import gzip import os import pickle import numpy as np def load_mnist(path, kind='train'): labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind) images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind) with gzip.open(labels_path, 'rb') as lbpath: labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8) with gzip.open(images_path, 'rb') as imgpath: images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784) return images, labels def get_mnist_data(num_training=5000, num_validation=500, num_test=500): mnist_dir = r'D:\daima\mnist' # 修改为mnist数据集所在的目录 X_train, y_train = load_mnist(mnist_dir, kind='train') X_test, y_test = load_mnist(mnist_dir, kind='t10k') print(X_train.shape) mask = range(num_training, num_training + num_validation) X_val = X_train[mask] y_val = y_train[mask] mask = range(num_training) X_train = X_train[mask] y_train = y_train[mask] mask = range(num_test) X_test = X_test[mask] y_test = y_test[mask] X_train = X_train.astype('float32') / 255 X_val = X_val.astype('float32') / 255 X_test = X_test.astype('float32') / 255 return X_train, y_train, X_val, y_val, X_test, y_testTypeError: tuple indices must be integers or slices, not str

这段代码出现了一个错误：TypeError: tuple indices must be integers or slices, not str。这是因为在load_mnist函数中返回的是一个元组类型的变量(images, labels)，而在get_mnist_data函数中却使用了类似字典的方式访问这个元组变量，如X_train, y_train = load_mnist(mnist_dir, kind='train')，应该改为X_train, y_train = load_mnist(mnist_dir, kind=0)，X_test, y_test = load_mnist(mnist_dir, kind=1)，这样就能够正确访问这个元组变量的元素了。

相关问题

import os.path import gzip import pickle import os import numpy as np import urllib url_base = 'http://yann.lecun.com/exdb/mnist/' key_file = { 'train_img':'train-images-idx3-ubyte.gz', 'train_label':'train-labels-idx1-ubyte.gz', 'test_img':'t10k-images-idx3-ubyte.gz', 'test_label':'t10k-labels-idx1-ubyte.gz' } dataset_dir = os.path.dirname(os.path.abspath("_file_")) save_file = dataset_dir + "/mnist.pkl" train_num=60000 test_num=10000 img_dim=(1,28,28) img_size=784 def _download(file_name): file_path = dataset_dir+"/"+file_name if os.path.exists(file_path): return print("Downloading"+file_name+" ... ") urllib.request.urlretrieve(url_base + file_name,file_path) print("Done") def download_mnist(): for v in key_file.values(): _download(v) def _load_label(file_name): file_path = dataset_dir+ "/" +file_name print("Converting" + file_name +"to Numpy Array ...") with gzip.open(file_path,'rb') as f: labels = np.frombuffer(f.read(),np.uint8,offset=8) print("Done") return labels def _load_img(file_name): file_path=dataset_dir+"/"+file_name print("Converting"+file_name+"to Numpy Array ...") with gzip.open(file_path,'rb') as f: data = np.frombuffer(f.read(),np.uint8,offset=16) data = data.reshape(-1,img_size) print("Done") return data def _convert_numpy(): dataset = {} dataset['train_img'] = _load_img(key_file['train_img']) dataset['train_label'] = _load_label(key_file['train_label']) dataset['test_img'] = _load_img(key_file['test_img']) dataset['test_label'] = _load_label(key_file['test_label']) return dataset def init_mnist(): download_mnist() dataset = _convert_numpy() print("Creating pickle file ...") with open(save_file,'wb') as f: pickle.dump(dataset,f,-1) print("Done") if __name__ =='__main__': init_mnist()

这段代码是用于下载MNIST数据集并将数据集转换成Numpy数组格式的函数。MNIST数据集是一个手写数字识别数据集，包含了60000张训练图片和10000张测试图片。在函数中，首先定义了数据集的下载地址和四个文件的名称，然后定义了四个函数用于下载和转换数据集。其中，_load_label和_load_img函数用于将标签和图片数据转换成Numpy数组格式，并且在转换过程中使用了gzip库解压缩数据。_convert_numpy函数用于将四个Numpy数组合成一个字典类型的数据集。最后，init_mnist函数用于下载数据集并将数据集转换成Numpy数组格式，并使用pickle库将数据集保存到本地文件中。如果MNIST数据集已经下载并保存到本地文件中，则直接加载本地文件中的数据集。

