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.