TensorFlow ValueError：无法为形状为'（？，64，64，3）'的Tensor u'Placeholder：0'输入形状（64，64，3）的值

Question

我是TensorFlow和机器学习的新手。我正在尝试将两个对象归类为杯子和Pendrive（jpeg图像）。我已经成功训练并导出了model.ckpt。现在，我正在尝试恢复保存的model.ckpt以进行预测。这是脚本：

import tensorflow as tf
import math
import numpy as np
from PIL import Image
from numpy import array


# image parameters
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)) 
    # Store weights for our convolution and fully-connected layers
    with tf.name_scope('weights'):
        weights = {
            # 5x5 conv, 3 input channel, 32 outputs each
            'wc1': tf.Variable(tf.random_normal([5, 5, 1 * IMAGE_CHANNELS, 32])),
            # 5x5 conv, 32 inputs, 64 outputs
            'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
            # 5x5 conv, 64 inputs, 128 outputs
            'wc3': tf.Variable(tf.random_normal([5, 5, 64, 128])),
            # 5x5 conv, 128 inputs, 256 outputs
            'wc4': tf.Variable(tf.random_normal([5, 5, 128, 256])),
            # fully connected, k * k * 256 inputs, 1024 outputs
            'wd1': tf.Variable(tf.random_normal([k * k * 256, 1024])),
            # 1024 inputs, 2 class labels (prediction)
            'out': tf.Variable(tf.random_normal([1024, NUM_CLASSES]))
        }

    # Store biases for our convolution and fully-connected layers
    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 "...Model Loaded..."   
       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 'Neural Network predicted', my_classification[0], "for your image"


if __name__ == '__main__':
     main()

当我运行上述脚本进行预测时，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)'

我究竟做错了什么？以及如何修复numpy数组的形状？

Answer 1

image形状为(64,64,3)。

您的输入占位符_x的形状为(?, 64,64,3)。

问题是您要为占位符提供不同形状的值。

您必须将其值(1, 64, 64, 3)=一批1张图像。

只需将您的image价值重塑为一个大小为1的批次即可。

image = array(img).reshape(1, 64,64,3)

PS：输入占位符接受一批图像的事实，这意味着您可以并行运行一批图像的谓词。您可以尝试使用形状为张量的张量读取多于1张图像（N张图像），然后构建一批N张图像(N, 64,64,3)