X_train,T_train=idx2numpy.convert_from_file('emnist/emnist-letters-train-images-idx3-ubyte'),idx2numpy.convert_from_file('emnist/emnist-letters-train-labels-idx1-ubyte')转化为相同形式train_num = 60000 test_num = 10000 img_dim = (1, 28, 28) img_size = 784 def _download(file_name): file_path = dataset_dir + "/" + file_name if os.path.exists(file_path): return print("Downloading " + file_name + " ... ") urllib.request.urlretrieve(url_base + file_name, file_path) print("Done") def download_mnist(): for v in key_file.values(): _download(v) def _load_label(file_name): file_path = dataset_dir + "/" + file_name print("Converting " + file_name + " to NumPy Array ...") with gzip.open(file_path, 'rb') as f: labels = np.frombuffer(f.read(), np.uint8, offset=8) print("Done") return labels def _load_img(file_name): file_path = dataset_dir + "/" + file_name print("Converting " + file_name + " to NumPy Array ...") with gzip.open(file_path, 'rb') as f: data = np.frombuffer(f.read(), np.uint8, offset=16) data = data.reshape(-1, img_size) print("Done") return data def _convert_numpy(): dataset = {} dataset['train_img'] = _load_img(key_file['train_img']) dataset['train_label'] = _load_label(key_file['train_label']) dataset['test_img'] = _load_img(key_file['test_img']) dataset['test_label'] = _load_label(key_file['test_label']) return dataset def init_mnist(): download_mnist() dataset = _convert_numpy() print("Creating pickle file ...") with open(save_file, 'wb') as f: pickle.dump(dataset, f, -1) print("Done!") def _change_one_hot_label(X): T = np.zeros((X.size, 10)) for idx, row in enumerate(T): row[X[idx]] = 1 return T def load_mnist(normalize=True, flatten=True, one_hot_label=False): """读入MNIST数据集 Parameters ---------- normalize : 将图像的像素值正规化为0.0~1.0 one_hot_label : one_hot_label为True的情况下，标签作为one-hot数组返回 one-hot数组是指[0,0,1,0,0,0,0,0,0,0]这样的数组 flatten : 是否将图像展开为一维数组 Returns ------- (训练图像, 训练标签), (测试图像, 测试标签) """ if not os.path.exists(save_file): init_mnist() with open(save_file, 'rb') as f: dataset = pickle.load(f) if normalize: for key in ('train_img', 'test_img'): dataset[key] = dataset[key].astype(np.float32) dataset[key] /= 255.0 if one_hot_label: dataset['train_label'] = _change_one_hot_label(dataset['train_label']) dataset['test_label'] = _change_one_hot_label(dataset['test_label']) if not flatten: for key in ('train_img', 'test_img'): dataset[key] = dataset[key].reshape(-1, 1, 28, 28) return (dataset['train_img'], dataset['train_label']), (dataset['test_img'], dataset['test_label']) if name == 'main': init_mnist()模仿这段代码将获取同样形式

你提供的是MNIST数据集的加载代码，而不是EMNIST。如果要加载EMNIST数据集，可以使用以下代码：

import idx2numpy
import numpy as np

def load_emnist():
    X_train = idx2numpy.convert_from_file('emnist/emnist-letters-train-images-idx3-ubyte')
    T_train = idx2numpy.convert_from_file('emnist/emnist-letters-train-labels-idx1-ubyte')
    X_test = idx2numpy.convert_from_file('emnist/emnist-letters-test-images-idx3-ubyte')
    T_test = idx2numpy.convert_from_file('emnist/emnist-letters-test-labels-idx1-ubyte')
    # 将数据展开为一维数组
    X_train = X_train.reshape(X_train.shape[0], -1)
    X_test = X_test.reshape(X_test.shape[0], -1)
    # 将标签转换为one-hot编码
    T_train = np.eye(26)[T_train]
    T_test = np.eye(26)[T_test]
    return (X_train, T_train), (X_test, T_test)

这个代码将返回一个tuple，其中第一个元素是训练数据，第二个元素是测试数据，每个数据都是一个tuple，其中第一个元素是图像数据，第二个元素是标签数据。图像数据是展开为一维数组的，标签数据是one-hot编码的。