cifar10 进行多分类

最新推荐文章于 2024-11-11 20:36:16 发布

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最新推荐文章于 2024-11-11 20:36:16 发布 · 404 阅读

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#cifar10数据多分类 #tensorflow 神经网络

本文介绍如何使用Python和TensorFlow从CIFAR-10数据集中加载图片数据，并实现一个二分类模型。通过pickle模块读取数据集，利用numpy进行数据预处理，最后在TensorFlow中构建模型并训练。

import pickle
import numpy as np
import os
CIFAR_DIR = r'./data/cifar-10-batches-py'
print(os.listdir(CIFAR_DIR))

with open(os.path.join(CIFAR_DIR, 'data_batch_1'), 'rb') as f:
    data = pickle.load(f,encoding='latin1')
    print(type(data))
    print(type(data['data']))
    print(data['data'].shape)

image_arr = data['data'][100]
image_arr = image_arr.reshape((3,32,32)) # 32,32,33
image_arr = image_arr.transpose((1,2,0)) # numpy转换维度

import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
imshow(image_arr)
plt.show()

# tensorlow model
import tensorflow as tf
import os
import numpy as np

def load_data(filename):
    """ read data from data file."""
    with open(filename, 'rb') as f:
        data = pickle.load(f,encoding='latin1')
        return data['data'],data['labels']

class CifarData():
    def __init__(self, filenames, need_shuffle):
        all_data = []
        all_labels = []
        for filename in filenames:
            data , labels