大学暑假兼职在酒店干了两个月服务员，是真的累，早上八九点上班准备接客，客人中午一波，晚上一波，遇到会吹逼、能喝的人，经常夜里 11 点下班。一天都要站着，腿酸脚痛，遇到婚庆之类的，就更累了。。。。

大胆一点，没什么大不了！

挖煤警告

@ivmm 租用云没法报销，老板（导师）不想自己掏钱。。。

救救孩子吧

基因竞争的载体罢了，哪来什么意义。

@littlejohnny 为什么你能发语音

性和暴力是刻在我们基因里的

可以看看张宇的考研数学辅导视频，轻松幽默

渔民，出海捕鱼，赶海。

GU

@fzdfengzi 笑死我了

支持一波

@baiye23333 模型要求
1.2 Network Architecture
Implement a neural network with layers in the following order:
Input Image size 32 × 32 × 3.
Convolutional layer Apply 16 flters with size 3 × 3 × 3, stride 1 and padding 0.
Layer input 32 × 32 × 3, output 30 × 30 × 16.
ReLU layer Apply ReLU activation function on each component.
Layer input 30 × 30 × 16, output 30 × 30 × 16.
Pooling layer Max-pooling with size 2 × 2 and stride 2.
Layer input 30 × 30 × 16, output 15 × 15 × 16.
Fully-connected layer Reshape the data to a vector with length 3600 and fully connect to 10 output nodes.
Layer input 15 × 15 × 16 = 3600, output size 10.
Softmax layer Apply softmax function to get the fnal output, indicating the probability in each category.
Layer input size 10, output size 10.
Here you should calculate the forward and backward propagation by yourself.

@baiye23333
import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
np.set_printoptions(threshold=np.inf)
#open file
def unpickle(file):
import pickle
with open(file, 'rb') as fo:
dict = pickle.load(fo,encoding='bytes')
return dict

dict1 = unpickle('cifar-10-batches-py/data_batch_1')
dict2 = unpickle('cifar-10-batches-py/data_batch_2')
dict3 = unpickle('cifar-10-batches-py/data_batch_3')
dict4 = unpickle('cifar-10-batches-py/data_batch_4')
dict5 = unpickle('cifar-10-batches-py/data_batch_5')
test_dict = unpickle("cifar-10-batches-py/test_batch")

data1_4d = np.reshape(dict1[b'data'],(10000, 32, 32, 3), order = 'F')
data1_4d = np.rot90(data1_4d, k = 3, axes = (1,2))
data2_4d = np.reshape(dict2[b'data'],(10000, 32, 32, 3), order = 'F')
data2_4d = np.rot90(data2_4d, k = 3, axes = (1,2))
data3_4d = np.reshape(dict3[b'data'], (10000, 32, 32, 3), order = 'F')
data3_4d = np.rot90(data3_4d, k = 3, axes = (1,2))
data4_4d = np.reshape(dict4[b'data'], (10000, 32, 32, 3), order = 'F')
data4_4d = np.rot90(data4_4d, k = 3, axes = (1,2))
data5_4d = np.reshape(dict5[b'data'], (10000, 32, 32, 3), order = 'F')
data5_4d = np.rot90(data5_4d, k = 3, axes = (1,2))
test_data = np.reshape(test_dict[b'data'], (10000, 32, 32, 3), order = 'F')
test_data = np.rot90(test_data, k = 3, axes = (1,2))

label1 = dict1[b'labels']
label2 = dict2[b'labels']
label3 = dict3[b'labels']
label4 = dict4[b'labels']
label5 = dict5[b'labels']
test_label = test_dict[b'labels']

def softmax(x):
#减去最大值
x-=np.max(x)
x = np.exp(x)/np.sum(np.exp(x))
return x
#权值参数初始化
weight = np.random.normal(loc = 0,scale = 0.01,size = (3,3,3,16))
bias = np.zeros([16],dtype = np.float64)
conv_out = np.zeros([30,30,16],dtype = np.float64)
Maxpool_out = np.zeros([15,15,16],dtype = np.float64)
weight_of_fc = np.random.uniform(0,0.1,size = (3600,10))
fc_in = np.zeros([1,3600],dtype = np.float64)
softmax_out = np.zeros([1,10],dtype = np.float64)
Relu_out = np.zeros([30,30,16],dtype = np.float64)
dl_div_weight = np.zeros([3,3,3,16],dtype = np.float64)
dl_div_bias = np.zeros([16],dtype = np.float64)


