TensorFlow ValueError: Cannot feed value of shape (64, 64, 3) for Tensor u'Placeholder:0', which has shape '(?, 64, 64, 3)' TensorFlow 值错误：无法为形状为 (64, 64, 3) 的张量 u'Placeholder:0' 提供值，该张量的形状为 '(?, 64, 64, 3)'。

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TensorFlow ValueError: Cannot feed value of shape (64, 64, 3) for Tensor u'Placeholder:0', which has shape '(?, 64, 64, 3)' TensorFlow 值错误：无法为形状为 (64, 64, 3) 的张量 u'Placeholder:0' 提供值，该张量的形状为 '(?, 64, 64, 3)'。

我对TensorFlow和机器学习都不太了解。我试图对两个对象进行分类，一个是杯子，一个是U盘（jpeg图像）。我已经成功训练并导出了model.ckpt模型。现在我正在尝试恢复保存的model.ckpt以进行预测。以下是脚本：

import tensorflow as tf
import math
import numpy as np
from PIL import Image
from numpy import array
# 图像参数
IMAGE_SIZE = 64
IMAGE_CHANNELS = 3
NUM_CLASSES = 2
def main():
    image = np.zeros((64, 64, 3))
    img = Image.open('./IMG_0849.JPG')
    img = img.resize((64, 64))
    image = array(img).reshape(64,64,3)
    k = int(math.ceil(IMAGE_SIZE / 2.0 / 2.0 / 2.0 / 2.0)) 
    # 为我们的卷积和全连接层存储权重
    with tf.name_scope('weights'):
        weights = {
            # 5x5卷积，3个输入通道，每个通道32个输出
            'wc1': tf.Variable(tf.random_normal([5, 5, 1 * IMAGE_CHANNELS, 32])),
            # 5x5卷积，32个输入，64个输出
            'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
            # 5x5卷积，64个输入，128个输出
            'wc3': tf.Variable(tf.random_normal([5, 5, 64, 128])),
            # 5x5卷积，128个输入，256个输出
            'wc4': tf.Variable(tf.random_normal([5, 5, 128, 256])),
            # 全连接，k * k * 256个输入，1024个输出
            'wd1': tf.Variable(tf.random_normal([k * k * 256, 1024])),
            # 1024个输入，2个类别标签（预测）
            'out': tf.Variable(tf.random_normal([1024, NUM_CLASSES]))
        }
    # 为我们的卷积和全连接层存储偏差
    with tf.name_scope('biases'):
        biases = {
            'bc1': tf.Variable(tf.random_normal([32])),
            'bc2': tf.Variable(tf.random_normal([64])),
            'bc3': tf.Variable(tf.random_normal([128])),
            'bc4': tf.Variable(tf.random_normal([256])),
            'bd1': tf.Variable(tf.random_normal([1024])),
            'out': tf.Variable(tf.random_normal([NUM_CLASSES]))
        }
   saver = tf.train.Saver()
   with tf.Session() as sess:
       saver.restore(sess, "./model.ckpt")
       print "...模型已加载..."   
       x_ = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE , IMAGE_SIZE , IMAGE_CHANNELS])
       y_ = tf.placeholder(tf.float32, shape=[None, NUM_CLASSES])
       keep_prob = tf.placeholder(tf.float32)
       init = tf.initialize_all_variables()
       sess.run(init)
       my_classification = sess.run(tf.argmax(y_, 1), feed_dict={x_:image})
       print '神经网络预测你的图像为', my_classification[0]
if __name__ == '__main__':
     main()

当我运行上述预测脚本时，出现以下错误：

ValueError: Cannot feed value of shape (64, 64, 3) for Tensor u'Placeholder:0', which has shape '(?, 64, 64, 3)'

我做错了什么？如何修复numpy数组的形状？

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