我在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)]]
这是相关的代码
# 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})
`
我还不能尝试很多,因为我只是在评估我的第一个模型,并且这个错误(表示期望为1并得到1)并不是很有帮助。
错误消息不是很好,但是查看代码可能会解释正在发生的事情。
出现问题是因为train_labels
(大概是)一维向量。维数从0开始编号,因此向量仅具有第0维数,但是您调用的tf.argmax(train_labels, 1)
将第一个维数作为argmax的尝试却不存在。
实际上,根本不需要使用标签的argmax。相反,您可以简单地编写:
correct_prediction = tf.equal(tf.argmax(train_prediction, 1), train_labels)
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我来说两句