Question

我试图评估我的模型时遇到以下错误。

tensorflow.python.framework.errors.InvalidArgumentError: Minimum tensor rank: 1 but got: 1 [[Node: ArgMax_1 = ArgMax[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_Placeholder_1_0, ArgMax_1/dimension/_40)]]

以下是相关代码

        # Predictions for the current training minibatch.
        train_prediction = tf.nn.softmax(logits)
        correct_prediction = tf.equal(tf.argmax(train_prediction, 1), tf.argmax(train_labels, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        sess.run(tf.initialize_all_variables())
        for i in range(1000000):
            start_time = time()

            images, labels = get_batch(fifo_queue, FLAGS.batch_size)

            feed_dict = {
                train_images: images,
                train_labels: labels
            }

            _, loss_value, learn_rate, predictions = sess.run(
                [train_step, cross_entropy, learning_rate, train_prediction],
                feed_dict=feed_dict)

            duration = time() - start_time
              if i % 1 == 0:
                # Print status to stdout.
                 print('Step %d: loss = %.3f (%.3f sec)' % (i, loss_value, duration))

                 train_accuracy = accuracy.eval(feed_dict={
                     train_images: images, train_labels: labels, keep_prob: 1.0})
                 print("step %d, training accuracy %g"%(i, train_accuracy))
                 train_step.run(feed_dict={train_images: images[0], train_labels: labels[1], keep_prob: 0.5})

`

我还没有尝试过，因为我只是得到了我的第一个模型评估，这个错误（表示期待1并得到1）并没有太大的帮助。

Answer 1

错误信息不是很好，但查看the code可能会解释发生了什么。

问题出现是因为train_labels（推测）是一维向量。维度从0开始编号，因此向量只有第0维度，但是tf.argmax(train_labels, 1)的调用会尝试在第1维度中获取argmax，而不存在。

事实上，根本不需要使用标签的argmax。相反，你可以简单地写：

correct_prediction = tf.equal(tf.argmax(train_prediction, 1), train_labels)

Tensorflow错误：最小张量等级：1但得到：1

1 个答案: