使用TensorFlow搭建一個(gè)全連接神經(jīng)網(wǎng)絡(luò)教程-創(chuàng)新互聯(lián)

說明

發(fā)展壯大離不開廣大客戶長期以來的信賴與支持,我們將始終秉承“誠信為本、服務(wù)至上”的服務(wù)理念,堅(jiān)持“二合一”的優(yōu)良服務(wù)模式,真誠服務(wù)每家企業(yè),認(rèn)真做好每個(gè)細(xì)節(jié),不斷完善自我,成就企業(yè),實(shí)現(xiàn)共贏。行業(yè)涉及成都鑿毛機(jī)等,在重慶網(wǎng)站建設(shè)公司、全網(wǎng)整合營銷推廣、WAP手機(jī)網(wǎng)站、VI設(shè)計(jì)、軟件開發(fā)等項(xiàng)目上具有豐富的設(shè)計(jì)經(jīng)驗(yàn)。

本例子利用TensorFlow搭建一個(gè)全連接神經(jīng)網(wǎng)絡(luò),實(shí)現(xiàn)對(duì)MNIST手寫數(shù)字的識(shí)別。

先上代碼

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

# prepare data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

xs = tf.placeholder(tf.float32, [None, 784])
ys = tf.placeholder(tf.float32, [None, 10])

# the model of the fully-connected network
weights = tf.Variable(tf.random_normal([784, 10]))
biases = tf.Variable(tf.zeros([1, 10]) + 0.1)
outputs = tf.matmul(xs, weights) + biases
predictions = tf.nn.softmax(outputs)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(predictions),
            reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

# compute the accuracy
correct_predictions = tf.equal(tf.argmax(predictions, 1), tf.argmax(ys, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))

with tf.Session() as sess:
 init = tf.global_variables_initializer()
 sess.run(init)
 for i in range(1000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={
   xs: batch_xs,
   ys: batch_ys
  })
  if i % 50 == 0:
   print(sess.run(accuracy, feed_dict={
    xs: mnist.test.images,
    ys: mnist.test.labels
   }))

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