用TensorFlow搭建一个全连接神经网络
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
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
}))
代码解析
1. 读取MNIST数据
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
2. 建立占位符
xs = tf.placeholder(tf.float32, [None, 784])
ys = tf.placeholder(tf.float32, [None, 10])
-
xs代表图片像素数据, 每张图片(28×28)被展开成(1×784), 有多少图片还未定, 所以shape为None×784. -
ys代表图片标签数据, 0-9十个数字被表示成One-hot形式, 即只有对应bit为1, 其余为0.
3. 建立模型
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：
$$ouput=Softmax(input \times weight + bias)$$
4. 计算正确率
correct_predictions = tf.equal(tf.argmax(predictions, 1), tf.argmax(ys, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
5. 使用模型
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
}))
运行结果
训练1000个循环, 准确率在87%左右.
Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
0.1041
0.632
0.7357
0.7837
0.7971
0.8147
0.8283
0.8376
0.8423
0.8501
0.8501
0.8533
0.8567
0.8597
0.8552
0.8647
0.8654
0.8701
0.8712
0.8712
参考
- 我的个人主页：http://www.techping.cn/
- 我的CSDN博客：http://blog.csdn.net/techping
- 我的简书：http://www.jianshu.com/users/b2a36e431d5e/
- 我的GitHub：https://github.com/techping
