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对于强化学习，我读到张量板并不理想，因为它提供了每集和/或步骤的输入。由于在强化学习中有数千个步骤，它并没有给我们提供内容的概述。我在这里看到了这个修改过的张量板类： https ://pythonprogramming.net/deep-q-learning-dqn-reinforcement-learning-python-tutorial/

班上：

class ModifiedTensorBoard(TensorBoard):
    # Overriding init to set initial step and writer (we want one log file for all .fit() calls)
    def __init__(self, name, **kwargs):
        super().__init__(**kwargs)
        self.step = 1
        self.writer = tf.summary.create_file_writer(self.log_dir)
        self._log_write_dir = os.path.join(self.log_dir, name)

    # Overriding this method to stop creating default log writer
    def set_model(self, model):
        pass

    # Overrided, saves logs with our step number
    # (otherwise every .fit() will start writing from 0th step)
    def on_epoch_end(self, epoch, logs=None):
        self.update_stats(**logs)

    # Overrided
    # We train for one batch only, no need to save anything at epoch end
    def on_batch_end(self, batch, logs=None):
        pass

    # Overrided, so won't close writer
    def on_train_end(self, _):
        pass

    def on_train_batch_end(self, batch, logs=None):
        pass

    # Custom method for saving own metrics
    # Creates writer, writes custom metrics and closes writer
    def update_stats(self, **stats):
        self._write_logs(stats, self.step)

    def _write_logs(self, logs, index):
        with self.writer.as_default():
            for name, value in logs.items():
                tf.summary.scalar(name, value, step=index)
                self.step += 1
                self.writer.flush()

我想让它与这一层一起工作：

n_actions = env.action_space.n
input_dim = env.observation_space.n
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(20, input_dim = input_dim , activation = 'relu'))#32
model.add(tf.keras.layers.Dense(10, activation = 'relu'))#10
model.add(tf.keras.layers.Dense(n_actions, activation = 'linear'))
model.compile(optimizer=tf.keras.optimizers.Adam(), loss = 'mse')

但我还没有让它工作。任何曾经使用过 tensorboard 的人，你知道如何设置它吗？非常感谢任何见解。

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1 回答 1

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我总是在训练 RL 算法期间使用 tensorboard，而无需像上面那样修改任何代码。只需启动您的作家：

writer = tf.summary.create_file_writer(logdir=log_folder)

开始你的代码：

with writer.as_default():
    ... do everythng indented inside here

例如，如果您想将奖励或第一层的权重保存到 tensorboard 每 100 步，只需执行以下操作：

if step % 100 = 0:
    tf.summary.scalar(name="reward", data=reward, step=step)
    dqn_variable = model.trainable_variables
    tf.summary.histogram(name="dqn_variables", data=tf.convert_to_tensor(dqn_variable[0]), step=step)
    writer.flush()

这应该够了吧 ：）

于 2021-01-15T20:05:23.470 回答