You must feed a value for placeholder tensor 'Placeholder' with dtype float

I’m a newer to tensorflow, I really don’t know how to solve the problem.

The code is like:

1. Feed the train with values:

   sess.run(train_op, feed_dict={images: e, labels: l, keep_prob_fc2: 0.5})
   

2. Use the value in CNN:

   x = tf.placeholder(tf.float32, [None, 10 * 1024])
   

Then have the error

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float
     [[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/gpu:0"]()]]

I print the input valuetypes using print(e.dtype) and the result is float32 and e.shape:(10, 32, 32, 1).

I really don’t know why this error is happening.

---

The code format

First:

 define the CNN model 
       "image = tf.placeholder(tf.float32, [FLAGS.batch_size, 32,32,1])" is here

Second:

 loss funtion and train_op is here
       "label = tf.placeholder(tf.float32, [None, FLAGS.batch_size])" is here

Third is the session:

images, labels = getShuffleimage()#here will get shuffle data
num_examples = 0
init = tf.initialize_local_variables()

with tf.Session() as sess:
    # Start populating the filename queue.
    sess.run(init)
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord, sess=sess)

    try:
        step = 0
        while not coord.should_stop():
            start_time = time.time()
            image, label = sess.run([images, labels])#get shuffle images
            print(image.shape)
            print(image.dtype)
            sess.run(train_op, feed_dict={image: image, label: label , keep_prob_fc2: 0.5})
            duration = time.time() - start_time

    except tf.errors.OutOfRangeError:
        print('Done training after reading all data')
    finally:
        # When done, ask the threads to stop.
        coord.request_stop()

        # Wait for threads to finish.
        coord.join(threads)
        sess.close()