簡単にtenssorflowの中でDatasetピクチャーの大量がおよび次元の操作を読み取って詳しく説明します。

import tensorflow as tf
 
import glob
import os
 
 
def _parse_function(filename):
  # print(filename)
  image_string = tf.read_file(filename)
  image_decoded = tf.image.decode_image(image_string) # (375, 500, 3)
 
  image_resized = tf.image.resize_image_with_crop_or_pad(image_decoded, 200, 200)
  return image_resized
 
 
 
 
with tf.Session() as sess:
 
  print( sess.run( img ).shape  )

import tensorflow as tf
 
import glob
import os
 
'''
  Dataset       
'''
 
def _parse_function(filename):
  # print(filename)
  image_string = tf.read_file(filename)
  image_decoded = tf.image.decode_image(image_string) # (375, 500, 3)
 
  image_decoded = tf.expand_dims(image_decoded, axis=0)
 
  image_resized = tf.image.resize_image_with_crop_or_pad(image_decoded, 200, 200)
  return image_resized
 
 
 
img = _parse_function('../pascal/VOCdevkit/VOC2012/JPEGImages/2007_000068.jpg')
 
# image_resized = tf.image.resize_image_with_crop_or_pad( tf.truncated_normal((1,220,300,3))*10, 200, 200)            
 
with tf.Session() as sess:
 
  print( sess.run( img ).shape  ) #         ，        ，      ，     ，       
  #InvalidArgumentError (see above for traceback): Input shape axis 0 must equal 4, got shape [5]

import tensorflow as tf
 
import glob
import os
 
'''
  Dataset       ：
  
      ：
      1.        list,   Dataset  from_tensor_slices（）
      2.     ，     ， list        ， resize,  
        tf.read_file(filename)        ，            ，      ，        
               resize,     batch         ，  get_next()      [batch, w, h, c ]
      3.   shuffle , batch repeat   
      
      4. iterator = dataset.make_one_shot_iterator()      
      
      5. iterator.get_next()     batch   
'''
 
def _parse_function(filename):
  # print(filename)
  image_string = tf.read_file(filename)
  image_decoded = tf.image.decode_image(image_string) #(375, 500, 3)
  '''
    Tensor` with type `uint8` with shape `[height, width, num_channels]` for
     BMP, JPEG, and PNG images and shape `[num_frames, height, width, 3]` for
     GIF images.
  '''
 
  # image_resized = tf.image.resize_images(label, [200, 200])
  ''' images   ，      
     images: 4-D Tensor of shape `[batch, height, width, channels]` or
      3-D Tensor of shape `[height, width, channels]`.
    size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The
       new size for the images.
  
  '''
  image_resized = tf.image.resize_image_with_crop_or_pad(image_decoded, 200, 200)
 
  # return tf.squeeze(mage_resized,axis=0)
  return image_resized
 
filenames = glob.glob( os.path.join('../pascal/VOCdevkit/VOC2012/JPEGImages', "*." + 'jpg') )
 
 
dataset = tf.data.Dataset.from_tensor_slices((filenames))
 
dataset = dataset.map(_parse_function)
 
dataset = dataset.shuffle(10).batch(2).repeat(10)
iterator = dataset.make_one_shot_iterator()
 
img = iterator.get_next()
 
with tf.Session() as sess:
  # print( sess.run(img).shape ) #(4, 200, 200, 3)
  for _ in range (10):
    print( sess.run(img).shape )