Numpy实现卷积神经网络(CNN)的示例
importnumpyasnp
importsys
defconv_(img,conv_filter):
filter_size=conv_filter.shape[1]
result=np.zeros((img.shape))
#循环遍历图像以应用卷积运算
forrinnp.uint16(np.arange(filter_size/2.0,img.shape[0]-filter_size/2.0+1)):
forcinnp.uint16(np.arange(filter_size/2.0,img.shape[1]-filter_size/2.0+1)):
#卷积的区域
curr_region=img[r-np.uint16(np.floor(filter_size/2.0)):r+np.uint16(np.ceil(filter_size/2.0)),
c-np.uint16(np.floor(filter_size/2.0)):c+np.uint16(np.ceil(filter_size/2.0))]
#卷积操作
curr_result=curr_region*conv_filter
conv_sum=np.sum(curr_result)
#将求和保存到特征图中
result[r,c]=conv_sum
#裁剪结果矩阵的异常值
final_result=result[np.uint16(filter_size/2.0):result.shape[0]-np.uint16(filter_size/2.0),
np.uint16(filter_size/2.0):result.shape[1]-np.uint16(filter_size/2.0)]
returnfinal_result
defconv(img,conv_filter):
#检查图像通道的数量是否与过滤器深度匹配
iflen(img.shape)>2orlen(conv_filter.shape)>3:
ifimg.shape[-1]!=conv_filter.shape[-1]:
print("错误：图像和过滤器中的通道数必须匹配")
sys.exit()
#检查过滤器是否是方阵
ifconv_filter.shape[1]!=conv_filter.shape[2]:
print('错误：过滤器必须是方阵')
sys.exit()
#检查过滤器大小是否是奇数
ifconv_filter.shape[1]%2==0:
print('错误：过滤器大小必须是奇数')
sys.exit()
#定义一个空的特征图，用于保存过滤器与图像的卷积输出
feature_maps=np.zeros((img.shape[0]-conv_filter.shape[1]+1,
img.shape[1]-conv_filter.shape[1]+1,
conv_filter.shape[0]))
#卷积操作
forfilter_numinrange(conv_filter.shape[0]):
print("Filter",filter_num+1)
curr_filter=conv_filter[filter_num,:]
#检查单个过滤器是否有多个通道。如果有，那么每个通道将对图像进行卷积。所有卷积的结果加起来得到一个特征图。
iflen(curr_filter.shape)>2:
conv_map=conv_(img[:,:,0],curr_filter[:,:,0])
forch_numinrange(1,curr_filter.shape[-1]):
conv_map=conv_map+conv_(img[:,:,ch_num],curr_filter[:,:,ch_num])
else:
conv_map=conv_(img,curr_filter)
feature_maps[:,:,filter_num]=conv_map
returnfeature_maps
defpooling(feature_map,size=2,stride=2):
#定义池化操作的输出
pool_out=np.zeros((np.uint16((feature_map.shape[0]-size+1)/stride+1),
np.uint16((feature_map.shape[1]-size+1)/stride+1),
feature_map.shape[-1]))
formap_numinrange(feature_map.shape[-1]):
r2=0
forrinnp.arange(0,feature_map.shape[0]-size+1,stride):
c2=0
forcinnp.arange(0,feature_map.shape[1]-size+1,stride):
pool_out[r2,c2,map_num]=np.max([feature_map[r:r+size,c:c+size,map_num]])
c2=c2+1
r2=r2+1
returnpool_out
importskimage.data
importnumpy
importmatplotlib
importmatplotlib.pyplotasplt
importNumPyCNNasnumpycnn
#读取图像
img=skimage.data.chelsea()
#转成灰度图像
img=skimage.color.rgb2gray(img)
#初始化卷积核
l1_filter=numpy.zeros((2,3,3))
#检测垂直边缘
l1_filter[0,:,:]=numpy.array([[[-1,0,1],[-1,0,1],[-1,0,1]]])
#检测水平边缘
l1_filter[1,:,:]=numpy.array([[[1,1,1],[0,0,0],[-1,-1,-1]]])
"""
第一个卷积层
"""
#卷积操作
l1_feature_map=numpycnn.conv(img,l1_filter)
#ReLU
l1_feature_map_relu=numpycnn.relu(l1_feature_map)
#Pooling
l1_feature_map_relu_pool=numpycnn.pooling(l1_feature_map_relu,2,2)
"""
第二个卷积层
"""
#初始化卷积核
l2_filter=numpy.random.rand(3,5,5,l1_feature_map_relu_pool.shape[-1])
#卷积操作
l2_feature_map=numpycnn.conv(l1_feature_map_relu_pool,l2_filter)
#ReLU
l2_feature_map_relu=numpycnn.relu(l2_feature_map)
#Pooling
l2_feature_map_relu_pool=numpycnn.pooling(l2_feature_map_relu,2,2)
"""
第三个卷积层
"""
#初始化卷积核
l3_filter=numpy.random.rand(1,7,7,l2_feature_map_relu_pool.shape[-1])
#卷积操作
l3_feature_map=numpycnn.conv(l2_feature_map_relu_pool,l3_filter)
#ReLU
l3_feature_map_relu=numpycnn.relu(l3_feature_map)
#Pooling
l3_feature_map_relu_pool=numpycnn.pooling(l3_feature_map_relu,2,2)
"""
结果可视化
"""
fig0,ax0=plt.subplots(nrows=1,ncols=1)
ax0.imshow(img).set_cmap("gray")
ax0.set_title("InputImage")
