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mnist是一份手写数字集，今天利用网站对mnist进行了简单的学习

 

from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data",one_hot=True)import tensorflow as tfx = tf.placeholder("float", [None, 784])w = tf.Variable(tf.zeros([784, 10]))b = tf.Variable(tf.zeros([10]))y = tf.nn.softmax(tf.matmul(x, w)+b)y_ = tf.placeholder("float", [None, 10])cross_entropy = -tf.reduce_sum(y_*tf.log(y))train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)init = tf.initialize_all_variables()print("init")sess = tf.Session()sess.run(init)print("开始计算")for i in range(1000):  batch_xs, batch_ys = mnist.train.next_batch(100) #重点内容  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))print (sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

总结一下，使用了softmax分类，并用了交叉熵作为loss函数，使用的优化器为随机梯度下降法

batch_xs, batch_ys = mnist.train.next_batch(100)

这一句是全文的重点，读取数据

然后查阅了相关资料，看到了关于mnist的数据存储结构

 (train-labels-idx1-ubyte):

[offset] [type]          [value]          [description]

0000     32 bit integer  0x00000801(2049) magic number (MSB first) 0004     32 bit integer  60000            number of items 0008     unsigned byte   ??               label 0009     unsigned byte   ??               label ........ xxxx     unsigned byte   ??               label

The labels values are 0 to 9.

(train-images-idx3-ubyte):

[offset] [type]          [value]          [description]

0000     32 bit integer  0x00000803(2051) magic number 0004     32 bit integer  60000            number of images 0008     32 bit integer  28               number of rows 0012     32 bit integer  28               number of columns 0016     unsigned byte   ??               pixel 0017     unsigned byte   ??               pixel ........ xxxx     unsigned byte   ??               pixel

Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).

TEST SET LABEL FILE (t10k-labels-idx1-ubyte):

[offset] [type]          [value]          [description]

0000     32 bit integer  0x00000801(2049) magic number (MSB first) 0004     32 bit integer  10000            number of items 0008     unsigned byte   ??               label 0009     unsigned byte   ??               label ........ xxxx     unsigned byte   ??               label

The labels values are 0 to 9.

TEST SET IMAGE FILE (t10k-images-idx3-ubyte):

[offset] [type]          [value]          [description]

0000     32 bit integer  0x00000803(2051) magic number 0004     32 bit integer  10000            number of images 0008     32 bit integer  28               number of rows 0012     32 bit integer  28               number of columns 0016     unsigned byte   ??               pixel 0017     unsigned byte   ??               pixel ........ xxxx     unsigned byte   ??               pixel

Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).

 

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