如何在 mnist tensorflow python 中获得预测的 class 标签？

Question

after training mnist tensor flow model and below I test model. I don't know how we extract the predict labels results list. It just gives me accuracy and loss value. Anyone can help me to solve this question?

 # Evaluate the model and print results
  eval_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
      x={"x": eval_data},
      y=eval_labels,
      num_epochs=1,
      shuffle=False)
  eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)

Answer 1

In MNIST dataset the labels are encoded in numbers like above:

0 = T-shirt/top
1 = Trouser
2 = Pullover
3 = Dress
4 = Coat
5 = Sandal
6 = Shirt
7 = Sneaker
8 = Bag
9 = Ankle boot

So when you call model.predict(test_image) this will return an array with length equal 10, where each index represent a confidence of the model that the image corresponds to each of the different articles of clothing, exactly following the order of the numbers above inside the array order, so if the array has the highest number in index 3 it means that the model predicted that this outfit is a Dress, to get the highest index in the array you can call np.argmax(model.predict(test_image)[0])

To facilitate the mapping of the index to a real value, that is, if the predicted clothing is a dress or boot, you can store the clothing names in an array such as:

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

And in the end you can call: class_names[np.argmax(model.predict(test_image)[0])]