如何使用tf返回预测和标签。估计器(使用预测或评估方法)?

训练结束后 '/my/directory' 你有一堆检查点文件。

您需要再次设置输入管道,手动加载其中一个文件,然后开始在存储预测和标签的批次中循环:

# Rebuild the input pipeline
input_fn = create_eval_input_fn(path=eval_files)
features, labels = input_fn()

# Rebuild the model
predictions = model_fn(features, labels, tf.estimator.ModeKeys.EVAL).predictions

# Manually load the latest checkpoint
saver = tf.train.Saver()
with tf.Session() as sess:
    ckpt = tf.train.get_checkpoint_state('/my/directory')
    saver.restore(sess, ckpt.model_checkpoint_path)

    # Loop through the batches and store predictions and labels
    prediction_values = []
    label_values = []
    while True:
        try:
            preds, lbls = sess.run([predictions, labels])
            prediction_values += preds
            label_values += lbls
        except tf.errors.OutOfRangeError:
            break
    # store prediction_values and label_values somewhere

更新: 更改为直接使用 model_fn 您已经拥有的功能。