Final Up to date on November 15, 2022

PyTorch is an open-source deep studying framework based mostly on Python language. It lets you construct, prepare, and deploy deep studying fashions, providing plenty of versatility and effectivity.

PyTorch is primarily centered on tensor operations whereas a tensor could be a quantity, matrix, or a multi-dimensional array.

On this tutorial, we are going to carry out some primary operations on one-dimensional tensors as they’re advanced mathematical objects and an important a part of the PyTorch library. Subsequently, earlier than going into the element and extra superior ideas, one ought to know the fundamentals.

After going by means of this tutorial, you’ll:

- Perceive the fundamentals of one-dimensional tensor operations in PyTorch.
- Learn about tensor varieties and shapes and carry out tensor slicing and indexing operations.
- Be capable of apply some strategies on tensor objects, equivalent to imply, customary deviation, addition, multiplication, and extra.

Let’s get began.

**Varieties and Shapes of One-Dimensional Tensors**

First off, let’s import just a few libraries we’ll use on this tutorial.

import torch import numpy as np import pandas as pd |

When you’ve got expertise in different programming languages, the best technique to perceive a tensor is to think about it as a multidimensional array. Subsequently, a one-dimensional tensor is just a one-dimensional array, or a vector. With a view to convert a listing of integers to tensor, apply `torch.tensor()`

constructor. As an example, we’ll take a listing of integers and convert it to numerous tensor objects.

int_to_tensor = torch.tensor([10, 11, 12, 13]) print(“Tensor object kind after conversion: “, int_to_tensor.dtype) print(“Tensor object kind after conversion: “, int_to_tensor.kind()) |

Tensor object kind after conversion: torch.int64 Tensor object kind after conversion: torch.LongTensor |

Additionally, you’ll be able to apply the identical technique torch.tensor() to transform a float checklist to a float tensor.

float_to_tensor = torch.tensor([10.0, 11.0, 12.0, 13.0]) print(“Tensor object kind after conversion: “, float_to_tensor.dtype) print(“Tensor object kind after conversion: “, float_to_tensor.kind()) |

Tensor object kind after conversion: torch.float32 Tensor object kind after conversion: torch.FloatTensor |

Notice that components of a listing that should be transformed right into a tensor will need to have the identical kind. Furthermore, if you wish to convert a listing to a sure tensor kind, torch additionally lets you do this. The code traces under, for instance, will convert a listing of integers to a float tensor.

int_list_to_float_tensor = torch.FloatTensor([10, 11, 12, 13]) int_list_to_float_tensor.kind() print(“Tensor kind after conversion: “, int_list_to_float_tensor.kind()) |

Tensor kind after conversion: torch.FloatTensor |

Equally, `dimension()`

and `ndimension()`

strategies let you discover the scale and dimensions of a tensor object.

print(“Dimension of the int_list_to_float_tensor: “, int_list_to_float_tensor.dimension()) print(“Dimensions of the int_list_to_float_tensor: “,int_list_to_float_tensor.ndimension()) |

Dimension of the int_list_to_float_tensor: torch.Dimension([4]) Dimensions of the int_list_to_float_tensor: 1 |

For reshaping a tensor object, `view()`

technique will be utilized. It takes `rows`

and `columns`

as arguments. For instance, let’s use this technique to reshape `int_list_to_float_tensor`

.

reshaped_tensor = int_list_to_float_tensor.view(4, 1) print(“Unique Dimension of the tensor: “, reshaped_tensor) print(“New dimension of the tensor: “, reshaped_tensor) |

Unique Dimension of the tensor: tensor([[10.], [11.], [12.], [13.]]) New dimension of the tensor: tensor([[10.], [11.], [12.], [13.]]) |

As you’ll be able to see, the `view()`

technique has modified the scale of the tensor to `torch.Dimension([4, 1])`

, with 4 rows and 1 column.

Whereas the variety of components in a tensor object ought to stay fixed after `view()`

technique is utilized, you should use `-1`

(equivalent to `reshaped_tensor`

) to reshape a dynamic-sized tensor.**.**view(-1, 1)

**Changing Numpy Arrays to Tensors**

Pytorch additionally lets you convert NumPy arrays to tensors. You should use `torch.from_numpy`

for this operation. Let’s take a NumPy array and apply the operation.

numpy_arr = np.array([10.0, 11.0, 12.0, 13.0]) from_numpy_to_tensor = torch.from_numpy(numpy_arr)
print(“dtype of the tensor: “, from_numpy_to_tensor.dtype) print(“kind of the tensor: “, from_numpy_to_tensor.kind()) |

dtype of the tensor: torch.float64 kind of the tensor: torch.DoubleTensor |

Equally, you’ll be able to convert the tensor object again to a NumPy array. Let’s use the earlier instance to point out the way it’s completed.

