TensorFlow ValueError:无法为形状为“(?, 64, 64, 3)"的张量 u'Placeholder:0' 提供形状 (64, 64, 3) 的值 [英] TensorFlow ValueError: Cannot feed value of shape (64, 64, 3) for Tensor u'Placeholder:0', which has shape '(?, 64, 64, 3)'

问题描述

我是 TensorFlow 和机器学习的新手.我正在尝试对杯子和随身碟(jpeg 图像)两个对象进行分类.我已经成功训练并导出了一个 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:

I am new to TensorFlow and machine learning. I am trying to classify two objects a cup and a pendrive (jpeg images). I have trained and exported a model.ckpt successfully. Now I am trying to restore the saved model.ckpt for prediction. Here is the script:

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:

When I run the above script for prediction I get the following error:

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?以及如何修复 numpy 数组的形状?

What am I doing wrong? And how do I fix the shape of numpy array?

推荐答案

image 的形状为 (64,64,3).

您的输入占位符 _x 的形状为 (?, 64,64,3).

Your input placeholder _x have a shape of (?, 64,64,3).

问题在于您为占位符提供了不同形状的值.

The problem is that you're feeding the placeholder with a value of a different shape.

你必须用 (1, 64, 64, 3) = a batch of 1 image.

You have to feed it with a value of (1, 64, 64, 3) = a batch of 1 image.

只需将您的 image 值重塑为大小为 1 的批次.

Just reshape your image value to a batch with size one.

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

P.S:输入占位符接受一批图像这一事实意味着您可以并行运行一批图像的预测.您可以尝试使用形状为 (N, 64,64,3)

P.S: the fact that the input placeholder accepts a batch of images, means that you can run predicions for a batch of images in parallel. You can try to read more than 1 image (N images) and than build a batch of N image, using a tensor with shape (N, 64,64,3)

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