问题
I am new to Neural Networks and went through the MNIST example for beginners.
I am currently trying to use this example on another dataset from Kaggle that does not have test labels.
If I run the model on the test data set without corresponding labels and therefore unable to compute the accuracy like in the MNIST example, I would like to be able to see the predictions. Is it possible to access observations and their predicted labels somehow and print them out nicely?
回答1:
I think you just need to evaluate your output-tensor as stated in the tutorial:
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
To get the output of a tensor see the docs:
After the graph has been launched in a session, the value of the Tensor can be computed by passing it to Session.run(). t.eval() is a shortcut for calling tf.get_default_session().run(t).
If you want to get predictions rather than accuracy, you need to evaluate your ouput tensor y in the same way:
print(sess.run(y, feed_dict={x: mnist.test.images}))
回答2:
prediction=tf.argmax(y,1)
print prediction.eval(feed_dict={x: mnist.test.images}).
For more details, check out https://github.com/tensorflow/tensorflow/issues/97
来源：https://stackoverflow.com/questions/34060332/how-to-get-predicted-class-labels-in-tensorflows-mnist-example