Error WHile running demo about returnn HOT 3 CLOSED

Train data:
  input: 1 x 1
  output: {'classes': [79, 1], 'sizes': [2, 1], 'data': [1, 2]}
  HDF dataset, sequences: 3, frames: 764399
Devices:
  cpu0: cpu0 (units: 1 clock: 1.08Ghz memory: 2.0GB) working on 1 batch (update on device)
Learning-rate-control: no file specified, not saving history (no proper restart possible)
using adam with nag and momentum schedule
Network layer topology:
  input #: 1
  hidden 1Dto2D '1Dto2D' #: 1
  hidden source 'classes_source' #: 2
  hidden conv2 'conv0' #: 15
  hidden conv2 'conv1' #: 45
  hidden conv2 'conv2' #: 75
  hidden conv2 'conv3' #: 105
  hidden conv2 'conv4' #: 105
  hidden mdlstm 'mdlstm0' #: 30
  hidden mdlstm 'mdlstm1' #: 60
  hidden mdlstm 'mdlstm2' #: 90
  hidden mdlstm 'mdlstm3' #: 120
  hidden mdlstm 'mdlstm4' #: 120
  output softmax 'output' #: 82
net params #: 2627902
net trainable params: [W_conv0, b_conv0, W_conv1, b_conv1, W_conv2, b_conv2, W_conv3, b_conv3, W_conv4, b_conv4, U1_mdlstm0, U2_mdlstm0, U3_mdlstm0, U4_mdlstm0, V1_mdlstm0, V2_mdlstm0, V3_mdlstm0, V4_mdlstm0, W1_mdlstm0, W2_mdlstm0, W3_mdlstm0, W4_mdlstm0, b1_mdlstm0, b2_mdlstm0, b3_mdlstm0, b4_mdlstm0, U1_mdlstm1, U2_mdlstm1, U3_mdlstm1, U4_mdlstm1, V1_mdlstm1, V2_mdlstm1, V3_mdlstm1, V4_mdlstm1, W1_mdlstm1, W2_mdlstm1, W3_mdlstm1, W4_mdlstm1, b1_mdlstm1, b2_mdlstm1, b3_mdlstm1, b4_mdlstm1, U1_mdlstm2, U2_mdlstm2, U3_mdlstm2, U4_mdlstm2, V1_mdlstm2, V2_mdlstm2, V3_mdlstm2, V4_mdlstm2, W1_mdlstm2, W2_mdlstm2, W3_mdlstm2, W4_mdlstm2, b1_mdlstm2, b2_mdlstm2, b3_mdlstm2, b4_mdlstm2, U1_mdlstm3, U2_mdlstm3, U3_mdlstm3, U4_mdlstm3, V1_mdlstm3, V2_mdlstm3, V3_mdlstm3, V4_mdlstm3, W1_mdlstm3, W2_mdlstm3, W3_mdlstm3, W4_mdlstm3, b1_mdlstm3, b2_mdlstm3, b3_mdlstm3, b4_mdlstm3, U1_mdlstm4, U2_mdlstm4, U3_mdlstm4, U4_mdlstm4, V1_mdlstm4, V2_mdlstm4, V3_mdlstm4, V4_mdlstm4, W1_mdlstm4, W2_mdlstm4, W3_mdlstm4, W4_mdlstm4, b1_mdlstm4, b2_mdlstm4, b3_mdlstm4, b4_mdlstm4, W_in_mdlstm4_output, b_output]
start training at epoch 1 and batch 0
using batch size: 600000, max seqs: 10
learning rate control: ConstantLearningRate(defaultLearningRate=0.0005, minLearningRate=0.0, defaultLearningRates={1: 0.0005, 25: 0.0003, 35: 0.0001}, errorMeasureKey=None, relativeErrorAlsoRelativeToLearningRate=False, minNumEpochsPerNewLearningRate=0, filename=None), epoch data: 1: EpochData(learningRate=0.0005, error={}), 25: EpochData(learningRate=0.0003, error={}), 35: EpochData(learningRate=0.0001, error={}), error key: None
pretrain: None
start epoch 1 with learning rate 0.0005 ...
TaskThread train failed
Unhandled exception <class 'AssertionError'> in thread <TrainTaskThread(TaskThread train, started daemon 140403630638848)>, proc 7390.
