python keepdim 理解并实现Python中的keepdim功能 在Python中,特别是在使用NumPy或PyTorch等库时,你可能会听到keepdim这个概念。keepdim用于在进行张量操作时控制维度的保留,尤其是在进行求和或其他降维操作时。本文将带你逐步实现keepdim功能,并通过示例代码及详细注释解释每一步。 流程概述 为便于理解,我们
2.keepdim在PyTorch中的应用 在PyTorch中,keepdim参数有着相似的作用。以下是一个利用PyTorch示例来说明其用途: importtorch# 创建一个二维张量tensor_data=torch.tensor([[1,2,3],[4,5,6]])# 沿着维度0求和,保留维度tensor_sum_keepdim=torch.sum(tensor_data,dim=0,keepdim=True)print("保留维度的结果:...
def batch_norm(is_training, x, gamma, beta, moving_mean, moving_var, eps=1e-5, momentum=0.9): if not is_training: x_hat = (x - moving_mean) / torch.sqrt(moving_var + eps) else: mean = x.mean(dim=0, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True) ...
keepdim=True) + norm_eps)**0.5# return tensor * (norm_weights /rms)defrms_norm(tensor, norm_weights):return(tensor * torch.rsqrt (tensor.pow (2).mean (-1, keepdim=True) + norm_eps)) * norm_weights
self.weight = nn.Parameter(torch.ones(dim)) # Learnable scaling parameter def forward(self, x): # Calculate the root mean square (RMS) and normalize return self.weight * (x.float() * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)).type_as(x) ...
# numpy mean = numpy.mean(raw_data, axis=0) # raw_data shape (n_data,n_dim) covar = numpy.cov(raw_data.T) U, S, VT = numpy.linalg.svd(covar) # tensorflow (requires tensorflow_probability) raw_data_tf = tf.constant(raw_data, dtype=tf.dtypes.float32) mean = tf.reduce_mean(...
lat=(ds_gridmet_revised.lat>=bottom)&(ds_gridmet_revised.lat<=top),).mean(dim=['lat','lon'])ds_Austin_CPC=ds_CPC_interp.isel(lon=(ds_CPC_interp.lon>=left)&(ds_CPC_interp.lon<=right),lat=(ds_CPC_interp.lat>=bottom)&(ds_CPC_interp.lat<=top),).mean(dim=['lat','lon']...
ary.resize(dim1, ...dimNn):与重赋值shape同; ary.reshape(dim1, ...dimNn):返回新数组,原数组不变; ndarry函数: ary.astype(dType):转换元素类型,以新数组(copy)形式返回; mean:求均值 sum:求和 cumsum:累加; cumprod:累乘; std:标准差; ...
# Calculating RMSNormdefrms_norm(tensor,norm_weights):# Calculate the mean of the square of tensor values along the last dimensionsquared_mean=tensor.pow(2).mean(-1,keepdim=True)# Add a small value to avoid division by zeronormalized=torch.rsqrt(squared_mean+norm_eps)# Multiply normalized ...
dev. of 7 runs, 1000 loops each) >>> l = ["xyz"]*NUM_ITERS >>> %timeit -n1000 convert_list_to_string(l, NUM_ITERS) 10.1 µs± 1.06 µs per loop (mean± std. dev. of 7 runs, 1000 loops each)Let's increase the number of iterations by a factor of 10....