How to use BCELoss, MSELoss (pytorch)



Python
library
pip

MeCab

Common
class

pickle

read/readline

numpy
asfarray

digitize

expit

linalg.solve

meshgrid

mgrid

ndmin

pad

poly1d

polyfit

prod

shape

matplotlib
figure

pcolormesh

scatter

pytorch
BCELoss, MSELoss

device

Embedding

TensorDataset, Dataloader

RNN, LSTM
scikit-learn
SVC

GaussianNB

scipy
interpolate
tkinter
postscript

image display

frame, grid

Crop Image

other
linear interpolation

Hysteresis switch

Square/Triangle wave

OpenAI gym
CartPole-v0

By purpose
1 of K Coding


Release date:2022/2/28         

In Japanese


■Description of BCELoss, MSELoss

Calculates the error function used in machine learning.

■Specific example using BCELoss, MSELoss

<Binary Cross Entropy Loss>

import torch
import torch.nn as nn

loss = nn.BCELoss()
estimate = torch.tensor([0.7,0.2,0.1]) # estimate value
real = torch.tensor([1.0, 0, 0]) # true value or target value

print(loss(estimate , real))

 ⇒ tensor(0.2284)


The calculation is below


<MSE:Mean Square Error>
The format is the same as above, changing the "BCE Loss" part to "MSE Loss".


import torch
import torch.nn as nn

loss = nn.MSELoss()
estimate = torch.tensor([0.7,0.2,0.1]) # estimate value
real = torch.tensor([1.0, 0, 0]) # true value or target value

print(loss(estimate , real))

 ⇒ tensor(0.0467)


The calculation is below










List of related articles



Python
library
pip

MeCab

Common
class

pickle

read/readline

numpy
asfarray

digitize

expit

linalg.solve

meshgrid

mgrid

ndmin

pad

poly1d

polyfit

prod

shape

matplotlib
figure

pcolormesh

scatter

pytorch
BCELoss, MSELoss

device

Embedding

TensorDataset, Dataloader

RNN, LSTM
scikit-learn
SVC

GaussianNB

scipy
interpolate
tkinter
postscript

image display

frame, grid

Crop Image

other
linear interpolation

Hysteresis switch

Square/Triangle wave

OpenAI gym
CartPole-v0

By purpose
1 of K Coding