类 的 TensorFlow 估计器数量不变

Question

我尝试对 MNIST 数据集使用 tensorflow 估计器。出于某种原因，它一直说我的 n_classes 设置为 1，即使它是 10！

import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)


feature_columns = [tf.feature_column.numeric_column("x", shape=[784])]

# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
                                        hidden_units=[500, 500, 500],
                                        n_classes=10,
                                        model_dir="/tmp/MT")
for i in range(100000):
    xdata, ydata = mnist.train.next_batch(500)
    train_input_fn = tf.estimator.inputs.numpy_input_fn(
        x={"x":xdata},
        y=ydata,
        num_epochs=None,
        shuffle=True)
    classifier.train(input_fn=train_input_fn, steps=2000)

# Define the test inputs
test_input_fn = tf.estimator.inputs.numpy_input_fn(
    x= {"x":mnist.test.images},
    y= mnist.test.labels,
    num_epochs=1,
    shuffle=False)

# Evaluate accuracy.
accuracy_score = classifier.evaluate(input_fn=test_input_fn)["accuracy"]

print("\nTest Accuracy: {0:f}\n".format(accuracy_score))

错误：

ValueError: Mismatched label shape. Classifier configured with n_classes=1.  Received 10. Suggested Fix: check your n_classes argument to the estimator and/or the shape of your label.

Process finished with exit code 1

Answer 1

这是个好问题。 tf.estimator.DNNClassifier is using tf.losses.sparse_softmax_cross_entropy loss, in other words it expects ordinal encoding, instead of one-hot (can't find it in the doc, only the source code):

labels must be a dense Tensor with shape matching logits, namely [D0, D1, ... DN, 1]. If label_vocabulary given, labels must be a string Tensor with values from the vocabulary. If label_vocabulary is not given, labels must be an integer Tensor with values specifying the class index.

您应该使用 one_hot=False 读取数据并将标签转换为 int32 以使其工作：

y=ydata.astype(np.int32)