Question

我试图实施CNN来玩游戏。我和theano / lasagne一起使用python。我已经建立了网络，现在正在研究如何训练它。

所以现在我有一批32个州，对于该批次中的每个州，该行动的行动和预期奖励。

现在我如何训练网络，以便了解这些状态下的这些行为会带来这些奖励？

编辑：澄清我的问题。

以下是我的完整代码：http://pastebin.com/zY8w98Ng 蛇导入：http://pastebin.com/fgGCabzR

我遇到这个问题：

def _train(self):
    # Prepare Theano variables for inputs and targets
    input_var = T.tensor4('inputs')
    target_var = T.ivector('targets')
    states = T.tensor4('states')
    print "sampling mini batch..."
    # sample a mini_batch to train on
    mini_batch = random.sample(self._observations, self.MINI_BATCH_SIZE)
    # get the batch variables
    previous_states = [d[self.OBS_LAST_STATE_INDEX] for d in mini_batch]
    actions = [d[self.OBS_ACTION_INDEX] for d in mini_batch]
    rewards = [d[self.OBS_REWARD_INDEX] for d in mini_batch]
    current_states = np.array([d[self.OBS_CURRENT_STATE_INDEX] for d in mini_batch])
    agents_expected_reward = []
    # print np.rollaxis(current_states, 3, 1).shape
    print "compiling current states..."
    current_states = np.rollaxis(current_states, 3, 1)
    current_states = theano.compile.sharedvalue.shared(current_states)

    print "getting network output from current states..."
    agents_reward_per_action = lasagne.layers.get_output(self._output_layer, current_states)


    print "rewards adding..."
    for i in range(len(mini_batch)):
        if mini_batch[i][self.OBS_TERMINAL_INDEX]:
            # this was a terminal frame so need so scale future reward...
            agents_expected_reward.append(rewards[i])
        else:
            agents_expected_reward.append(
                rewards[i] + self.FUTURE_REWARD_DISCOUNT * np.max(agents_reward_per_action[i].eval()))

    # figure out how to train the model (self._output_layer) with previous_states,
    # actions and agent_expected_rewards

我想使用previous_states，actions和agent_expected_rewards更新模型，以便它了解这些操作会带来这些奖励。

我希望它看起来像这样：

train_model = theano.function(inputs=[input_var],
    outputs=self._output_layer,
    givens={
        states: previous_states,
        rewards: agents_expected_reward
        expected_rewards: agents_expected_reward)

我只是不知道这些赠品会如何影响模型，因为在构建网络时我没有指定它们。我也无法在theano和lasagne文档中找到它。

那么我该如何更新模型/网络以便它能够学习＆＃39;。

如果仍不清楚，请评论仍需要哪些信息。我现在已经试图解决这个问题几天了。

Answer 1

完成文档后，我终于找到了答案。我以前在错误的地方寻找过。

    network = self._output_layer
    prediction = lasagne.layers.get_output(network)
    loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
    loss = loss.mean()

    params = lasagne.layers.get_all_params(network, trainable=True)
    updates = lasagne.updates.sgd(loss, params, self.LEARN_RATE)
    givens = {
        states: current_states,
        expected: agents_expected_reward,
        real_rewards: rewards
    }
    train_fn = theano.function([input_var, target_var], loss,
                                    updates=updates, on_unused_input='warn',
                                    givens=givens,
                                    allow_input_downcast='True')
    train_fn(current_states, agents_expected_reward)

卷积神经网络：如何训练它？（无监督）

1 个答案:

卷积神经网络：如何训练它？ （无监督）

1 个答案:

卷积神经网络：如何训练它？（无监督）