Add a useful `__repr__` to base net class. about skorch HOT 3 CLOSED

    def __repr__(self):
        params = self.get_params(deep=False)

        if self.initialized_:
            parts = ['initialized ' + str(self.__class__) + ' (']
        else:
            parts = ['uninitialized ' + str(self.__class__) + ' (']

        if params['history']:
            history = params.pop('history')
            params['history'] = 'history=[...],  # {} epochs'.format(len(history))

        for key, val in sorted(params.items()):
            val = str(val)
            if '\n' in val:
                val = '\n  '.join(val.split('\n'))
            parts.append('  {}={},'.format(key, val))

        parts.append(')')
        return '\n'.join(parts)

net = net = MyNet(
    MyModule,
    max_epochs=20,
    lr=0.1,
    module__num_units=123,
    module__dropout=0.33,
    optim__lr=0.22,
    iterator_train__shuffle=True,
    callbacks=[callbacks.EpochTimer, ('epoch2', callbacks.EpochTimer())],
    callbacks__epoch2__foo='bar',
)

# ==>

uninitialized <class '__main__.MyNet'> (
  batch_size=128,
  callbacks=[<class 'inferno.callbacks.EpochTimer'>, ('epoch2', <inferno.callbacks.EpochTimer object at 0x7fc530540160>)],
  callbacks__epoch2__foo=bar,
  cold_start=True,
  criterion=<class 'torch.nn.modules.loss.NLLLoss'>,
  dataset=<class 'inferno.dataset.Dataset'>,
  gradient_clip_norm_type=2,
  gradient_clip_value=None,
  history=None,
  initialized_=False,
  iterator_test=<class 'torch.utils.data.dataloader.DataLoader'>,
  iterator_train=<class 'torch.utils.data.dataloader.DataLoader'>,
  iterator_train__shuffle=True,
  lr=0.1,
  max_epochs=20,
  module=<class '__main__.MyModule'>,
  module__dropout=0.33,
  module__num_units=123,
  optim=<class 'torch.optim.sgd.SGD'>,
  optim__lr=0.22,
  train_split=<inferno.dataset.CVSplit object at 0x7fc535cacc50>,
  use_cuda=False,
  verbose=1,
)

initialized <class '__main__.MyNet'> (
  batch_size=128,
  callbacks=[<class 'inferno.callbacks.EpochTimer'>, ('epoch2', <inferno.callbacks.EpochTimer object at 0x7fc530540160>)],
  callbacks_=[('epoch_timer', <inferno.callbacks.EpochTimer object at 0x7fc535c98fd0>), ('train_loss', <inferno.callbacks.Scoring object at 0x7fc535d14828>), ('valid_loss', <inferno.callbacks.Scoring object at 0x7fc535d14f28>), ('valid_acc', <inferno.callbacks.Scoring object at 0x7fc59ad08e10>), ('EpochTimer', <inferno.callbacks.EpochTimer object at 0x7fc530534a90>), ('epoch2', <inferno.callbacks.EpochTimer object at 0x7fc530540160>), ('print_log', <inferno.callbacks.PrintLog object at 0x7fc59acf6f98>)],
  callbacks__epoch2__foo=bar,
  cold_start=True,
  criterion=<class 'torch.nn.modules.loss.NLLLoss'>,
  criterion_=NLLLoss (
  ),
  dataset=<class 'inferno.dataset.Dataset'>,
  gradient_clip_norm_type=2,
  gradient_clip_value=None,
  history=history=[...],  # 20 epochs,
  initialized_=True,
  iterator_test=<class 'torch.utils.data.dataloader.DataLoader'>,
  iterator_train=<class 'torch.utils.data.dataloader.DataLoader'>,
  iterator_train__shuffle=True,
  lr=0.1,
  max_epochs=20,
  module=<class '__main__.MyModule'>,
  module_=MyModule (
    (dense0): Linear (20 -> 123)
    (dropout): Dropout (p = 0.33)
    (dense1): Linear (123 -> 10)
    (output): Linear (10 -> 2)
  ),
  module__dropout=0.33,
  module__num_units=123,
  optim=<class 'torch.optim.sgd.SGD'>,
  optim_=<torch.optim.sgd.SGD object at 0x7fc530540198>,
  optim__lr=0.22,
  train_split=<inferno.dataset.CVSplit object at 0x7fc535cacc50>,
  use_cuda=False,
  verbose=1,
)