@
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()