MNIST機器學習入門

2021-09-24 17:07:06 字數 1274 閱讀 1436

使用mnist 資料集，

用softmax回歸**標籤

使用交叉熵損失函式計算損失值

使用梯度下降法優化引數

#


-*- coding: utf-8 -*-


import


tensorflow as tf


#import


tensorflow.examples.tutorials.mnist.input_data as input_data


mnist = input_data.read_data_sets("


mnist_data/


", one_hot=true)


x = tf.placeholder(tf.float32, [none, 784])


y_= tf.placeholder(tf.float32, [none, 10])    #


實際標籤


w = tf.variable(tf.zeros([784, 10]))


b = tf.variable(tf.zeros([10]))


y = tf.nn.softmax(tf.matmul(x, w) +b)          #


**標籤


loss = -tf.reduce_sum(y_*tf.log(y))               #


計算損失值


train_step = tf.train.gradientdescentoptimizer(0.01).minimize(loss)#


梯度下降優化引數


init =tf.global_variables_initializer()


with tf.session() as sess:


sess.run(init)


for i in range(1000):


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


傳入訓練集


sess.run(train_step,feed_dict=)#


訓練correct = tf.equal(tf.argmax(y,1), tf.argmax(y_ ,1))


accuracy = tf.reduce_mean(tf.cast(correct, "


float"))


print (sess.run(accuracy, feed_dict=))  #


傳入測試集

posted on

2017-09-06 17:04

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