import numpy as np
创新互联专业提供成都主机托管四川主机托管成都服务器托管四川服务器托管，支持按月付款！我们的承诺：贵族品质、平民价格，机房位于中国电信/网通/移动机房，德阳服务器托管服务有保障！
# 序列化和反序列化
import pickle
from sklearn.preprocessing import OneHotEncoder
import warnings
warnings.filterwarnings('ignore')
import tensorflow as tf
数据加载（使用pickle）
def unpickle(file):
import pickle
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='ISO-8859-1')
return dict
labels = []
X_train = []
for i in range(1,6):
data = unpickle('./cifar-10-batches-py/data_batch_%d'%(i))
labels.append(data['labels'])
X_train.append(data['data'])
# 将list类型转换为ndarray
X_train = np.array(X_train)
y_train = np.array(labels).reshape(-1)
# reshape
X_train = X_train.reshape(-1,3072)
# 目标值概率
one_hot = OneHotEncoder()
y_train =one_hot.fit_transform(y_train.reshape(-1,1)).toarray()
# 测试数据加载
test = unpickle('./cifar-10-batches-py/test_batch')
X_test = test['data']
y_test = one_hot.transform(np.array(test['labels']).reshape(-1,1)).toarray()
# 从总数据中获取一批数据
index = 0
def next_batch(X,y):
global index
batch_X = X[index*128:(index+1)*128]
batch_y = y[index*128:(index+1)*128]
index+=1
if index == 390:
index = 0
return batch_X,batch_y
构建神经网络
1.生成对应卷积核
2.tf.nn.conv2d进行卷积运算
3.归一化操作tf.layers.batch_normalization
4.激活函数（relu）
5.池化操作
X = tf.placeholder(dtype=tf.float32,shape = [None,3072])
y = tf.placeholder(dtype=tf.float32,shape = [None,10])
kp = tf.placeholder(dtype=tf.float32)
def gen_v(shape,std = 5e-2):
return tf.Variable(tf.truncated_normal(shape = shape,stddev=std))
def conv(input_,filter_,b):
conv = tf.nn.conv2d(input_,filter_,strides=[1,1,1,1],padding='SAME') + b
conv = tf.layers.batch_normalization(conv,training=True)
conv = tf.nn.relu(conv)
return tf.nn.max_pool(conv,[1,3,3,1],[1,2,2,1],'SAME')
def net_work(X,kp):
# 形状改变，4维
input_ = tf.reshape(X,shape = [-1,32,32,3])
# 第一层
filter1 = gen_v(shape = [3,3,3,64])
b1 = gen_v(shape = [64])
pool1 = conv(input_,filter1,b1)
# 第二层
filter2 = gen_v([3,3,64,128])
b2 = gen_v(shape = [128])
pool2 = conv(pool1,filter2,b2)
# 第三层
filter3 = gen_v([3,3,128,256])
b3 = gen_v([256])
pool3 = conv(pool2,filter3,b3)
# 第一层全连接层
dense = tf.reshape(pool3,shape = [-1,4*4*256])
fc1_w = gen_v(shape = [4*4*256,1024])
fc1_b = gen_v([1024])
bn_fc_1 = tf.layers.batch_normalization(tf.matmul(dense,fc1_w) + fc1_b,training=True)
function(){ //智汇返佣 http://www.kaifx.cn/broker/thinkmarkets.html
relu_fc_1 = tf.nn.relu(bn_fc_1)
# fc1.shape = [-1,1024]
# dropout
dp = tf.nn.dropout(relu_fc_1,keep_prob=kp)
# fc2 输出层
out_w = gen_v(shape = [1024,10])
out_b = gen_v(shape = [10])
out = tf.matmul(dp,out_w) + out_b
return out
损失函数准确率&最优化
out = net_work(X,kp)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y,logits=out))
# 准确率
y_ = tf.nn.softmax(out)
