TensorFlow学习笔记（三）：TensorFlow实现逻辑回归模型

一、Mnist数据集的介绍与获取

1.Mnist数据集的介绍

简介

60000行的训练数据集（mnist.train）和10000行的测试数据集（mnist.test）
每一个MNIST数据单元有两部分组成：一张包含手写数字的图片和一个对应的标签。我们把这些图片设为“xs”，把这些标签设为“ys”。训练数据集和测试数据集都包含xs和ys，比如训练数据集的图片是 mnist.train.images ，训练数据集的标签是 mnist.train.labels。

官网：http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html

下载和读取数据集

import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

运行

from tensorflow.examples.tutorials.mnist import input_data

# one_hot 独热编码，也叫一位有效编码。在任意时候只有一位为1，其他位都是0
mnist = input_data.read_data_sets("data/", one_hot=True)

train_images = mnist.train.images
train_labels = mnist.train.labels
test_images = mnist.test.images
test_labels = mnist.test.labels

print("train_images_shape:", train_images.shape)
print("train_labels_shape:", train_labels.shape)
print("test_images_shape:", test_images.shape)
print("test_labels_shape:", test_labels.shape)
print("train_images:", train_images[0]) # 获取55000张里第一张
print("train_images_length:",len(train_images[0]))
print("train_labels:", train_labels[0])

运行结果

Extracting data/train-images-idx3-ubyte.gz
Extracting data/train-labels-idx1-ubyte.gz
Extracting data/t10k-images-idx3-ubyte.gz
Extracting data/t10k-labels-idx1-ubyte.gz
train_images_shape: (55000, 784) # 一共55000张训练数据 784=28*28像素点
train_labels_shape: (55000, 10) # 10列 0到9十个数字
test_images_shape: (10000, 784) # 10000张测试数据 10000行 784列
test_labels_shape: (10000, 10)
train_images: [ 0.          0.          0.          0.          0.          0.          0.
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