TensorFlow 睬坑以及某些特定用法

1. 异步读取图片的时候报错：

想在tf.train.slice_input_producer加入num_epoch参数，控制epoch 例如测试这种只读入一个epoch场景，再配合 

tf.train.batch(***， allow_smaller_final_batch=True) 进行验证、测试集操作，报以下错误：

OutOfRangeError: FIFOQueue '_1758_batch_41/fifo_queue' is closed and has insufficient elements (requested 4, current size 0)
	 [[Node: batch_41 = QueueDequeueUpToV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](batch_41/fifo_queue, batch_41/n)]]

Caused by op 'batch_41', defined at:
  File "C:\ProgramData\Anaconda3\lib\runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  *
  。。。。。。。。。。。。
  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1470, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

OutOfRangeError (see above for traceback): FIFOQueue '_1758_batch_41/fifo_queue' is closed and has insufficient elements (requested 4, current size 0)
	 [[Node: batch_41 = QueueDequeueUpToV2[component_types=[DT_FLOAT, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](batch_41/fifo_queue, batch_41/n)]]

 

image_list1 = glob.glob(image_dir1)
image_list2 = glob.glob(image_dir2+'*.jpg')
image_list = image_list2 + image_list1
print(len(image_list))
image_tensor_list = tf.convert_to_tensor(image_list)
l = [np.random.randint(0,10) for _ in range(len(image_list))]
label_tensor_list = tf.convert_to_tensor(l)

input_queue = tf.train.slice_input_producer([image_tensor_list, label_tensor_list],             
                              shuffle=False,num_epochs=3)
image_reader = tf.read_file(input_queue[0])
image = tf.image.decode_jpeg(image_reader,channels=3)
print(image.shape)
image = tf.image.resize_images(image, size=(100,100))

a,b = tf.train.batch([image, input_queue[1]],batch_size=4,enqueue_many=False, 
               allow_smaller_final_batch=True)

logits = tf.reduce_mean(tf.multiply(a, tf.constant(3, dtype=tf.float32)), axis=[1,2,3])
loss = tf.subtract(logits, tf.squeeze(tf.cast(b, dtype=tf.float32),))
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    for i in range(56):
        k = sess.run([loss])
        print (k,i)
    coord.request_stop()
    coord.join(threads)

在sess内部加入 sess.run(tf.local_variables_initializer())可解决，原因：

tf.global_variables_initializer()添加节点用于初始化所有的变量(GraphKeys.VARIABLES)

tf.local_variables_initializer()返回一个初始化所有局部变量的操作（Op）。初始化局部变量（GraphKeys.LOCAL_VARIABLE）。GraphKeys.LOCAL_VARIABLE中的变量指的是被添加入图中，但是未被储存的变量。

上面的num_epochs=3 可能属于后者，在run的的时候需要这个变量但是未配置，（猜测，可能不正确）

 

2.enqueue

queue = tf.FIFOQueue(...)
enqueue_op = queue.enqueue(example)
qr = tf.train.QueueRunner(queue, [enqueue_op] * num_theadings)

1.使用tf.train.add_queue_runner(qr) 

tf.train.add_queue_runner(qr) 

input = q.dequeue(...)
train(input).....

sess: 
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)#！！！！
#由于有可能 add_queue_runner(qr)添加了多个入队qr，所以threadＳ！可能多个进程（每个进程可能多个入队） 


coord.request_stop()
coord.join(threads)
....其他都一样

2.直接用qr创建进程

#tf.train.add_queue_runner(qr) 不再需要这一步！

input = q.dequeue(...)
train(input).....

sess:
coord = tf.train.Coordinator()
thread = qr.create_threads(sess, coord=coord, start=True) #！！！！相当于入队操作的单一进程

coord.request_stop()
coord.join(threads)



在使用tf.train.start_queue_runners(sess=sess, coord=coord)的时候，前面肯定有
tf.train.add_queue_runner(qr) 相当于：

ops.GraphKeys.QUEUE_RUNNERS中的所有qr入队进程
for qr in ops.get_collection(collection):
    threads.extend(qr.create_threads(sess, coord=coord, daemon=daemon,
                                     start=start))
return threads

2. Bilinear upsample in tensorflow 

实际上keras中upsample模块的内部实现就是tf.image.resize

所以可以

upsampled = tf.image.resize_images(input_tensor, [new_height, new_width])