如何使用 estimator API 在 tensorboard 上添加更多细节 [英] How to add more details on tensorboard using estimator API

问题描述

我按照 https://www.tensorflow.org/tutorials/estimators 创建了我的模型/cnn.

我在我的模型中添加了 SummarySaverHook

I added SummarySaverHook to my model

    summary_hook = tf.train.SummarySaverHook(
    100,
    output_dir='C:/Users/dir',
    summary_op=tf.summary.merge_all())

# Configure the Training Op (for TRAIN mode)
if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
    train_op = optimizer.minimize(
        loss=loss,
        global_step=tf.train.get_global_step())
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op, training_hooks=[summary_hook])

但是当我运行一个 get only enqueue_input 图(我不知道它是什么)和模型图时.我想要获得准确性和损失图表.

But when i run a get only enqueue_input chart(I don't known what is it) and model graph. I want get accuracy and loss charts.

所以我想在我的张量板中添加一些细节.

So i want a couple of details in my tensorboard.

1. 损失和准确度字符
2. 可以及时获得准确度图表，因为在估算器中，我只在最后一步之后才能获得准确度.
3. 我能否在 tensorboard 中获得更多细节，比如错误的预测图像?但是没有 Session 和 Graph 创建，只能通过 estimator api?

推荐答案

首先，你不需要使用 summary_hook.您只需要在指定 logits 后立即使用 tf.metrics 指定所需的指标.

First of all, you don't need to use summary_hook. You just need to specify desired metrics with tf.metrics right after you specify logits.

 logits = tf.layers.dense(inputs=dropout, units=10)

 predictions = {
          "classes": tf.argmax(input=logits, axis=1),
          "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
 }

 accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions['classes']
 tf.summary.scalar('acc', accuracy[1])

然后把这个tf.logging.set_verbosity(tf.logging.INFO)在您输入之后，如果您还没有这样做的话.

And put this tf.logging.set_verbosity(tf.logging.INFO) right after your inputs, if you haven't done so.

您可以通过将 eval_metric_ops = {'accuracy':accuracy} dict 插入到 tf.estimator.EstimatorSpec

You can plot evaluation metrics by inserting eval_metric_ops = {'accuracy': accuracy} dict to tf.estimator.EstimatorSpec

您可以使用 tf.summary 来可视化图像、权重和偏差等.

You can use tf.summary for visualizing images, weights and biases, etc.

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