I go to the PyTorch website and select the following options
PyTorch Build: Stable (1.2)
Your OS: Windows
Package:... moreI go to the PyTorch website and select the following options
PyTorch Build: Stable (1.2)
Your OS: Windows
Package: pip
Language: Python 3.7
CUDA: None
(All of these are correct)
Than it displays a command to run
pip3 install torch==1.2.0+cpu torchvision==0.4.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
I have already tried to mix around the the different options but none of them has worked.
ERROR: ERROR: Could not find a version that satisfies the requirement torch==1.2.0+cpu (from versions: 0.1.2, 0.1.2.post1, 0.1.2.post2) ERROR: No matching distribution found for torch==1.2.0+cpu
I tried to do pip install pytorch but pytorch doesn't support pypi less
I am confused about the method view() in the following code snippet.
class Net(nn.Module):
def __init__(self):
super(Net,... moreI am confused about the method view() in the following code snippet.
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2,2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16*5*5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
My confusion is regarding the following line.
x = x.view(-1, 16*5*5)
What does tensor.view() function do? I have seen its usage in many places, but I can't understand how it interprets its parameters.What happens if I give negative values as parameters to the view() function? For example, what happens if I call, tensor_variable.view(1, 1, -1)?Can... less
There seems to be several ways to create a copy of a tensor in Pytorch, including
y = tensor.new_tensor(x) #a
y = x.clone().detach()... moreThere seems to be several ways to create a copy of a tensor in Pytorch, including
y = tensor.new_tensor(x) #a
y = x.clone().detach() #b
y = torch.empty_like(x).copy_(x) #c
y = torch.tensor(x) #d
b is explicitly preferred over a and d according to a UserWarning I get if I execute either a or d. Why is it preferred? Performance? I'd argue it's less readable.Any reasons for/against using c?
Can we train a classification model which has weights available from different dataset and continue the training using new dataset also making use of available weights?
I am confused about the method view() in the following code snippet.
class Net(nn.Module):
def __init__(self):
super(Net,... moreI am confused about the method view() in the following code snippet.
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2,2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16*5*5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
My confusion is regarding the following line.
x = x.view(-1, 16*5*5)
What does tensor.view() function do? I have seen its usage in many places, but I can't understand how it interprets its parameters.What happens if I give negative values as parameters to the view() function? For example, what happens if I call, tensor_variable.view(1, 1, -1)?Can... less
In numpy, we use ndarray.reshape() for reshaping an array.I noticed that in pytorch, people use torch.view() for the same purpose, but at the same time, there is also a... moreIn numpy, we use ndarray.reshape() for reshaping an array.I noticed that in pytorch, people use torch.view() for the same purpose, but at the same time, there is also a torch.reshape() existing.So I am wondering what the differences are between them and when I should use either of them?
I am trying to re-execute a GitHub project on my computer for recommendation using embedding, the goal is to first embed the user and item present in the movieLens dataset, and... moreI am trying to re-execute a GitHub project on my computer for recommendation using embedding, the goal is to first embed the user and item present in the movieLens dataset, and then use the inner product to predict a rating, when I finished the integration of all components, I got an error in the training.
Code:
from lightfm.datasets import fetch_movielens
movielens = fetch_movielens()
ratings_train, ratings_test = movielens, movielens
def _binarize(dataset):
return dataset.tocoo()
train, test = _binarize(movielens), _binarize(movielens)
class ScaledEmbedding(nn.Embedding):
""" Change the scale from normal to """
def reset_parameters(self):
self.weight.data.normal_(0, 1.0 / self.embedding_dim)
if self.padding_idx is not None:
self.weight.data.fill_(0)
I am trying to perform matrix multiplication of multiple matrices in PyTorch and was wondering what is the equivalent of numpy.linalg.multi_dot() in PyTorch?
If there isn't one,... moreI am trying to perform matrix multiplication of multiple matrices in PyTorch and was wondering what is the equivalent of numpy.linalg.multi_dot() in PyTorch?
If there isn't one, what is the next best way (in terms of speed and memory) I can do this in PyTorch?
Code:
import numpy as np
import torch
A = np.random.rand(3, 3)
B = np.random.rand(3, 3)
C = np.random.rand(3, 3)
How do I convert a PyTorch tensor into a python list?
My current use case is to convert a tensor of size into a list of 2048 elements.
My tensor has floating point values. Is... moreHow do I convert a PyTorch tensor into a python list?
My current use case is to convert a tensor of size into a list of 2048 elements.
My tensor has floating point values. Is there a solution which also accounts for int and possibly other data types?
I have checked the PyTorch tutorial and questions similar to this one on Stackoverflow.
I get confused; does the embedding in pytorch (Embedding) make the similar words closer to... moreI have checked the PyTorch tutorial and questions similar to this one on Stackoverflow.
I get confused; does the embedding in pytorch (Embedding) make the similar words closer to each other? And do I just need to give to it all the sentences? Or it is just a lookup table and I need to code the model?
