Creating Tensor in PyTorch

There are various methods to create a tensor in PyTorch.  A tensor can contain elements of a single data type. We can create a tensor using a python list or NumPy array. The torch has 10 variants of tensors for both GPU and CPU. Below are different ways of defining a tensor.

• torch.Tensor() : It copies the data and creates its tensor. It is an alias for torch.FloatTensor.
• torch.tensor() : It also copies the data to create a tensor; however, it infers the data type automatically.
• torch.as_tensor() : The data is shared and not copied in this case while creating the data and accepts any type of array for tensor creation.
• torch.from_numpy() : It is similar to tensor.as_tensor() however it accepts only numpy array.

Example 2:

 Output:

Tensor:  tensor([1, 2, 3, 4, 5, 6]) Data type:  torch.int64 

Tensor:  tensor([1., 2., 3., 4., 5., 6.]) Data type:  torch.float32 

Tensor:  tensor([1.5000, 3.4000, 6.8000, 9.3000, 7.0000, 2.8000], dtype=torch.float64) Data type:  torch.float64 

Tensor:  tensor([1.5000, 3.4000, 6.8000, 9.3000, 7.0000, 2.8000], dtype=torch.float64) Data type:  torch.float64 

Deep Learning is a branch of Machine Learning where algorithms are written that mimic the functioning of a human brain. The most commonly used libraries in deep learning are Tensorflow and PyTorch. Pytorch is an open-source deep learning framework available with a Python and C++ interface. The PyTorch resides inside the torch module. In PyTorch, the data that has to be processed is input in the form of a tensor.