学习笔记（七）unbuntu16.04下实现简单的卷积网络识别MNIST数据集

本机配置：GTX940M 8G

本文环境：ubuntu16.04+tensorflow1.5.0+Sublime text3+python3.5

完整代码：

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
sess = tf.InteractiveSession()

def weight_variable(shape):
initial = tf.truncated_normal(shape,stddev=0.1)
return tf.Variable(initial)

def bias_variable(shape):
initial = tf.constant(0.1,shape=shape)
return tf.Variable(initial)

def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')

x=tf.placeholder(tf.float32,[None,784])
y_=tf.placeholder(tf.float32,[None,10])
x_image=tf.reshape(x,[-1,28,28,1])

W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variable([32])

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7*7*64,1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1) + b_fc1)

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)

W_fc2 = weight_variable([1024,10])
b_fc2 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2)

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ *tf.log(y_conv),reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_conv,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

tf.global_variables_initializer().run()
for i in range(20000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
print("step %d,training accuracy %g"%(i,train_accuracy))
train_step.run(feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})

print("test accuracy %g"%accuracy.eval(feed_dict={
x:mnist.test.images,y_: mnist.test.labels,keep_prob:1.0}))

代码解释见tensorflow中文社区 深入MNIST章节

然后我出现了ResourceExhaustedError的错误：



原因是由于显存不足，解决方法是换个更好的电脑。

好吧不存在的，我穷。

还有另一个解决方法，把patch值改小，

我将代码中的

W_conv1 = weight_variable([5,5,1,32])

和

W_conv2 = weight_variable([5,5,32,64])

中的5改为1，就可以运算了。你也可以不用改到1这么小，根据自身电脑情况吧～反正我的电脑是2都不行只有1可以。。

总共用时26分钟训练了20000次，精确度为96%。

patch值为5的时候精确度是更高的，几乎接近1，但我显卡不够没办法了～