Question

我正在尝试为我的图像分类制作CNN。在开始之前，我正在尝试制作一个简单的神经网络模型。但我有错误按摩，“不能为Tensor'Plankholder_30：0'提供形状值（1,224,224,3），其形状为'（1,150528）'” 我的图像大小都是不同的...所以我将所有图像大小调整为224 * 224 * 3并制作模型以对7个标签进行分类..但我遇到了这个错误，我不知道该怎么办。这是我的代码请帮助

# -*- coding: utf-8 -*-
"""
Created on Fri Jan 12 14:46:07 2018

@author: Yoon seock ju
"""
#clear all the variable in spyder
%reset

#import packages
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from scipy import misc
import glob
import random
from numpy import array


#InteractiveSession ready
sess = tf.InteractiveSession()


#size for resize image data
size = 224,224

#train, test image list
trainset = []
trainlabel = []
testset = []
testlabel = []

#read train_image data
for i in range(7):
    path = "C:/Users/user/Desktop/향수이미지/train/"+str(i)+"/*.jpg"
    for image_path in glob.glob(path):
        image = misc.imread(image_path)
        print(image.shape)
        if (image.shape[0] >224) and (image.shape[1] > 224):
            print("resize start")
            image = misc.imresize(image,size)
            image = array(image).reshape(1, 224,224,3)
            trainset.append(image)
            trainlabel.append(i)
            print("resize image= ",image.shape)
            print(image.dtype)
    print(i," is done")    


#read test_image data
for i in range(7):
    path = "C:/Users/user/Desktop/향수이미지/test/"+str(i)+"/*.jpg"
    for image_path in glob.glob(path):
        image = misc.imread(image_path)
        print(image.shape)
        if (image.shape[0] >224) and (image.shape[1] > 224):
            print("resize start")
            image = misc.imresize(image,size)
            image = array(image).reshape(1,224,224,3)
            testset.append(image)
            testlabel.append(i)
            print("resize image= ",image.shape)
            print(image.dtype)
    print(i," is done")        

#set placeholder
x = tf.placeholder(tf.float32, shape = [1, 150528])#Our images 224*224*3
y_ = tf.placeholder(tf.float32, shape = [None, 7])#Our target

#set Variables
W = tf.Variable(tf.zeros([150528,7]))
b = tf.Variable(tf.zeros([7]))

#start session for initialize variables
sess.run(tf.global_variables_initializer())

#soft max & cost function
y = tf.nn.softmax(tf.matmul(x,W) + b)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y), reduction_indices=[1]))

#training the model
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)



#run train_step
for i in range(1170):
    train_step.run(feed_dict = {x:trainset[i], y_:trainlabel[i]})

Answer 1

您可以更改

行

image = array(image).reshape(1,224,224,3)

到

image = array(image).reshape(1,150528)

使您的数据形状与您的模型的输入形状相匹配。

Answer 2

因此我将所有图像大小调整为224 * 224 * 3并制作模型以对7个标签进行分类

不，你没有，因为

image = array(image).reshape(1,224,224,3)

应该是

image = array(image).reshape(1, 224 * 224 * 3)

Answer 3

针对可能正在使用PIL的其他人

image = Image.open(file_name))
image = image.convert('RGB')

无法为Tensor'Plankholder_30：0'提供形状值（1,224,224,3），其形状为'（1,150528）'

3 个答案: