fashion_mnist识别
fashion_mnist识别
使用relu6代替relu可以的到更好的识别率（循环数据集10次，正确率：75%-85%）
categorical_crossentropy（交叉熵）的使用大大提高了识别的准确性
save_weights保存权重，load_weights加载权重
import tensorflow as tf
import shutil
import os
from tensorflow.keras import Sequential,layers,datasets,optimizers
import threading
import matplotlib.pyplot as plt
import random
plt.rcParams['font.family'] = ['sans-serif']
plt.rcParams['font.sans-serif'] = ['SimHei']
Train_times = 11
lr = 0.0001
def Data_set(x,y):
x = tf.cast(x,dtype=tf.float32)/255.
y = tf.cast(y,dtype=tf.int32)
return x,y
(x,y),(t_x,t_y)= datasets.fashion_mnist.load_data()
db = tf.data.Dataset.from_tensor_slices((x,y))
db = db.map(Data_set).batch(64)
test_db = tf.data.Dataset.from_tensor_slices((t_x,t_y))
test_db = test_db.map(Data_set)
net = Sequential([
layers.Dense(28*28,activation=tf.nn.relu6),
layers.Dense(256,activation=tf.nn.relu6),
layers.Dense(256,activation=tf.nn.relu6),
layers.Dense(10,activation=tf.nn.relu6)
])
try:
net.load_weights("cpkt")
except:pass
path=r"LOG\TEST"
try:
shutil.rmtree(path)
except:pass
summary = tf.summary.create_file_writer(path)
optimizer = optimizers.Adam(lr =lr)
step=0
threading.Thread(target=os.system,args=(r'tensorboard --logdir LOG',)).start()
for sp in range(1,Train_times):
for S,(x,y) in enumerate(db):
x = tf.reshape(x, shape=(-1, 28 * 28))
with tf.GradientTape() as Tape:
out = net(x)
y = tf.one_hot(y, depth=10)
loss2 = tf.losses.categorical_crossentropy(y,out,from_logits=True)
gradient = Tape.gradient(loss2,net.trainable_variables)
optimizer.apply_gradients(zip(gradient,net.trainable_variables))
if (S%100==0):
print(float(tf.reduce_mean(loss2)))
ok_num=0
test_db = test_db.shuffle(100000)
test = iter(test_db)
for i in range(20):
x,y = test.next()
x = tf.reshape(x,shape=(-1,28*28))
out = net(x)
out = int(tf.argmax(out,axis=-1))
y = int(y)
if(out==y):
ok_num+=1
with summary.as_default():
tf.summary.scalar('每20张图片的正确率',float(ok_num/20*100),step=step)
step += 100
print(sp,":",S," Test 20:",ok_num/20*100)
net.save_weights("cpkt")
tf.saved_model.save(net,'model')
xsize=8
ysize=5
n = xsize*ysize
test = iter(test_db)
lable=['T恤','裤子','套头衫','连衣裙','外套','凉鞋','衬衫','运动鞋','包','靴子']
for i in range(1,n,2):
x,y = test.next()
plt.subplot(ysize,xsize,i)
plt.imshow(x,plt.cm.gray)
plt.title(lable[int(y)])
x = tf.reshape(x,shape=(-1,28*28))
result = net(x)
m_p = tf.argmax(result,axis=-1)
y = tf.one_hot([y],depth=10)
i+=1
plt.subplot(ysize, xsize, i)
plt.grid(color='b', ls = '-.', lw = 0.25)
plt.plot(['0','1','2','3','4','5','6','7','8','9'],result.numpy()[-1],color='b',linestyle='-',label='gess')
plt.plot(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], y.numpy()[-1],color='r',linestyle='--',label='real')
plt.title(lable[int(m_p.numpy()[-1])])
plt.show()