import urllib.request import os import tarfile import tensorflow as tf import numpy as np import pickle as p import matplotlib.pyplot as plt # 下载 url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz' filepath = 'data/cifar-10-python.tar.gz' if not os.path.isfile(filepath): result = urllib.request.urlretrieve(url, filepath) print("downloaded: ", result) else: print('Data file already exists!') # 解压 if not os.path.exists("data/cifar-10-batches-py"): tfile = tarfile.open('data/cifar-10-python.tar.gz') result = tfile.extractall('data/') print("Extract to ./data/cifar/cifar-10-batches-py/") else: print('Directory already exists!') def load_CIFAR_batch(filename): # 读取一个批次的样本，10000条 """load single batch of cifar""" with open(filename,"rb") as f: # 一个样本由标签和图像数据组成 # <1×label><3072×pixel>(3072=32*32*3) data_dict = p.load(f, encoding="bytes") images = data_dict[b"data"] labels = data_dict[b'labels'] # 把原始数据结构调整为BCWH(batches,channels,width,height) images = images.reshape(10000, 3, 32, 32) # 四维数组，维度的索引分别为0,1,2,3 # tensorflow处理数据图像的结构BWHC # 所以要把通道数据移动到最后一个维度:transpose函数是按照后面参数(可以理解为围绕的轴)对原数四维组进行转置 # 对照维度的索引，大致是把索引重新排列以实现转置（0,1,2,3）-（0,2,3,1），就是把原来索引1排到末尾了 # 一般低维数组（一维二维）用.T进行转置，高维数组用.transpose(索引参数)进行转置 images = images.transpose(0,2,3,1) labels = np.array(labels) return images, labels def load_CIFAR_data(data_dir): # 完整读取数据集 """load cifar data""" images_train = [] labels_train = [] for i in range(5): f = os.path.join(data_dir, 'data_batch_%d' % (i+1)) print('loading', f) # 调用load_CIFAR_batch()获得批量的图像及其对应的标签 image_batch, label_batch = load_CIFAR_batch(f) images_train.append(image_batch) labels_train.append(label_batch) # np.concatenate函数实现将多个数组进行合并成为一个数组，合并结果会降低一维(将索引为0和1的两维合并成一维) # 如原先的数组为(4,2,2)，用np.concatenate()后变成两维(8,2)数组 Xtrain = np.concatenate(images_train) Ytrain = np.concatenate(labels_train) del image_batch, label_batch Xtest, Ytest = load_CIFAR_batch(os.path.join(data_dir, 'test_batch')) print('finished loading CIFAR-10 data!') # 返回训练集图像和标签，测试集图像和标签 return Xtrain, Ytrain, Xtest, Ytest # 调用数据读取的函数读取数据 data_dir = 'data/cifar-10-batches-py/' Xtrain, Ytrain, Xtest, Ytest = load_CIFAR_data(data_dir) # 察看数据集的信息 print("training data shape: ", Xtrain.shape) # (50000, 32, 32, 3) print("training label shape: ", Ytrain.shape) # (50000,) print("testing data shape: ", Xtest.shape) # (10000, 32, 32, 3) print("testing data shape: ", Ytest.shape) # (10000, ) plt.show() plt.imshow(Xtrain[5]) plt.imshow(Xtest[65]) plt.show() print(Ytrain[5]) print(Ytest[1000])arrays must be of dtype byte, short, float32 or float64怎么解决

已完成理解「import urllib.request import os import tarfile import tensorflow as tf import numpy as np import pickle as p import matplotlib.pyplot as plt # 下载 url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz' filepath = 'data/cifar-10-python.tar.gz' if not os.path.isfile(filepath): result = urllib.request.urlretrieve(url, filepath) print("downloaded: ", result) else: print('Data file already exists!') # 解压 if not os.path.exists("data/cifar-10-batches-py"): tfile = tarfile.open('data/cifar-10-python.tar.gz') result = tfile.extractall('data/') print("Extract to ./data/cifar/cifar-10-batches-py/") else: print('Directory already exists!') def load_CIFAR_batch(filename): # 读取一个批次的样本，10000条 """load single batch of cifar""" with open(filename,"rb") as f: # 一个样本由标签和图像数据组成 # <1×label><3072×pixel>(3072=32*32*3) data_dict = p.load(f, encoding="bytes") images = data_dict[b"data"] labels = data_dict[b'labels'] # 把原始数据结构调整为BCWH(batches,channels,width,height) images = images.reshape(10000, 3, 32, 32) # 四维数组，维度的索引分别为0,1,2,3 # tensorflow处理数据图像的结构BWHC # 所以要把通道数据移动到最后一个维度:transpose函数是按照后面参数(可以理解为围绕的轴)对原数四维组进行转置 # 对照维度的索引，大致是把索引重新排列以实现转置（0,1,2,3）-（0,2,3,1），就是把原来索引1排到末尾了 # 一般低维数组（一维二维）用.T进行转置，高维数组用.transpose(索引参数)进行转置 images = images.transpose(0,2,3,1) labels = np.array(labels) return images, labels def load_CIFAR_data(data_dir): # 完整读取数据集 """load cifar data""" images_train = [] labels_train = [] for i in range(5): f = os.path.join(data_dir, 'data_batch_%d' % (i+1)) print('loading', f) # 调用load_CIFAR_batch()获得批量的图像及其对应的标签 image_batch, label_batch = load_CIFAR_batch(f) images_train.append(image_batch) labels_train.append(label_batch) # np.concatenate函数实现将多个数组进行合并成为一个数组，合并结果会降低一维(将索引为0和1的两维合并成一维) # 如原先的数组为(4,2,2)，用np.concatenate()后变成两维(8,2)数组 Xtrain = np.concatenate(images_train) Ytrain = np.concatenate(labels_train) del image_batch, label_batch Xtest, Ytest = load_CIFAR_batch(os.path.join(data_dir, 'test_batch')) print('finished loading CIFAR-10 data!') # 返回训练集图像和标签，测试集图像和标签 return Xtrain, Ytrain, Xtest, Ytest # 调用数据读取的函数读取数据 data_dir = 'data/cifar-10-batches-py/' Xtrain, Ytrain, Xtest, Ytest = load_CIFAR_data(data_dir) # 察看数据集的信息 print("training data shape: ", Xtrain.shape) # (50000, 32, 32, 3) print("training label shape: ", Ytrain.shape) # (50000,) print("testing data shape: ", Xtest.shape) # (10000, 32, 32, 3) print("testing data shape: ", Ytest.shape) # (10000, ) plt.show() plt.imshow(Xtrain[5]) plt.imshow(Xtest[65]) plt.show() print(Ytrain[5]) print(Ytest[1000])arrays must be of dtype byte, short, float32 or float64怎么解决」