function - CybAI news

18 Aug

HuberLoss() in PyTorch

stp2y0 CommentsNewscoding, community, development, engineering, function, huberloss, inclusive, lossfunction, pytorch, software

Buy Me a Coffee☕ *Memos: HuberLoss() can get the 0D or more D tensor of the zero or more values(float) computed by Huber Loss from the 0D or more D tensor of zero or more elements as shown below: *Memos: The 1st argument for initialization is reduction(Optional-Default:'mean'-Type:str). *'none', 'mean' or 'sum' can be selected. The 2nd argument for initialization is delta(Optional-Default:1.0-Type:float). *It must be 0<delta. The 1st argument is input(Required-Type:tensor of float). The 2nd argument is target(Required-Type:tensor of float). input and target should be the same size otherwise there is a warning. The empty 1D or more D input and…

02 Aug

The loss functions in PyTorch

stp2y0 CommentsNewscoding, community, development, engineering, function, inclusive, lossfunction, neuralnetwork, pytorch, software

Buy Me a Coffee☕ *My post explains optimizers in PyTorch. A loss function is the function which can get the mean(average) of the sum of the losses(differences) between a model's predictions and true values(train or test data) to optimize a model during training or to evaluate how good a model is during testing. *Loss function is also called Cost Function or Error Function. There are popular loss functions as shown below: (1) L1 Loss: can compute the mean(average) of the sum of the absolute losses(differences) between a model's predictions and true values(train and test data). 's formula: is used for…

25 Jun

Set dtype with dtype argument functions and get it in PyTorch

stp2y0 CommentsNewsargument, coding, community, development, dtype, engineering, function, inclusive, pytorch, software

You can set dtype with the functions which have dtype arguments and get it with dtype and type() as shown below: *Memos: tensor(). *My post explains tensor(): import torch my_tensor = torch.tensor([0, 1, 2]) my_tensor = torch.tensor([0, 1, 2], dtype=torch.int64) my_tensor = torch.tensor([0, 1, 2], dtype=int) my_tensor, my_tensor.dtype, my_tensor.type() # (tensor([0, 1, 2]), torch.int64, 'torch.LongTensor') my_tensor = torch.tensor([0., 1., 2.], dtype=torch.float64) my_tensor = torch.tensor([0., 1., 2.], dtype=float) my_tensor, my_tensor.dtype, my_tensor.type() # (tensor([0., 1., 2.], dtype=torch.float64), # torch.float64, # 'torch.DoubleTensor') my_tensor = torch.tensor([0.+7.j, 1.+4.j, 2.+5.j], dtype=torch.complex32) my_tensor, my_tensor.dtype, my_tensor.type() # (tensor([0.+7.j, 1.+4.j, 2.+5.j], dtype=torch.complex32), # torch.complex32, # 'torch.ComplexHalfTensor') my_tensor = torch.tensor([True,…

17 Jun

The loss functions for Neural Network in PyTorch

stp2y0 CommentsNewscoding, community, development, engineering, function, inclusive, lossfunction, neuralnetwork, pytorch, software

A loss function is the function which can get the difference(gap) between a model's predictions and true values to evaluate how good a model is. *Loss function is also called Cost Function or Error Function. There are popular loss function as shown below: (1) L1 Loss: can compute the average of the sum of the absolute differences between a model's predictions and true values. 's formula is as shown below: is also called Mean Absolute Error(MAE). is L1Loss() in PyTorch. (2) L2 Loss: can compute the average of the sum of the squared differences between a model's predictions and true…