追随梦想，实现梦想

import torchimport numpy as npimport matplotlib.pyplot as pltx_data = [1.0, 2.0, 3.0]y_data = [2.0, 4.0, 6.0]# our model for the forward passdef forward(x): return x * w# loss function...

# 12_3_hello_rnn_seqimport torchimport torch.nn as nnfrom torch.autograd import Variabletorch.manual_seed(777) # reproducibility# 0 1 2 3 4idx2cahr = ['h', 'i', 'e', 'l...

原作者给出的代码运行的时候存在一个问题：TypeError: list indices must be integers or slices, not torch.LongTensor出错的地方在：sys.stdout.write(idx2char[idx.data[0]])sys.stdout.write(idx2char[idx.data[0]])修改为为：sys.stdout.write(i...

代码1import torchimport torch.nn as nnfrom torch.autograd import Variable# One hot encoding for each char in 'hello'h = [1, 0, 0, 0]e = [0, 1, 0, 0]l = [0, 0, 1, 0]o = [0, 0, 0, 1]# One cell R...

实现Inception modules相关文章使用GPU运算from __future__ import print_functionimport argparseimport torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimfrom torchvision impo...

from __future__ import print_functionimport argparseimport torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimfrom torchvision import datasets, transformsfrom t...

pred = torch.max(a,1,keepdim=True)[1]TypeError: torch.max received an invalid combination of arguments - got (torch.LongTensor, int, keepdim=bool), but expected one of: * (torch.LongTensor source) * (...

from __future__ import print_functionimport torchimport torch.nn as nnimport torch.nn.functional as Fimport torch.optim as optimfrom torchvision import datasets, transformsfrom torch.autograd i...

# References# https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/01-basics/pytorch_basics/main.py# http://pytorch.org/tutorials/beginner/data_loading_tutorial.html#dataset-classimport...

纠正了作者代码中个的一个问题，即看数据大小的代码import torchfrom torch.autograd import Variableimport numpy as npxy = np.loadtxt('./data/diabetes.csv.gz', delimiter=',', dtype=np.float32)x_data = Variable(torch.from_num...

记住常用的句型（用多了，应该就记住了）import torchfrom torch.autograd import Variableimport torch.nn.functional as Fx_data = Variable(torch.Tensor([[1.0], [2.0], [3.0], [4.0]]))y_data = Variable(torch.Tensor([[0],...

几个步骤要记住1.Design your model using class、2.Construct loss and optimizer  (select from PyTorch API)3. Training cycle (forward, backward, update)（同时写代码的时候，一定要对齐）import torchfrom torch.autograd import Var...

import torchfrom torch import nnfrom torch.autograd import Variablex_data = [1.0, 2.0, 3.0]y_data = [2.0, 4.0, 6.0]w = Variable(torch.Tensor([1.0]), requires_grad=True) # Any random valueprint...

# Lab 12 RNNimport torchimport torch.nn as nnfrom torch.autograd import Variabletorch.manual_seed(777)idx2char = ['h', 'i', 'e', 'l', 'o']# Tech hihell->ihellox_data = [[0, 1, 0, 2, 3, 3]...