

def __init__(self, name, state_names, hidden_size=100, input_type='sequence', output_type='sequence', inner_init=None, outer_init=None, gate_activation='sigmoid', activation='tanh', steps=None, backward=False, mask=None, additional_input_dims=None):
    super(RecurrentLayer, self).__init__(name)
    self.state_names = state_names
    self.main_state = state_names[0]
    self.hidden_size = hidden_size
    self._gate_activation = gate_activation
    self._activation = activation
    self.gate_activate = build_activation(self._gate_activation)
    self.activate = build_activation(self._activation)
    self._input_type = input_type
    self._output_type = output_type
    self.inner_init = (inner_init if inner_init else OrthogonalInitializer())
    self.outer_init = (outer_init if outer_init else XavierGlorotInitializer())
    self._steps = steps
    self._mask = (mask.tensor if (type(mask) == NeuralVariable) else mask)
    self._go_backwards = backward
    self.additional_input_dims = (additional_input_dims if additional_input_dims else [])
    if (input_type not in INPUT_TYPES):
        raise Exception('Input type of {} is wrong: {}'.format(name, input_type))
    if (inner_init not in OUTPUT_TYPES):
        raise Exception('Output type of {} is wrong: {}'.format(name, output_type))

dataset/ETHPy150Open zomux/deepy/deepy/layers/recurrent.py RecurrentLayer.__init__/VarMisuse@237/358 `output_type`->`inner_init`