tensor_to_numpy = from_numpy_to_tensor.numpy() print(“again to numpy from tensor: “, tensor_to_numpy) print(“dtype of transformed numpy array: “, tensor_to_numpy.dtype) |

again to numpy from tensor: [10. 11. 12. 13.] dtype of transformed numpy array: float64 |

**Changing Pandas Collection to Tensors**

It’s also possible to convert a pandas collection to a tensor. For this, first you’ll must retailer the pandas collection with `values()`

operate utilizing a NumPy array.

pandas_series=pd.Collection([1, 0.2, 3, 13.1]) store_with_numpy=torch.from_numpy(pandas_series.values) print(“Saved tensor in numpy array: “, store_with_numpy) print(“dtype of saved tensor: “, store_with_numpy.dtype) print(“kind of saved tensor: “, store_with_numpy.kind()) |

Saved tensor in numpy array: tensor([ 1.0000, 0.2000, 3.0000, 13.1000], dtype=torch.float64) dtype of saved tensor: torch.float64 kind of saved tensor: torch.DoubleTensor |

Moreover, the Pytorch framework permits us to do loads with tensors equivalent to its `merchandise()`

technique returns a python quantity from a tensor and `tolist()`

technique returns a listing.

new_tensor=torch.tensor([10, 11, 12, 13]) print(“the second merchandise is”,new_tensor[1].merchandise()) tensor_to_list=new_tensor.tolist() print(‘tensor:’, new_tensor,“nlist:”,tensor_to_list) |

the second merchandise is 11 tensor: tensor([10, 11, 12, 13]) checklist: [10, 11, 12, 13] |

**Indexing and Slicing in One-Dimensional Tensors**

Indexing and slicing operations are nearly the identical in Pytorch as python. Subsequently, the primary index all the time begins at 0 and the final index is lower than the overall size of the tensor. Use sq. brackets to entry any quantity in a tensor.

tensor_index = torch.tensor([0, 1, 2, 3]) print(“Test worth at index 0:”,tensor_index[0]) print(“Test worth at index 3:”,tensor_index[3]) |

Test worth at index 0: tensor(0) Test worth at index 3: tensor(3) |

Like a listing in python, you too can carry out slicing operations on the values in a tensor. Furthermore, the Pytorch library lets you change sure values in a tensor as effectively.

Let’s take an instance to test how these operations will be utilized.

example_tensor = torch.tensor([50, 11, 22, 33, 44]) sclicing_tensor = example_tensor[1:4] print(“instance tensor : “, example_tensor) print(“subset of instance tensor:”, sclicing_tensor) |

instance tensor : tensor([50, 11, 22, 33, 44]) subset of instance tensor: tensor([11, 22, 33]) |

Now, let’s change the worth at index 3 of `example_tensor`

:

print(“worth at index 3 of instance tensor:”, example_tensor[3]) example_tensor[3] = 0 print(“new tensor:”, example_tensor) |

worth at index 3 of instance tensor: tensor(0) new tensor: tensor([50, 11, 22, 0, 44]) |

**Some Features to Apply on One-Dimensional Tensors**

On this part, we’ll evaluate some statistical strategies that may be utilized on tensor objects.

**Min and Max Features**

These two helpful strategies are employed to seek out the minimal and most worth in a tensor. Right here is how they work.

We’ll use a `sample_tensor`

for instance to use these strategies.

sample_tensor = torch.tensor([5, 4, 3, 2, 1]) min_value = sample_tensor.min() max_value = sample_tensor.max() print(“test minimal worth within the tensor: “, min_value) print(“test most worth within the tensor: “, max_value) |

test minimal worth within the tensor: tensor(1) test most worth within the tensor: tensor(5) |

**Imply and Customary Deviation**

Imply and customary deviation are sometimes used whereas doing statistical operations on tensors. You possibly can apply these two metrics utilizing `.imply()`

and `.std()`

capabilities in Pytorch.

Let’s use an instance to see how these two metrics are calculated.

mean_std_tensor = torch.tensor([–1.0, 2.0, 1, –2]) Imply = mean_std_tensor.imply() print(“imply of mean_std_tensor: “, Imply) std_dev = mean_std_tensor.std() print(“customary deviation of mean_std_tensor: “, std_dev) |

imply of mean_std_tensor: tensor(0.) customary deviation of mean_std_tensor: tensor(1.8257) |

**Easy Addition and Multiplication Operations on One-Dimensional Tensors**

Addition and Multiplication operations will be simply utilized on tensors in Pytorch. On this part, we’ll create two one-dimensional tensors to show how these operations can be utilized.

