tensorflow操作mnist数据

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/love_image_xie/article/details/84668494

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist = input_data.read_data_sets('data/mnist',one_hot=True)
# 打印“Training data size: 55000”
print("Training data size: ",mnist.train.num_examples)
# 打印“Validating data size: 5000”
print("Validating data size: ",mnist.validation.num_examples)
# 打印“Testing data size: 10000”
print("Testing data size: ",mnist.test.num_examples)
# 打印“Example training data: [0. 0. 0. ... 0.380 0.376 ... 0.]”
print("Example training data: ",mnist.train.images[0])
# 打印“Example training data label: [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]”
print("Example training data label: ",mnist.train.labels[0])

batch_size = 100
# 从train的集合中选取batch_size个训练数据
xs, ys = mnist.train.next_batch(batch_size)
# 输出“X shape:(100,784)”
print("X shape: ", xs.shape)
# 输出"Y shape:(100,10)"
print("Y shape: ", ys.shape)

从上述可看出，input_data.read_data_sets函数自动将mnist数据集划分为train，test，validation，其中train集合中包含55000张图像，validation集合包括5000张图像，test集合包含10000张图像。