Tensorflow实战之下载MNIST数据，自动分成train， validation和test三个数据集
TensorFlow 实战Google深度学习框架 第2版 ,郑泽宇之P96。下载MNIST数据，自动分成train， validation和test三个数据集，源码如下：
#!/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])运行结果如下：
"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.
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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.
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0. 0. 0. 0. 0. 0.
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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 0.3019608
0.46274513 0.2392157 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.3529412
0.5411765 0.9215687 0.9215687 0.9215687 0.9215687 0.9215687
0.9215687 0.9843138 0.9843138 0.9725491 0.9960785 0.9607844
0.9215687 0.74509805 0.08235294 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0.54901963 0.9843138 0.9960785 0.9960785
0.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.9960785
0.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.9960785
0.7411765 0.09019608 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.8862746 0.9960785 0.81568635 0.7803922 0.7803922 0.7803922
0.7803922 0.54509807 0.2392157 0.2392157 0.2392157 0.2392157
0.2392157 0.5019608 0.8705883 0.9960785 0.9960785 0.7411765
0.08235294 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.14901961 0.32156864
0.0509804 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.13333334 0.8352942 0.9960785 0.9960785 0.45098042 0.
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0. 0. 0. 0. 0. 0.32941177
0.9960785 0.9960785 0.9176471 0. 0. 0.
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0. 0. 0. 0.32941177 0.9960785 0.9960785
0.9176471 0. 0. 0. 0. 0.
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0.4156863 0.6156863 0.9960785 0.9960785 0.95294124 0.20000002
0. 0. 0. 0. 0. 0.
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0. 0. 0. 0. 0. 0.09803922
0.45882356 0.8941177 0.8941177 0.8941177 0.9921569 0.9960785
0.9960785 0.9960785 0.9960785 0.94117653 0. 0.
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0. 0.26666668 0.4666667 0.86274517 0.9960785 0.9960785
0.9960785 0.9960785 0.9960785 0.9960785 0.9960785 0.9960785
0.9960785 0.5568628 0. 0. 0. 0.
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0. 0. 0. 0.14509805 0.73333335 0.9921569
0.9960785 0.9960785 0.9960785 0.8745099 0.8078432 0.8078432
0.29411766 0.26666668 0.8431373 0.9960785 0.9960785 0.45882356
0. 0. 0. 0. 0. 0.
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0.4431373 0.8588236 0.9960785 0.9490197 0.89019614 0.45098042
0.34901962 0.12156864 0. 0. 0. 0.
0.7843138 0.9960785 0.9450981 0.16078432 0. 0.
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0. 0. 0. 0. 0.6627451 0.9960785
0.6901961 0.24313727 0. 0. 0. 0.
0. 0. 0. 0.18823531 0.9058824 0.9960785
0.9176471 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. 0.54509807
0.9960785 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 0.65882355
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.
0.9490197 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 0.9450981
0.3372549 0. 0. 0. 0. 0.
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0. 0. 0. 0. 0. 0.
0.01960784 0.8078432 0.96470594 0.6156863 0. 0.
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0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.01568628 0.45882356
0.27058825 0. 0. 0. 0. 0.
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Example training data label: [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]
Process finished with exit code 0