pytorch

[pytorch] 순환신경망 (Recurrent Neural Network) 예제 코드

import torchimport torch.nn as nnimport torch.optim as optimimport numpy as npn_hidden = 35lr = 0.01epochs = 1000string = "hello pytorch. how long can a rnn cell remember? show me your limit!"chars = "abcdefghijklmnopqrstuvwxyz ?!.,:;01"char_list = [i for i in chars]n_letters = len(char_list)# one-hot encodingdef string_to_onehot(string): start = np.zeros(shape=n_letters, dtype=int) end = np...

[pytorch] 합성곱 신경망 (Convolution Neural Network) 예제 코드

import torchimport torch.nn as nnimport torch.optim as optimimport torch.nn.init as initimport torchvision.datasets as dsetimport torchvision.transforms as transformsfrom torch.utils.data import DataLoaderbatch_size = 256learning_rate = 0.0002num_epoch = 10mnist_train = dset.MNIST("./", train=True, transform=transforms.ToTensor(), target_transform=None, download=True)mnist_test = dset.MNIST("./"..

[pytorch] 심층신경망 (Deep Neural Network) 예제 코드

import torchimport torch.nn as nnimport torch.nn.init as initimport torch.optim as optimnum_data = 1000num_epoch = 10000noise = init.normal_(torch.FloatTensor(num_data, 1), std=1)x = init.uniform_(torch.Tensor(num_data, 1), -15, 15)y = (x**2) +3y_noise = y + noise# Sequential 함수 안에 작성된 순서대로 데이터 연산 진행# 은닉층은 4개, activation은 ReLU 사용model = nn.Sequential( nn.Linear(1,6), nn.ReLU(), nn.Linea..

[pytorch] 선형 회귀 (Linear Regression) 예제 코드

import torchimport torch.nn as nnimport torch.optim as optimimport torch.nn.init as initnum_data = 1000num_epoch = 500x = init.uniform(torch.Tensor(num_data, 1), -10, 10)noise = init.normal(torch.Tensor(num_data, 1), std=1)y = 2*x+3y_noise = 2*(x+noise)+3model = nn.Linear(1,1)# Loss함수로 L1 Lossloss_func = nn.L1Loss()# Optimizer로 Stochastic Gradient Descentoptimizer = optim.SGD(model.parameters(),..