我将相同的feed_dict提供给两个不同的操作,但第二个操作崩溃并出现错误:“你必须为占位符张量'input_tensor / TrainImage'提供一个值,其中dtype为float和shape [?,60,30,1]”
optimizer = tf.train.AdamOptimizer(learning_rate, 0.9).minimize(loss = loss,global_step=globalstep )
merged = tf.summary.merge_all()
accuary_sum = tf.summary.scalar('Accuracy', accuracy)
accuary_sum_training = tf.summary.scalar('AccuaryTraining',accuracy)
...(打开sess等)
feed=self.feed_dict(True,self.step)
acc,_ = sess.run([accuracy,optimizer],feed_dict=feed ) %works fine
try:
summary,sumacc= sess.run([merged,accuary_sum_training],feed)%doesnt work
错误提出:
tf_session.TF_Run(session, options,
feed_dict, fetch_list, target_list,
status, run_metadata)
答案 0 :(得分:0)
您正在运行不同的操作。假设第一个张量(准确度,优化器)不依赖于傻瓜,而后来的操作则是(合并,accuracy_sum_training)。