我尝试对 MNIST 数据集使用张量流估计器。由于某种原因它一直说我的n_classes即使它是 10，也被设置为 1！
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))
Error:
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