EXCEPTION
Traceback (most recent call last):
  File "/home/mpl2/Projects/returnn/EngineTask.py", line 377, in run
    line: self.run_inner()
    locals:
      self = <local> <TrainTaskThread(TaskThread train, started daemon 140403630638848)>
      self.run_inner = <local> <bound method TaskThread.run_inner of <TrainTaskThread(TaskThread train, started daemon 140403630638848)>>
  File "/home/mpl2/Projects/returnn/EngineTask.py", line 402, in run_inner
    line: device.prepare(epoch=self.epoch, **self.get_device_prepare_args())
    locals:
      device = <local> <Device.Device object at 0x7fb27eeae898>
      device.prepare = <local> <bound method Device.prepare of <Device.Device object at 0x7fb27eeae898>>
      epoch = <not found>
      self = <local> <TrainTaskThread(TaskThread train, started daemon 140403630638848)>
      self.epoch = <local> 1
      self.get_device_prepare_args = <local> <bound method TrainTaskThread.get_device_prepare_args of <TrainTaskThread(TaskThread train, started daemon 140403630638848)>>
  File "/home/mpl2/Projects/returnn/Device.py", line 1424, in prepare
    line: self.set_net_params(network)
    locals:
      self = <local> <Device.Device object at 0x7fb27eeae898>
      self.set_net_params = <local> <bound method Device.set_net_params of <Device.Device object at 0x7fb27eeae898>>
      network = <local> <Network.LayerNetwork object at 0x7fb24dd05208>
  File "/home/mpl2/Projects/returnn/Device.py", line 1237, in set_net_params
    line: self.trainnet.set_params_by_dict(network.get_params_dict())
    locals:
      self = <local> <Device.Device object at 0x7fb27eeae898>
      self.trainnet = <local> <Network.LayerNetwork object at 0x7fb27eeaeb70>
      self.trainnet.set_params_by_dict = <local> <bound method LayerNetwork.set_params_by_dict of <Network.LayerNetwork object at 0x7fb27eeaeb70>>
      network = <local> <Network.LayerNetwork object at 0x7fb24dd05208>
      network.get_params_dict = <local> <bound method LayerNetwork.get_params_dict of <Network.LayerNetwork object at 0x7fb24dd05208>>
  File "/home/mpl2/Projects/returnn/Network.py", line 680, in set_params_by_dict
    line: self.output[name].set_params_by_dict(params[name])
    locals:
      self = <local> <Network.LayerNetwork object at 0x7fb27eeaeb70>
      self.output = <local> {'output': <<class 'NetworkOutputLayer.SequenceOutputLayer'> class:softmax name:output>}
      name = <local> 'output', len = 6
      set_params_by_dict = <not found>
      params = <local> {'output': {'b_output': array([-4.406719, -4.406719, -4.406719, -4.406719, -4.406719, -4.406719,
                              -4.406719, -4.406719, -4.406719, -4.406719, -4.406719, -4.406719,
                              -4.406719, -4.406719, -4.406719, -4.406719, -4.406719, -4.406719,
                              -4.406719, -4.406719, -4.406719, -4.406719, -4...., len = 13
  File "/home/mpl2/Projects/returnn/NetworkBaseLayer.py", line 148, in set_params_by_dict
    line: (self, p, self_param_shape, v.shape)
    locals:
      self = <local> <<class 'NetworkOutputLayer.SequenceOutputLayer'> class:softmax name:output>
      p = <local> 'b_output', len = 8
      self_param_shape = <local> (80,)
      v = <local> array([-4.406719, -4.406719, -4.406719, -4.406719, -4.406719, -4.406719,
                         -4.406719, -4.406719, -4.406719, -4.406719, -4.406719, -4.406719,
                         -4.406719, -4.406719, -4.406719, -4.406719, -4.406719, -4.406719,
                         -4.406719, -4.406719, -4.406719, -4.406719, -4.406719, -4.406719,
                       ..., len = 82
      v.shape = <local> (82,)
AssertionError: In <<class 'NetworkOutputLayer.SequenceOutputLayer'> class:softmax name:output>, param b_output shape does not match. Expected (80,), got (82,).

KeyboardInterrupt
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