TensorFlow tfrecords：toString（）方法改變圖像

Question

的尺寸我建立了一個模型來培訓TensorFlow卷積的自動編碼。我跟着instructions on Reading Data from the TF documentation在我自己的尺寸的圖像讀取233 X 233 X 3.下面是改編自這些說明我convert_to（）函數：

def convert_to(images, name): 
    """Converts a dataset to tfrecords.""" 
    num_examples = images.shape[0] 
    rows = images.shape[1] 
    cols = images.shape[2] 
    depth = images.shape[3] 

    filename = os.path.join(FLAGS.tmp_dir, name + '.tfrecords') 
    print('Writing', filename) 
    writer = tf.python_io.TFRecordWriter(filename) 
    for index in range(num_examples): 
    print(images[index].size) 
    image_raw = images[index].tostring() 
    print(len(image_raw)) 
    example = tf.train.Example(features=tf.train.Features(feature={ 
     'height': _int64_feature(rows), 
     'width': _int64_feature(cols), 
     'depth': _int64_feature(depth), 
     'image_raw': _bytes_feature(image_raw)})) 
    writer.write(example.SerializeToString()) 
    writer.close() 

當我的開始打印圖像的大小for循環中，大小爲162867，但是當我的ToString（）行後打印，尺寸爲1302936.這會導致因爲該模型認爲我的輸入爲8X它應該是什麼樣的道路問題。將示例中的'image_raw'條目更改爲_int64_feature（image_raw）還是更改我將其轉換爲字符串的方式更好？

或者，問題可能是在我的read_and_decode（）函數，例如該字符串未正確解碼或該示例未被解析......？

def read_and_decode(self, filename_queue): 
    reader = tf.TFRecordReader() 

    _, serialized_example = reader.read(filename_queue) 
    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) 
     }) 

    # Convert from a scalar string tensor to a uint8 tensor 
    image = tf.decode_raw(features['image_raw'], tf.uint8) 

    # Reshape into a 233 x 233 x 3 image and apply distortions 
    image = tf.reshape(image, (self.input_rows, self.input_cols, self.num_filters)) 

    image = data_sets.normalize(image) 
    image = data_sets.apply_augmentation(image) 

    return image 

謝謝！

Answer 1

的錯誤似乎是在這裏。

#Convert from a scalar string tensor to a uint8 tensor 
image = tf.decode_raw(features['image_raw'], tf.uint8)#the image looks like the tensor with 1302936 values. 
image.set_shape([self.input_rows*self.input_cols*self.num_filters])#self.input_rows*self.input_cols*self.num_filters equals 162867， right? 

這就是我所有的猜測導致您提供的代碼太少。