Python Tensorflow和cifar 10,测试单个图像
我试图用tensorflow的cifar-10预测单个图像的类别 我找到了此代码,但失败,出现以下错误: Assign需要两个张量的形状匹配。左侧形状=[18384]右侧形状=[2304384] 我知道这是因为该批次的规模只有1。(使用expand_dims,我创建了一个假批次。) 但我不知道怎么解决这个问题 我到处找,但没有找到解决办法。。 提前谢谢Python Tensorflow和cifar 10,测试单个图像,python,neural-network,tensorflow,deep-learning,Python,Neural Network,Tensorflow,Deep Learning,我试图用tensorflow的cifar-10预测单个图像的类别 我找到了此代码,但失败,出现以下错误: Assign需要两个张量的形状匹配。左侧形状=[18384]右侧形状=[2304384] 我知道这是因为该批次的规模只有1。(使用expand_dims,我创建了一个假批次。) 但我不知道怎么解决这个问题 我到处找,但没有找到解决办法。。 提前谢谢 from PIL import Image import tensorflow as tf from tensorflow.models.ima
from PIL import Image
import tensorflow as tf
from tensorflow.models.image.cifar10 import cifar10
width = 24
height = 24
categories = ["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck" ]
filename = "path/to/jpg" # absolute path to input image
im = 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
我尝试放置占位符,第一个形状中没有占位符,但出现以下错误:
新变量(local3/weights)的形状必须完全定义,但实际上是(?,384)
现在我真的迷路了。。
以下是新代码:
from PIL import Image
import tensorflow as tf
from tensorflow.models.image.cifar10 import cifar10
import itertools
width = 24
height = 24
categories = [ "airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck" ]
filename = "toto.jpg" # absolute path to input image
im = Image.open(filename)
im.save(filename, format='JPEG', subsampling=0, quality=100)
x = tf.placeholder(tf.float32, [None, 24, 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, height, 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)
_, top_k_pred = tf.nn.top_k(logits, 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_indices = sess.run([_, top_k_pred])
for key, value in enumerate(top_indices[0]):
print (categories[value] + ", " + str(_[0][key]))
我认为这是因为通过
tf.variable
或tf.get\u variable
获取的变量必须具有完整定义的形状。您可以检查代码并给出完整定义的形状