a = torch.tensor([1, 1]) b = torch.tensor([2, 2])
add = a + b multiply = a * b
print(“addition of two tensors: “, add) print(“multiplication of two tensors: “, multiply) |

addition of two tensors: tensor([3, 3]) multiplication of two tensors: tensor([2, 2]) |

To your comfort, under is all of the examples above tying collectively so you’ll be able to attempt them in a single shot:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import torch import numpy as np import pandas as pd
int_to_tensor = torch.tensor([10, 11, 12, 13]) print(“Tensor object kind after conversion: “, int_to_tensor.dtype) print(“Tensor object kind after conversion: “, int_to_tensor.kind())
float_to_tensor = torch.tensor([10.0, 11.0, 12.0, 13.0]) print(“Tensor object kind after conversion: “, float_to_tensor.dtype) print(“Tensor object kind after conversion: “, float_to_tensor.kind())
int_list_to_float_tensor = torch.FloatTensor([10, 11, 12, 13]) int_list_to_float_tensor.kind() print(“Tensor kind after conversion: “, int_list_to_float_tensor.kind())
print(“Dimension of the int_list_to_float_tensor: “, int_list_to_float_tensor.dimension()) print(“Dimensions of the int_list_to_float_tensor: “,int_list_to_float_tensor.ndimension())
reshaped_tensor = int_list_to_float_tensor.view(4, 1) print(“Unique Dimension of the tensor: “, reshaped_tensor) print(“New dimension of the tensor: “, reshaped_tensor)
numpy_arr = np.array([10.0, 11.0, 12.0, 13.0]) from_numpy_to_tensor = torch.from_numpy(numpy_arr) print(“dtype of the tensor: “, from_numpy_to_tensor.dtype) print(“kind of the tensor: “, from_numpy_to_tensor.kind())
tensor_to_numpy = from_numpy_to_tensor.numpy() print(“again to numpy from tensor: “, tensor_to_numpy) print(“dtype of transformed numpy array: “, tensor_to_numpy.dtype)
pandas_series=pd.Collection([1, 0.2, 3, 13.1]) store_with_numpy=torch.from_numpy(pandas_series.values) print(“Saved tensor in numpy array: “, store_with_numpy) print(“dtype of saved tensor: “, store_with_numpy.dtype) print(“kind of saved tensor: “, store_with_numpy.kind())
new_tensor=torch.tensor([10, 11, 12, 13]) print(“the second merchandise is”,new_tensor[1].merchandise()) tensor_to_list=new_tensor.tolist() print(‘tensor:’, new_tensor,“nlist:”,tensor_to_list)
tensor_index = torch.tensor([0, 1, 2, 3]) print(“Test worth at index 0:”,tensor_index[0]) print(“Test worth at index 3:”,tensor_index[3])
example_tensor = torch.tensor([50, 11, 22, 33, 44]) sclicing_tensor = example_tensor[1:4] print(“instance tensor : “, example_tensor) print(“subset of instance tensor:”, sclicing_tensor)
print(“worth at index 3 of instance tensor:”, example_tensor[3]) example_tensor[3] = 0 print(“new tensor:”, example_tensor)
sample_tensor = torch.tensor([5, 4, 3, 2, 1]) min_value = sample_tensor.min() max_value = sample_tensor.max() print(“test minimal worth within the tensor: “, min_value) print(“test most worth within the tensor: “, max_value)
mean_std_tensor = torch.tensor([–1.0, 2.0, 1, –2]) Imply = mean_std_tensor.imply() print(“imply of mean_std_tensor: “, Imply) std_dev = mean_std_tensor.std() print(“customary deviation of mean_std_tensor: “, std_dev)
a = torch.tensor([1, 1]) b = torch.tensor([2, 2]) add = a + b multiply = a * b print(“addition of two tensors: “, add) print(“multiplication of two tensors: “, multiply) |

## Additional Studying

Developed concurrently TensorFlow, PyTorch used to have a less complicated syntax till TensorFlow adopted Keras in its 2.x model. To be taught the fundamentals of PyTorch, it’s possible you’ll need to learn the PyTorch tutorials:

Particularly the fundamentals of PyTorch tensor will be discovered within the Tensor tutorial web page:

There are additionally fairly just a few books on PyTorch which are appropriate for newcomers. A extra not too long ago revealed e-book needs to be really helpful because the instruments and syntax are actively evolving. One instance is

**Abstract**

On this tutorial, you’ve found how you can use one-dimensional tensors in Pytorch.

Particularly, you discovered:

- The fundamentals of one-dimensional tensor operations in PyTorch
- About tensor varieties and shapes and how you can carry out tensor slicing and indexing operations
- Tips on how to apply some strategies on tensor objects, equivalent to imply, customary deviation, addition, and multiplication