# equal 相当于==
equal = tf.equal(tf.argmax(y,axis = -1),tf.argmax(y_,axis = 1))
accuracy = tf.reduce_mean(tf.cast(equal,tf.float32))
opt = tf.train.AdamOptimizer(0.01).minimize(loss)
opt
开启训练
saver = tf.train.Saver()
epoches = 100
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(epoches):
batch_X,batch_y = next_batch(X_train,y_train)
opt_,loss_ ,score_train= sess.run([opt,loss,accuracy],feed_dict = {X:batch_X,y:batch_y,kp:0.5})
print('iter count:%d。mini_batch loss:%0.4f。训练数据上的准确率：%0.4f。测试数据上准确率：%0.4f'%
(i+1,loss_,score_train,score_test))
if score_train > 0.6:
saver.save(sess,'./model/estimator',i+1)
saver.save(sess,'./model/estimator',i+1)
score_test = sess.run(accuracy,feed_dict = {X:X_test,y:y_test,kp:1.0})
print('测试数据上的准确率：',score_test)
iter count:1。mini_batch loss:3.1455。训练数据上的准确率：0.0938。测试数据上准确率：0.2853
iter count:2。mini_batch loss:3.9139。训练数据上的准确率：0.2891。测试数据上准确率：0.2853
iter count:3。mini_batch loss:5.1961。训练数据上的准确率：0.1562。测试数据上准确率：0.2853
iter count:4。mini_batch loss:3.9102。训练数据上的准确率：0.2344。测试数据上准确率：0.2853
iter count:5。mini_batch loss:4.1278。训练数据上的准确率：0.1719。测试数据上准确率：0.2853
.....
iter count:97。mini_batch loss:1.5752。训练数据上的准确率：0.4844。测试数据上准确率：0.2853
iter count:98。mini_batch loss:1.8480。训练数据上的准确率：0.3906。测试数据上准确率：0.2853
iter count:99。mini_batch loss:1.5662。训练数据上的准确率：0.5391。测试数据上准确率：0.2853
iter count:100。mini_batch loss:1.7489。训练数据上的准确率：0.4141。测试数据上准确率：0.2853
测试数据上的准确率： 0.4711
epoches = 1100
with tf.Session() as sess:
saver.restore(sess,'./model/estimator-100')
for i in range(100,epoches):
batch_X,batch_y = next_batch(X_train,y_train)
opt_,loss_ ,score_train= sess.run([opt,loss,accuracy],feed_dict = {X:batch_X,y:batch_y,kp:0.5})
print('iter count:%d。mini_batch loss:%0.4f。训练数据上的准确率：%0.4f。测试数据上准确率：%0.4f'%
(i+1,loss_,score_train,score_test))
if score_train > 0.6:
saver.save(sess,'./model/estimator',i+1)
saver.save(sess,'./model/estimator',i+1)
if (i%100 == 0) and (i != 100):
score_test = sess.run(accuracy,feed_dict = {X:X_test,y:y_test,kp:1.0})
print('----------------测试数据上的准确率：---------------',score_test)
iter count:101。mini_batch loss:1.4157。训练数据上的准确率：0.5234。测试数据上准确率：0.4711
iter count:102。mini_batch loss:1.6045。训练数据上的准确率：0.4375。测试数据上准确率：0.4711
....
iter count:748。mini_batch loss:0.6842。训练数据上的准确率：0.7734。测试数据上准确率：0.4711
iter count:749。mini_batch loss:0.6560。训练数据上的准确率：0.8203。测试数据上准确率：0.4711
iter count:750。mini_batch loss:0.7151。训练数据上的准确率：0.7578。测试数据上准确率：0.4711
iter count:751。mini_batch loss:0.8092。训练数据上的准确率：0.7344。测试数据上准确率：0.4711
iter count:752。mini_batch loss:0.7394。训练数据上的准确率：0.7422。测试数据上准确率：0.4711
iter count:753。mini_batch loss:0.8732。训练数据上的准确率：0.7188。测试数据上准确率：0.4711
iter count:754。mini_batch loss:0.8762。训练数据上的准确率：0.6953。测试数据上准确率：0.4711
准确率高达80%，博主亲测，以上准确率数据部分展示，大家可以多训练几次。哈哈哈~~~
