  ```
  
  I get the following error when trying to train:
  `ValueError: Cannot feed value of shape (256, 1) for Tensor u'TargetsData/Y:0', which has shape '(1000, 1)'`
  
  I'm used to TFLearn getting the dimensions right automatically, so this is a strange error.
  
  What am I doing wrong?