给RNN cell加dropout

class SwitchableDropoutWrapper(DropoutWrapper):
    def __init__(self, cell, is_train, input_keep_prob=1.0, output_keep_prob=1.0,
             seed=None):
        super(SwitchableDropoutWrapper, self).__init__(cell, input_keep_prob=input_keep_prob, output_keep_prob=output_keep_prob,
                                                       seed=seed)
        self.is_train = is_train

    def __call__(self, inputs, state, scope="dropout_rnn"):
        outputs_do, new_state_do = super(SwitchableDropoutWrapper, self).__call__(inputs, state, scope=scope)
        tf.get_variable_scope().reuse_variables()
        outputs, new_state = self._cell(inputs, state, scope)
        outputs = tf.cond(self.is_train, lambda: outputs_do, lambda: outputs)
        if isinstance(state, tuple):
            new_state = state.__class__(*[tf.cond(self.is_train, lambda: new_state_do_i, lambda: new_state_i)
                                          for new_state_do_i, new_state_i in zip(new_state_do, new_state)])
        else:
            new_state = tf.cond(self.is_train, lambda: new_state_do, lambda: new_state)
        return

注意​​tf.get_variable_scope().reuse_variables()​​​这句可能会引起​​Variable XXX does not exist, or was not created with tf.get_variable()​​的错误，按需调整