Question

我是Tensorflow的新手，使用official tutorial tf.estimator.DNNClassifier和自定义估计器来构建简单的NN来解决分类问题。

培训期间：

dnn_model = tf.estimator.DNNClassifier(hidden_units=[10,10,10],
                                       feature_columns = my_features_column,
                                       n_classes=5,
                                       optimizer = tf.train.AdamOptimizer()
                                      )

dnn_model.train(input_fn=train_input_func)

它将在特定时间报告损失，如下所示：

INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmphwkvj5le/model.ckpt-150
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 150 into /tmp/tmphwkvj5le/model.ckpt.
INFO:tensorflow:loss = 133.04277, step = 150
INFO:tensorflow:global_step/sec: 115.114
INFO:tensorflow:loss = 128.15938, step = 250 (0.872 sec)
INFO:tensorflow:global_step/sec: 134.317
INFO:tensorflow:loss = 123.093094, step = 350 (0.743 sec)
INFO:tensorflow:global_step/sec: 133.573
INFO:tensorflow:loss = 117.80729, step = 450 (0.748 sec)
INFO:tensorflow:global_step/sec: 135.081
INFO:tensorflow:loss = 114.07168, step = 550 (0.741 sec)
INFO:tensorflow:Saving checkpoints for 650 into /tmp/tmphwkvj5le/model.ckpt.
INFO:tensorflow:Loss for final step: 118.19583.

我想每批或每个时期打印分类准确度，就像keras中的日志信息一样：

Epoch 1/20
5000/5000 [==============================] - 1s 157us/step - loss: 1.4885 - acc: 0.3276 - val_loss: 1.4397 - val_acc: 0.3620
Epoch 2/20
5000/5000 [==============================] - 0s 66us/step - loss: 1.3792 - acc: 0.3922 - val_loss: 1.4001 - val_acc: 0.3768
.
.

如何找到有关此问题的教程？我发现所有讨论的都是更低级的API（张量，会话等）。

Answer 1

您要在估算器列中使用“挂钩”选项。

您要使用的特定钩子是这个：https://www.tensorflow.org/api_docs/python/tf/train/LoggingTensorHook

这里解决了类似的问题： https://stackoverflow.com/a/45716062/10498246 他们在不同的训练功能上使用了测井张量钩。

训练tf.estimator.DNNClassifier时的打印精度

1 个答案: