will help you To do a logical and operation for tensors containing only 1 and 0 elements you could use the :cmul() member function (elementwise multiplication). code :
th> torch.Tensor({0,1,1,0,0,1,0}):cmul(torch.Tensor({0,1,1,1,0,1,1}))
0
1
1
0
0
1
0
th> torch.Tensor({0,1,1,0,0,1,0}):eq(torch.Tensor({0,1,1,1,0,1,1}))
1
1
1
0
1
1
0
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bad argument #2 to ?(expecting number or torch.DoubleTensor or torch.DoubleStorage at Tensor.c:1125)
By : Loai Isaied
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , after many times trial, I finally find is the reason of type of sent_len, It should be a number type but it is not in my codes.

what's the difference between torch.Tensor() vs torch.empty() in pytorch?
By : Mir
Date : March 29 2020, 07:55 AM
With these it helps torch.Tensor() is just an alias to torch.FloatTensor() which is the default type of tensor, when no dtype is specified during tensor construction. From the torch for numpy users notes, it seems that torch.Tensor() is a dropin replacement of numpy.empty() code :
In [87]: torch.FloatTensor(2, 3)
Out[87]:
tensor([[1.0049e+08, 4.5688e41, 8.9389e38],
[ 3.0638e41, 4.4842e44, 0.0000e+00]])
In [88]: torch.FloatTensor(2, 3)
Out[88]:
tensor([[1.0049e+08, 4.5688e41, 1.6512e38],
[ 3.0638e41, 4.4842e44, 0.0000e+00]])
In [89]: torch.empty(2, 3)
Out[89]:
tensor([[1.0049e+08, 4.5688e41, 9.0400e38],
[ 3.0638e41, 4.4842e44, 0.0000e+00]])
In [90]: torch.empty(2, 3)
Out[90]:
tensor([[1.0049e+08, 4.5688e41, 9.2852e38],
[ 3.0638e41, 4.4842e44, 0.0000e+00]])

What is the difference between torch.tensor and torch.Tensor?
By : Shivani Gupta
Date : March 29 2020, 07:55 AM
wish helps you In PyTorch torch.Tensor is the main tensor class. So all tensors are just instances of torch.Tensor. When you call torch.Tensor() you will get an empty tensor without any data. code :
torch.tensor(data, dtype=None, device=None, requires_grad=False) → Tensor
tensor_without_data = torch.Tensor()
tensor_without_data = torch.tensor()

TypeError Traceback (most recent call last)
<ipythoninput12ebc3ceaa76d2> in <module>()
> 1 torch.tensor()
TypeError: tensor() missing 1 required positional arguments: "data"
torch.tensor(())
tensor([])

Differences between `torch.Tensor` and `torch.cuda.Tensor`
By : Grego
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , So generally both torch.Tensor and torch.cuda.Tensor are equivalent. You can do everything you like with them both. The key difference is just that torch.Tensor occupies CPU memory while torch.cuda.Tensor occupies GPU memory. Of course operations on a CPU Tensor are computed with CPU while operations for the GPU / CUDA Tensor are computed on GPU. code :
import torch
# device will be 'cuda' if a GPU is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# creating a CPU tensor
cpu_tensor = torch.rand(10)
# moving same tensor to GPU
gpu_tensor = cpu_tensor.to(device)
print(cpu_tensor, cpu_tensor.dtype, type(cpu_tensor), cpu_tensor.type())
print(gpu_tensor, gpu_tensor.dtype, type(gpu_tensor), gpu_tensor.type())
print(cpu_tensor*gpu_tensor)
tensor([0.8571, 0.9171, 0.6626, 0.8086, 0.6440, 0.3682, 0.9920, 0.4298, 0.0172,
0.1619]) torch.float32 <class 'torch.Tensor'> torch.FloatTensor
tensor([0.8571, 0.9171, 0.6626, 0.8086, 0.6440, 0.3682, 0.9920, 0.4298, 0.0172,
0.1619], device='cuda:0') torch.float32 <class 'torch.Tensor'> torch.cuda.FloatTensor

RuntimeError Traceback (most recent call last)
<ipythoninput15ac794171c178> in <module>()
12 print(gpu_tensor, gpu_tensor.dtype, type(gpu_tensor), gpu_tensor.type())
13
> 14 print(cpu_tensor*gpu_tensor)
RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #2 'other'

Torch, which command to insert data in a Torch Tensor?
By : sourabh varun verma
Date : March 29 2020, 07:55 AM
hop of those help? There is no function which corresponds to the append functionality of insert since Tensor objects are a fixed size. What I see your code doing is concatenating three tables into one. If you are using Tensors:

