Kann nicht aus Tensorflow tfrecord Datei lesen

Question

Ich bin in der Lage, die tfrecords Datei mit dem folgenden Code zu erstellen.

def _int64_feature(value): 
    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) 
def _bytes_feature(value): 
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) 

def convert_to_tfrecord(images,labels,file_name): 
    # images is a numpy array of shape (num_images,channel,rows,column) 
    # labels is a numpy array of shape (num_images,) 
    num_labels = np.shape(labels) 
    (num_images,depth,rows,cols) = np.shape(images) 
    writer = tf.python_io.TFRecordWriter(file_name) 
    for index in range(num_images): 
     image_raw = images[index] 
     image_raw = image_raw.astype(np.float32) 
     image_raw = image_raw.tostring() 
     example = tf.train.Example(features=tf.train.Features(feature={ 
      'height': _int64_feature(rows), 
      'width': _int64_feature(cols), 
      'depth': _int64_feature(depth), 
      'label': _int64_feature(int(labels[index])), 
      'image_raw': _bytes_feature(image_raw)})) 

     writer.write(example.SerializeToString()) 
    writer.close() 

Aber, während Daten aus der tfrecord Datei zu lesen, indem Sie die unten Funktion

def read_and_decode(filename_queue): 
    reader = tf.TFRecordReader() 
    _, serialized_example = reader.read(filename_queue) 
    img_features = tf.parse_single_example(
     serialized_example, 
     features={ 
      'height': tf.FixedLenFeature([], tf.int64), 
      'width': tf.FixedLenFeature([], tf.int64), 
      'depth': tf.FixedLenFeature([], tf.int64), 
      'image_raw': tf.FixedLenFeature([], tf.string), 
      'label': tf.FixedLenFeature([], tf.int64), 
     }) 

    image = tf.decode_raw(img_features['image_raw'], tf.float32) 
    label = tf.cast(img_features['label'], tf.int32) 
    height = tf.cast(img_features['height'], tf.int32) 
    width = tf.cast(img_features['width'], tf.int32) 
    depth = tf.cast(img_features['depth'], tf.int32) 
    image_shape = tf.stack([depth,height, width]) 
    image = tf.reshape(image, image_shape) 
    return image,label 

def inputs(batch_size, num_epochs): 
    filename = ['set1.tfrecords'] 
    # dir_path is a global variable 
    file_path = dir_path + 'set1.tfrecords' 
    filename_queue = tf.train.string_input_producer([file_path], num_epochs=1) 
    image,label = read_and_decode(filename_queue) 
    images, sparse_labels = tf.train.shuffle_batch(
     [image, label], batch_size=batch_size, num_threads=2, 
     capacity=1000 + 3 * batch_size, min_after_dequeue=1000) 
    return images, sparse_labels 

Ich erhalte die folgenden Fehler ständig

images, labels = tf.train.shuffle_batch([image, label], batch_size=10, capacity=30, num_threads=1, min_after_dequeue=10) 

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py", line 1225, in shuffle_batch 
name=name) 

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py", line 781, in _shuffle_batch 
dtypes=types, shapes=shapes, shared_name=shared_name) 

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/data_flow_ops.py", line 641, in __init__ 
shapes = _as_shape_list(shapes, dtypes) 

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/data_flow_ops.py", line 77, in _as_shape_list 
raise ValueError("All shapes must be fully defined: %s" % shapes) 

ValueError: All shapes must be fully defined: [TensorShape([Dimension(None)]), TensorShape([])] 

Was ist der Grund für die obigen Fehler und wie kann man das überwinden? Ich bin in der Lage, die Datei tfrecords zu lesen, indem ich über die Datei mit tf.python_io.tf_record_iterator(path=filename) iteriere.

Answer 1

Der Fehler tritt auf, weil tf.train.shuffle_batch die Form Ihrer Tensoren kennen muss, um sie stapelweise verarbeiten zu können (Objekte in einem Stapel müssen die gleiche Form haben). Im Prinzip können Rohdaten jedoch unterschiedliche Größen haben, so dass tf.decode_raw keine Form für Ihren Tensor definiert.

In den Kommentaren, erwähnen Sie, dass alle Ihre Bilder Form haben (192,81,2), so müssen Sie nur diese Form im Bild Tensor setzen, bevor aus read_and_decode Rückkehr:

def read_and_decode(filename_queue): 
    # rest of your code here 
    image_shape = [height, width, depth] 
    image = tf.reshape(image, image_shape) 
    image.set_shape(image_shape) #<<<<<<<<<<<<<<< 
    return image,label