使用TensorFlow搭建一个全连接神经网络教程

使用使用TensorFlow搭建一个全连接神经网络教程搭建一个全连接神经网络教程

今天小编就为大家分享一篇使用TensorFlow搭建一个全连接神经网络教程，具有很好的参考价值，希望对大家

有所帮助。一起跟随小编过来看看吧

说明说明

本例子利用TensorFlow搭建一个全连接神经网络，实现对MNIST手写数字的识别。

先上代码先上代码

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

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:

ys: batch_ys

})

if i % 50 == 0:

print(sess.run(accuracy, feed_dict={

xs: mnist.test.images,

ys: mnist.test.labels

}))

代码解析

1. 读取读取MNIST数据数据

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

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)

使用Softmax函数作为激活函数：