def fc_forward(in_pic):
global conv_out, weight, Maxpool_out, bias, Relu_out ,softmax_out
global weight_of_fc, fc_in, dl_div_weight,dl_div_bias
#卷积操作，Convolutional layer Apply 16 flters with size 3 × 3 × 3,
#stride 1 and padding 0，Layer input 32 × 32 × 3, output 30 × 30 × 16.
for i in range (16):
for j in range(30):
for k in range(30):
conv_out[j][k][i] = (in_pic[j:j+3,k:k+3,0] * weight[:,:,0,i]).sum()+ \
(in_pic[j:j+3,k:k+3,1] * weight[:,:,1,i]).sum()+ \
(in_pic[j:j+3,k:k+3,2] * weight[:,:,2,i]).sum()
conv_out += bias
Relu_out = np.choose(conv_out < 0 ,(conv_out,0))#激活函数
for i in range(16):#池化层
for j in range(15):
for k in range(15):
Maxpool_out[j][k][i] = np.max(Relu_out[j*2:j*2+2,k*2:k*2+2,i])
fc_in = np.reshape(Maxpool_out,(1,3600))
fc_out = np.dot(fc_in,weight_of_fc)
softmax_out = softmax(fc_out)
return (np.argmax(fc_out))
#损失函数，交叉熵
#loss =y*np.logp(标签索引对应的)
def back_forward(inputs,label):#优化卷积层和池化层的参数
global conv_out, weight, Maxpool_out, bias, Relu_out ,softmax_out
global weight_of_fc, fc_in, dl_div_weight,dl_div_bias
for index,input_picture in enumerate(inputs):
num_predict = fc_forward(input_picture)
print("softmax_out : ", softmax_out)
print("预测结果: ",num_predict,"真实值: ",label[index])
#loss 对全连接层输出的偏导 p-y,此时 softmax_out 为 dl_div_dz，z 为全连接最后输出
softmax_out[0][label[index]] -= 1

dw_fc = np.dot(np.transpose(fc_in),softmax_out)
#将 fc_in 转为 3600*1，softmax_out 为 1*10，dw_fc 为 3600*10
dl_div_dfc3600 = np.dot(softmax_out,np.transpose(weight_of_fc))
#weight_of_fc 为 3600*10，dl/dz=softmax_out 为 1*10，dl_div_dfc3600：1*3600·
dl_div_dMaxpool_out = np.reshape(dl_div_dfc3600,(15,15,16))

#求对激活层输出(池化层输入)的偏导:
dl_div_dRelu_out = np.zeros([30,30,16],dtype = np.float64)
for i in range(16):
for j in range(15):
for k in range(15):
if Maxpool_out[j][k][i] == Relu_out[j*2][k*2][i]:
dl_div_dRelu_out[j*2][k*2][i] = dl_div_dMaxpool_out[j][k][i]
elif Maxpool_out[j][k][i] == Relu_out[j*2+1][k*2][i]:
dl_div_dRelu_out[j*2+1][k*2][i] = dl_div_dMaxpool_out[j][k][i]
elif Maxpool_out[j][k][i] == Relu_out[j*2][k*2+1][i]:
dl_div_dRelu_out[j*2][k*2+1][i] = dl_div_dMaxpool_out[j][k][i]
else:
dl_div_dRelu_out[j*2+1][k*2+1][i] = dl_div_dMaxpool_out[j][k][i]
#loss 对 relu(input)即 conv_out 的偏导
dReluout_div_convout = np.choose(conv_out >= 0,(0,1))#reluout 对 reluin 的偏导
dl_div_convout = dReluout_div_convout * dl_div_dRelu_out #30*30*16

#loss 对卷积层 w 和 bias 的偏导
for i in range(16):
for j in range(3):
for k in range(3):
for m in range(3):
dl_div_weight[k,m,j,i] = \
(input_picture[k:k+30,m:m+30,j] * dl_div_convout[:,:,i]).sum()
dl_div_bias[i] = dl_div_convout[:,:,i].sum()
weight_of_fc =weight_of_fc - 0.001 * dw_fc
weight = weight - 0.001 * dl_div_weight
bias =bias - 0.001 * dl_div_bias
def train():
back_forward(data1_4d,label1)
back_forward(data2_4d,label2)
back_forward(data3_4d,label3)
back_forward(data4_4d,label4)
back_forward(data5_4d,label5)
train()

奶茶店？