plot_heatmap(ax2,cm_with_norm,'Confusion Matrix\nWith Normalization') # 添加共用的颜色条 cbar_ax=fig.add_axes([0.92,0.15,0.02,0.7]) sm=plt.cm.ScalarMappable(cmap='viridis',norm=plt.Normalize(vmin=0,vmax=np.max([...
plt.colorbar() plt.title('Random Image') plt.show() 在上述代码中,imshow()函数用于显示随机生成的10×10像素的图片,并使用cmap='gray'参数将其显示为灰度图。colorbar()函数则会在旁边显示一个颜色条,指示不同的灰度级别。 二、使用Seaborn进行数据可视化 Seaborn是基于Matplotlib构建的高级可视化库,它简化了...
plot_heatmap(ax2, cm_with_norm, 'Confusion Matrix\nWith Normalization') # 添加共用的颜色条 cbar_ax=fig.add_axes([0.92, 0.15, 0.02, 0.7]) sm=plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=0, vmax=np.max([cm_without_norm, cm_with_norm]))) fig.colorbar(sm, cax=c...
plot_heatmap(ax2,cm_with_norm,'Confusion Matrix\nWith Normalization')# 添加共用的颜色条cbar_ax=fig.add_axes([0.92,0.15,0.02,0.7])sm=plt.cm.ScalarMappable(cmap='viridis',norm=plt.Normalize(vmin=0,vmax=np.max([cm_without_norm,cm_with_norm])))fig.colorbar(sm,cax=cbar_ax)# 调整布局...
plot_heatmap(ax2, cm_with_norm, 'Confusion Matrix\nWith Normalization') # 添加共用的颜色条 cbar_ax=fig.add_axes([0.92, 0.15, 0.02, 0.7]) sm=plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=0, vmax=np.max([cm_without_norm, cm_with_norm]))) ...
# 绘制热图plot_heatmap(ax1, cm_without_norm, 'Confusion Matrix\nWithout Normalization')plot_heatmap(ax2, cm_with_norm, 'Confusion Matrix\nWith Normalization') # 添加共用的颜色条cbar_ax = fig.add_axes([0.92, 0.15, ...
plot_heatmap(ax2, cm_with_norm, 'Confusion Matrix\nWith Normalization') # 添加共用的颜色条 cbar_ax=fig.add_axes([0.92, 0.15, 0.02, 0.7]) sm=plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=0, vmax=np.max([cm_without_norm, cm_with_norm]))) ...
plot_heatmap(ax2, cm_with_norm, 'Confusion Matrix\nWith Normalization') # 添加共用的颜色条 cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7]) sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=0, vmax=np.max([cm_without_norm, cm_with_norm]))) ...
plot_heatmap(ax2, cm_with_norm, 'Confusion Matrix\nWith Normalization') # 添加共用的颜色条 cbar_ax = fig.add_axes([0.92, 0.15, 0.02, 0.7]) sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=0, vmax=np.max([cm_without_norm, cm_with_norm]))) ...