我们在使用数控加工中心的过程中,最常见的数控代码有两种,一种是G代码,一种是M代码。本文整理了常见的G代码和M代码的含义,不同厂商不同的数控系统可能稍有出入,在实际中以说明书为准。...G代码:准备功能, 控制机床动作(比如G00快速移动) M代码:辅助功能, 辅助机
CR400BF-S型动车组GHMI屏报故障代码5236,TBM收到的手柄指令相差2级及以上时: 1.人工施加( )停车。 2.停车后施加停放制动,逐级进行B2-B7级位操作,每级位停留时间大于5秒,若无故障报出,正常运行。 3.若有故障报出,如B5-B7任一级位无故障,使用无故障级位或EB级位控车维持运行;若B5-B7级位均报出故障,...
gghistogram(df1, x="weight", add = "mean", rug = TRUE, fill = "sex", palette = c("#00AFBB", "#E7B800")) # 同时设置边框和填充颜色gghistogram(df1, x="weight", add = "mean", rug = TRUE, color = "sex", fill = "sex", palette = c("#00AFBB", "#E7B800")) # 修改x...
error = y_train_tensor - yhat loss = (error ** 2).mean() # No more manual computation of gradients! # a_grad = -2 * error.mean() # b_grad = -2 * (x_tensor * error).mean() # We just tell PyTorch to work its way BACKWARDS from the specified loss! loss.backward() # Let...
代码: import numpy as np x=np.random.random(8) y=np.random.random(8) #方法一:根据公式求解 x_=x-np.mean(x) y_=y-np.mean(y) d1=np.dot(x_,y_)/(np.linalg.norm(x_)*np.linalg.norm(y_)) #方法二:根据numpy库求解 X=np.vstack([x,y]) ...
# 平均值(mean)设置为0,标准差(stdev)设置为0.02。 netG.apply(weights_init) # 打印生成器模型 print(netG) Generator( (main): Sequential( (0): ConvTranspose2d(100,512, kernel_size=(4,4), stride=(1,1), bias=False) (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, tra...
mean_std.py:计算mean和std的值。 makedata.py:生成数据集。 为了能在DP方式中使用混合精度,还需要在模型的forward函数前增加@autocast()。 计算mean和std 为了使模型更加快速的收敛,我们需要计算出mean和std的值,新建mean_std.py,插入代码: from torchvision.datasets import ImageFolder ...
二、代码实现¶ 1、配置代码¶ In [1]: importargparseimportosimportnumpyasnpimportmathimporttorchvision.transformsastransformsfromtorchvision.utilsimportsave_imagefromtorch.utils.dataimportDataLoaderfromtorchvisionimportdatasetsfromtorch.autogradimportVariableimporttorch.nnasnnimporttorch.nn.functionalasFimporttorch...
##'@固定因子 data_matrix$sample <- factor(data_matrix$sample, levels = c("CK","80-1","4-7","20-7","4-14","20-14","4-60","20-60")) ggplot(data_matrix, aes(x = sample, y = value, fill = sample)) + stat_summary(fun.data = mean_sd, geom = "errorbar", width =...
ggviolin(df4, x ="dose", y ="len",add= c("jitter","mean_sd")) # 添加误差棒 ggviolin(df4, x ="dose", y ="len",add="mean_sd", error.plot ="crossbar") # 修改颜色 ggviolin(df4,"dose","len",color ="black", fill ="gray") ...