ax0.get_xaxis().set_ticks([])
ax0.get_yaxis().set_ticks([])
plt.savefig("in_img1.png",bbox_inches="tight")
plt.close(fig0)
#第一层
fig1,ax1=plt.subplots(nrows=3,ncols=2)
ax1[0,0].imshow(l1_feature_map[:,:,0]).set_cmap("gray")
ax1[0,0].get_xaxis().set_ticks([])
ax1[0,0].get_yaxis().set_ticks([])
ax1[0,0].set_title("L1-Map1")
ax1[0,1].imshow(l1_feature_map[:,:,1]).set_cmap("gray")
ax1[0,1].get_xaxis().set_ticks([])
ax1[0,1].get_yaxis().set_ticks([])
ax1[0,1].set_title("L1-Map2")
ax1[1,0].imshow(l1_feature_map_relu[:,:,0]).set_cmap("gray")
ax1[1,0].get_xaxis().set_ticks([])
ax1[1,0].get_yaxis().set_ticks([])
ax1[1,0].set_title("L1-Map1ReLU")
ax1[1,1].imshow(l1_feature_map_relu[:,:,1]).set_cmap("gray")
ax1[1,1].get_xaxis().set_ticks([])
ax1[1,1].get_yaxis().set_ticks([])
ax1[1,1].set_title("L1-Map2ReLU")
ax1[2,0].imshow(l1_feature_map_relu_pool[:,:,0]).set_cmap("gray")
ax1[2,0].get_xaxis().set_ticks([])
ax1[2,0].get_yaxis().set_ticks([])
ax1[2,0].set_title("L1-Map1ReLUPool")
ax1[2,1].imshow(l1_feature_map_relu_pool[:,:,1]).set_cmap("gray")
ax1[2,0].get_xaxis().set_ticks([])
ax1[2,0].get_yaxis().set_ticks([])
ax1[2,1].set_title("L1-Map2ReLUPool")
plt.savefig("L1.png",bbox_inches="tight")
plt.close(fig1)
#第二层
fig2,ax2=plt.subplots(nrows=3,ncols=3)
ax2[0,0].imshow(l2_feature_map[:,:,0]).set_cmap("gray")
ax2[0,0].get_xaxis().set_ticks([])
ax2[0,0].get_yaxis().set_ticks([])
ax2[0,0].set_title("L2-Map1")
ax2[0,1].imshow(l2_feature_map[:,:,1]).set_cmap("gray")
ax2[0,1].get_xaxis().set_ticks([])
ax2[0,1].get_yaxis().set_ticks([])
ax2[0,1].set_title("L2-Map2")
ax2[0,2].imshow(l2_feature_map[:,:,2]).set_cmap("gray")
ax2[0,2].get_xaxis().set_ticks([])
ax2[0,2].get_yaxis().set_ticks([])
ax2[0,2].set_title("L2-Map3")
ax2[1,0].imshow(l2_feature_map_relu[:,:,0]).set_cmap("gray")
ax2[1,0].get_xaxis().set_ticks([])
ax2[1,0].get_yaxis().set_ticks([])
ax2[1,0].set_title("L2-Map1ReLU")
ax2[1,1].imshow(l2_feature_map_relu[:,:,1]).set_cmap("gray")
ax2[1,1].get_xaxis().set_ticks([])
ax2[1,1].get_yaxis().set_ticks([])
ax2[1,1].set_title("L2-Map2ReLU")
ax2[1,2].imshow(l2_feature_map_relu[:,:,2]).set_cmap("gray")
ax2[1,2].get_xaxis().set_ticks([])
ax2[1,2].get_yaxis().set_ticks([])
ax2[1,2].set_title("L2-Map3ReLU")
ax2[2,0].imshow(l2_feature_map_relu_pool[:,:,0]).set_cmap("gray")
ax2[2,0].get_xaxis().set_ticks([])
ax2[2,0].get_yaxis().set_ticks([])
ax2[2,0].set_title("L2-Map1ReLUPool")
ax2[2,1].imshow(l2_feature_map_relu_pool[:,:,1]).set_cmap("gray")
ax2[2,1].get_xaxis().set_ticks([])
ax2[2,1].get_yaxis().set_ticks([])
ax2[2,1].set_title("L2-Map2ReLUPool")
ax2[2,2].imshow(l2_feature_map_relu_pool[:,:,2]).set_cmap("gray")
ax2[2,2].get_xaxis().set_ticks([])
ax2[2,2].get_yaxis().set_ticks([])
ax2[2,2].set_title("L2-Map3ReLUPool")
plt.savefig("L2.png",bbox_inches="tight")
plt.close(fig2)
#第三层
fig3,ax3=plt.subplots(nrows=1,ncols=3)
ax3[0].imshow(l3_feature_map[:,:,0]).set_cmap("gray")
ax3[0].get_xaxis().set_ticks([])
ax3[0].get_yaxis().set_ticks([])
ax3[0].set_title("L3-Map1")
ax3[1].imshow(l3_feature_map_relu[:,:,0]).set_cmap("gray")
ax3[1].get_xaxis().set_ticks([])
ax3[1].get_yaxis().set_ticks([])
ax3[1].set_title("L3-Map1ReLU")
ax3[2].imshow(l3_feature_map_relu_pool[:,:,0]).set_cmap("gray")
ax3[2].get_xaxis().set_ticks([])
ax3[2].get_yaxis().set_ticks([])
ax3[2].set_title("L3-Map1ReLUPool")
plt.savefig("L3.png",bbox_inches="tight")
plt.close(fig3)
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