Question

我一整天都在这上班，我不认为别人会有所作为！

我有.png个文件，我已经制作了> 400份[ I got to use images with different shapes, but for now I just want to get this starting ]

这里的代码我使用跳转到图像作为具有标签的张量：

import tensorflow as tf
import os
import numpy
batch_Size =20
num_epochs = 100
files = os.listdir("Test_PNG")
files = ["Test_PNG/" + s for s in files]
files = [os.path.abspath(s) for s in files ]


def read_my_png_files( filename_queue):
    reader = tf.WholeFileReader()
    imgName,imgTensor = reader.read(filename_queue)
    img =  tf.image.decode_png(imgTensor,channels=0)
    # Processing should be add
    return img,imgName

def inputPipeline(filenames, batch_Size, num_epochs= None):
    filename_queue  = tf.train.string_input_producer(filenames, num_epochs=num_epochs,shuffle =True)
    img_file, label = read_my_png_files(filename_queue)
    min_after_dequeue = 100
    capacity = min_after_dequeue+3*batch_Size
    img_batch,label_batch = tf.train.shuffle_batch([img_file,label],batch_size=batch_Size,enqueue_many=True,
                                                    allow_smaller_final_batch=True, capacity=capacity,
                                                    min_after_dequeue =min_after_dequeue, shapes=[w,h,d])
    return img_batch,label_batch

images, Labels  = inputPipeline(files,batch_Size,num_epochs)

根据我的理解，我应该将20次图像作为张量及其标签。当我在这里运行代码时，我得到的是：

    ---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-3-08857195e465> in <module>()
     34     return img_batch,label_batch
     35 
---> 36 images, Labels  = inputPipeline(files,batch_Size,num_epochs)

<ipython-input-3-08857195e465> in inputPipeline(filenames, batch_Size, num_epochs)
     31     img_batch,label_batch = tf.train.shuffle_batch([img_file,label],batch_size=batch_Size,enqueue_many=True,
     32                                                     allow_smaller_final_batch=True, capacity=capacity,
---> 33                                                     min_after_dequeue =min_after_dequeue, shapes=[w,h,d])
     34     return img_batch,label_batch
     35 

c:\users\engine\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\training\input.py in shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, num_threads, seed, enqueue_many, shapes, allow_smaller_final_batch, shared_name, name)
   1212       allow_smaller_final_batch=allow_smaller_final_batch,
   1213       shared_name=shared_name,
-> 1214       name=name)
   1215 
   1216 

c:\users\engine\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\training\input.py in _shuffle_batch(tensors, batch_size, capacity, min_after_dequeue, keep_input, num_threads, seed, enqueue_many, shapes, allow_smaller_final_batch, shared_name, name)
    767     queue = data_flow_ops.RandomShuffleQueue(
    768         capacity=capacity, min_after_dequeue=min_after_dequeue, seed=seed,
--> 769         dtypes=types, shapes=shapes, shared_name=shared_name)
    770     _enqueue(queue, tensor_list, num_threads, enqueue_many, keep_input)
    771     full = (math_ops.cast(math_ops.maximum(0, queue.size() - min_after_dequeue),

c:\users\engine\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\ops\data_flow_ops.py in __init__(self, capacity, min_after_dequeue, dtypes, shapes, names, seed, shared_name, name)
    626         shared_name=shared_name, name=name)
    627 
--> 628     super(RandomShuffleQueue, self).__init__(dtypes, shapes, names, queue_ref)
    629 
    630 

c:\users\engine\appdata\local\programs\python\python35\lib\site-packages\tensorflow\python\ops\data_flow_ops.py in __init__(self, dtypes, shapes, names, queue_ref)
    151     if shapes is not None:
    152       if len(shapes) != len(dtypes):
--> 153         raise ValueError("Queue shapes must have the same length as dtypes")
    154       self._shapes = [tensor_shape.TensorShape(s) for s in shapes]
    155     else:

ValueError: Queue shapes must have the same length as dtypes

我声明在tf.train.shuffle_batch函数中使用的形状如下，但我仍然有形状错误！

知道如何解决这个问题？

Answer 1

Your issue was both from

the enqueue_many=True argument,
the shape of the shapes argument where the label dimension was not there.

So I would try with enqueue_many=False and shapes=[[h, w, c], []]).

Indeed, if you look at the shuffle_batch doc:

If enqueue_many is False, tensors is assumed to represent a single example. An input tensor with shape [x, y, z] will be output as a tensor with shape [batch_size, x, y, z].

If enqueue_many is True, tensors is assumed to represent a batch of examples, where the first dimension is indexed by example, and all members of tensors should have the same size in the first dimension. If an input tensor has shape [*, x, y, z], the output will have shape [batch_size, x, y, z].

But in your code, it seems you dequeue only one single file: img_file, label = read_my_png_files(filename_queue) and you pass it straight to the shuffle_batch function: img_batch,label_batch = tf.train.shuffle_batch([img_file,label], ...) so the * dimension is missing and TensorFlow is expecting that the first dimension of [img_file,label] is the number of examples.

Also bear in mind that the enqueue_many and the dequeue_many are independent; i.e.

*: the number of examples you queue into the queue, is independent of
batch_size: The new batch size pulled from the queue.

不能让tf.train.shuffle_batch（）正常工作

1 个答案: