文件名称:
tf.keras.datasets数据源
开发工具:
文件大小: 700mb
下载次数: 0
上传时间: 2020-08-02
详细说明：boston_housing module: Boston housing price regression dataset.
cifar10 module: CIFAR10 small images classification dataset.
cifar100 module: CIFAR100 small images classification dataset.
fashion_mnist module: Fashion-MNIST dataset.
imdb module: IMDB sentiment classification dataset.
mnist module: MNIST handwritten digits dataset.
reuters module: Reuters topic classification dataset.
import tensorflow as tf
from tensorflow import keras
fashion_mnist = keras.datasets.fashion_mnist
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()
mnist = keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
cifar100 = keras.datasets.cifar100
(x_train, y_train), (x_test, y_test) = cifar100.load_data()
cifar10 = keras.datasets.cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
imdb = keras.datasets.imdb
(x_train, y_train), (x_test, y_test) = imdb.load_data()
# word_index is a dictionary mapping words to an integer index
word_index = imdb.get_word_index()
# We reverse it, mapping integer indices to words
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
# We decode the review; note that our indices were offset by 3
# because 0, 1 and 2 are reserved indices for "padding", "start of sequence", and "unknown".
decoded_review = ' '.join([reverse_word_index.get(i - 3, '?') for i in x_train[0]])
print(decoded_review)
boston_housing = keras.datasets.boston_housing
(x_train, y_train), (x_test, y_test) = boston_housing.load_data()
reuters= keras.datasets.reuters
(x_train, y_train), (x_test, y_test) = reuters.load_data()
tf.keras.datasets.reuters.get_word_index(
path='reuters_word_index.json'
)
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