import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("mnist_data",one_hot=true)
import matplotlib.pyplot as plt
class net:
def __init__(self):
self.x = tf.placeholder(tf.float32,[none,28,28,1])
self.y = tf.placeholder(tf.float32,[none,10])
self.conv1_w = tf.variable(tf.random_normal([3,3,1,16],dtype=tf.float32,stddev=0.1))
self.conv1_b = tf.variable(tf.zeros([16]))
self.conv2_w = tf.variable(tf.random_normal([3,3,16,32],dtype=tf.float32,stddev=0.1))
self.conv2_b = tf.variable(tf.zeros([32]))
self.w1 = tf.variable(tf.random_normal([7*7*32,128],stddev=0.1))
self.b1 = tf.variable(tf.zeros([128]))
self.w2 = tf.variable(tf.random_normal([128,10],stddev=0.1))
self.b2 = tf.variable(tf.zeros([10]))
def forward(self):
self.conv1 = tf.nn.relu(tf.nn.conv2d(self.x,self.conv1_w,strides=[1,1,1,1],padding='same')+self.conv1_b)
self.pool1 = tf.nn.max_pool(self.conv1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='same')
self.conv2 = tf.nn.relu(tf.nn.conv2d(self.pool1,self.conv2_w,strides=[1,1,1,1],padding='same')+self.conv2_b)
self.pool2 = tf.nn.max_pool(self.conv2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='same')
self.flat = tf.reshape(self.pool2,[-1,7*7*32])
self.y1 = tf.nn.relu(tf.matmul(self.flat,self.w1)+self.b1)
self.y2 = tf.nn.softmax(tf.matmul(self.y1,self.w2)+self.b2)
def backward(self):
self.loss = tf.reduce_mean((self.y2-self.y)**2)
self.opt = tf.train.adamoptimizer().minimize(self.loss)
self.prediction_corect = tf.equal(tf.argmax(self.y2,1),tf.argmax(self.y,1))#比較**值和真實值是否相等
self.rst = tf.cast(self.prediction_corect,'float')#將布林值轉化為float型別
self.accuracy = tf.reduce_mean(self.rst)#求出平均值表示精度（百分數）
if __name__ == '__main__':
net = net()
net.forward()
net.backward()
init = tf.global_variables_initializer()
with tf.session() as sess:
sess.run(init)
a =
b =
c =
for i in range(1000):
x,y = mnist.train.next_batch(100)
x = x.reshape([100,28,28,1])
loss,acc,_ = sess.run([net.loss,net.accuracy,net.opt],feed_dict=)
if i%10 == 0:
plt.subplot(1,2,1)#生成1行兩列的子圖顯示在第乙個子圖
plt.plot(a,b)
plt.title('accuracy rate')
plt.subplot(1,2,2)#生成1行兩列的子圖顯示在第二個子圖
plt.plot(a,c)
plt.title('loss')
plt.pause(0.0001)
print(loss,acc)
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