TensorFlow 實戰(zhàn)Google深度學(xué)習(xí)框架 第2版 ,鄭澤宇之P96。下載MNIST數(shù)據(jù)，自動分成train， validation和test三個數(shù)據(jù)集，源碼如下：

#!/usr/bin/env python import os from tensorflow.examples.tutorials.mnist import input_data os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'mnist = input_data.read_data_sets("MNIST_data", one_hot=True)print("Training data size:\t", mnist.train.num_examples) print("Validating data size:\t", mnist.validation.num_examples) print("Testing data size:\t", mnist.test.num_examples) print("Example training data:\t", mnist.train.images[0]) print("Example training data label:\t", mnist.train.labels[0])

運行結(jié)果如下：

"C:\Program Files\Python\Python37\python.exe" "D:/Pycharm Projects/MLDemo/MLDemo.py" Extracting MNIST_data\train-labels-idx1-ubyte.gz Extracting MNIST_data\t10k-images-idx3-ubyte.gz Extracting MNIST_data\t10k-labels-idx1-ubyte.gz Training data size: 55000 Validating data size: 5000 Testing data size: 10000 Example training data: [0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0.3803922 0.37647063 0.30196080.46274513 0.2392157 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.35294120.5411765 0.9215687 0.9215687 0.9215687 0.9215687 0.92156870.9215687 0.9843138 0.9843138 0.9725491 0.9960785 0.96078440.9215687 0.74509805 0.08235294 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0.54901963 0.9843138 0.9960785 0.99607850.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.99607850.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.99607850.7411765 0.09019608 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0.8862746 0.9960785 0.81568635 0.7803922 0.7803922 0.78039220.7803922 0.54509807 0.2392157 0.2392157 0.2392157 0.23921570.2392157 0.5019608 0.8705883 0.9960785 0.9960785 0.74117650.08235294 0. 0. 0. 0. 0.0. 0. 0. 0. 0.14901961 0.321568640.0509804 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0.13333334 0.8352942 0.9960785 0.9960785 0.45098042 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.329411770.9960785 0.9960785 0.9176471 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0.32941177 0.9960785 0.99607850.9176471 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0.4156863 0.6156863 0.9960785 0.9960785 0.95294124 0.200000020. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.098039220.45882356 0.8941177 0.8941177 0.8941177 0.9921569 0.99607850.9960785 0.9960785 0.9960785 0.94117653 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0.26666668 0.4666667 0.86274517 0.9960785 0.99607850.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.99607850.9960785 0.5568628 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0.14509805 0.73333335 0.99215690.9960785 0.9960785 0.9960785 0.8745099 0.8078432 0.80784320.29411766 0.26666668 0.8431373 0.9960785 0.9960785 0.458823560. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0.4431373 0.8588236 0.9960785 0.9490197 0.89019614 0.450980420.34901962 0.12156864 0. 0. 0. 0.0.7843138 0.9960785 0.9450981 0.16078432 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0.6627451 0.99607850.6901961 0.24313727 0. 0. 0. 0.0. 0. 0. 0.18823531 0.9058824 0.99607850.9176471 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0.07058824 0.48627454 0. 0.0. 0. 0. 0. 0. 0.0. 0.32941177 0.9960785 0.9960785 0.6509804 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.545098070.9960785 0.9333334 0.22352943 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0.8235295 0.9803922 0.9960785 0.658823550. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0.9490197 0.9960785 0.93725497 0.22352943 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0.34901962 0.9843138 0.94509810.3372549 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0.01960784 0.8078432 0.96470594 0.6156863 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0.01568628 0.458823560.27058825 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. 0. 0.0. 0. 0. 0. ] Example training data label: [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]Process finished with exit code 0

?

總結(jié)

以上是生活随笔為你收集整理的Tensorflow实战之下载MNIST数据，自动分成train， validation和test三个数据集的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯，歡迎將生活随笔推薦給好友。