python – Tensorflow和cifar 10,测试单个图像

概述我试图用tensorflow的cifar-10预测单个图像的类. 我找到了这个代码,但它失败了这个错误： 分配要求两个张量的形状匹配. lhs shape = [18,384] rhs shape = [2304,384] 我理解这是因为批次的大小只有1.(使用expand_dims我创建一个假批次.) 但我不知道如何解决这个问题？ 我到处搜索但没有解决方案.. 提前致谢！ from PIL im 我试图用tensorflow的cifar-10预测单个图像的类.

我找到了这个代码,但它失败了这个错误：

分配要求两个张量的形状匹配. lhs shape = [18,384] rhs shape = [2304,384]
我理解这是因为批次的大小只有1.(使用expand_dims我创建一个假批次.)

但我不知道如何解决这个问题？

我到处搜索但没有解决方案..
提前致谢！

from PIL import Imageimport tensorflow as tffrom tensorflow.models.image.cifar10 import cifar10wIDth = 24height = 24categorIEs =  ["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck" ]filename = "path/to/jpg" # absolute path to input imageim = Image.open(filename)im.save(filename,format='JPEG',subsampling=0,quality=100)input_img = tf.image.decode_jpeg(tf.read_file(filename),channels=3)tf_cast = tf.cast(input_img,tf.float32)float_image = tf.image.resize_image_with_crop_or_pad(tf_cast,height,wIDth)images = tf.expand_dims(float_image,0)logits = cifar10.inference(images)_,top_k_pred = tf.nn.top_k(logits,k=5)init_op = tf.initialize_all_variables()with tf.Session() as sess:saver = tf.train.Saver()ckpt = tf.train.get_checkpoint_state('/tmp/cifar10_train')if ckpt and ckpt.model_checkpoint_path:    print("ckpt.model_checkpoint_path ",ckpt.model_checkpoint_path)    saver.restore(sess,ckpt.model_checkpoint_path)else:    print('No checkpoint file found')    exit(0)sess.run(init_op)_,top_indices = sess.run([_,top_k_pred])for key,value in enumerate(top_indices[0]):    print (categorIEs[value] + "," + str(_[0][key]))

编辑

我尝试放置一个占位符,在第一个形状中使用None,但是我收到了这个错误：
必须完全定义新变量(local3 / weights)的形状,而不是(？,384).

现在我真的迷路了……
这是新代码：

from PIL import Imageimport tensorflow as tffrom tensorflow.models.image.cifar10 import cifar10import itertoolswIDth = 24height = 24categorIEs = [ "airplane","truck" ]filename = "toto.jpg" # absolute path to input imageim = Image.open(filename)im.save(filename,quality=100)x = tf.placeholder(tf.float32,[None,24,3])init_op = tf.initialize_all_variables()with tf.Session() as sess: # Restore variables from training checkpoint.    input_img = tf.image.decode_jpeg(tf.read_file(filename),channels=3)    tf_cast = tf.cast(input_img,tf.float32)    float_image = tf.image.resize_image_with_crop_or_pad(tf_cast,wIDth)    images = tf.expand_dims(float_image,0)    i = images.eval()    print (i)    sess.run(init_op,Feed_dict={x: i})    logits = cifar10.inference(x)    _,k=5)    variable_averages = tf.train.ExponentialMovingAverage(        cifar10.MOVING_AVERAGE_DECAY)    variables_to_restore = variable_averages.variables_to_restore()    saver = tf.train.Saver(variables_to_restore)    ckpt = tf.train.get_checkpoint_state('/tmp/cifar10_train')    if ckpt and ckpt.model_checkpoint_path:        print("ckpt.model_checkpoint_path ",ckpt.model_checkpoint_path)        saver.restore(sess,ckpt.model_checkpoint_path)    else:        print('No checkpoint file found')        exit(0)    _,top_k_pred])    for key,value in enumerate(top_indices[0]):        print (categorIEs[value] + "," + str(_[0][key]))

解决方法 我认为这是因为tf.Variable或tf.get_variable获取的变量必须具有完整定义的形状.您可以检查代码并提供完整定义的形状. 总结

以上是内存溢出为你收集整理的python – Tensorflow和cifar 10,测试单个图像全部内容，希望文章能够帮你解决python – Tensorflow和cifar 10,测试单个图像所遇到的程序开发问题。

如果觉得内存溢出网站内容还不错，欢迎将内存溢出网站推荐给程序员好友。