@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？

说不定会带个对象回来

你们去团我偷塔

最近 V 站超纲题越来越多了

不管怎样，生活还要继续。

181,65kg,饭量很大，就是吃不胖，反而都长肌肉了，以前还没毕业，同学都叫我魔鬼筋肉人，很烦。。

近视 10 年，现在 800 度还有救吗?

@yestodayHadRain 你是不是错过了什么

客服跟我说去大一点的营业厅才能办

我也经常搞混 true 和 true🐕

基本语法就行了吧，条件判断，循环之类，文件操作

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