Final Up to date on November 15, 2022
PyTorch is an open-source deep studying framework primarily based on Python language. It means that you can construct, prepare, and deploy deep studying fashions, providing plenty of versatility and effectivity.
PyTorch is primarily targeted on tensor operations whereas a tensor could be a quantity, matrix, or a multi-dimensional array.
On this tutorial, we’ll carry out some fundamental operations on one-dimensional tensors as they’re advanced mathematical objects and a vital a part of the PyTorch library. Due to this fact, earlier than going into the element and extra superior ideas, one ought to know the fundamentals.
After going via this tutorial, you’ll:
- Perceive the fundamentals of one-dimensional tensor operations in PyTorch.
- Learn about tensor sorts and shapes and carry out tensor slicing and indexing operations.
- Be capable to apply some strategies on tensor objects, resembling imply, commonplace deviation, addition, multiplication, and extra.
Let’s get began.
One-Dimensional Tensors in Pytorch
Image by Jo Szczepanska. Some rights reserved.
Varieties and Shapes of One-Dimensional Tensors
First off, let’s import a couple of libraries we’ll use on this tutorial.
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import torch import numpy as np import pandas as pd |
When you have expertise in different programming languages, the best strategy to perceive a tensor is to think about it as a multidimensional array. Due to this fact, a one-dimensional tensor is just a one-dimensional array, or a vector. As a way to convert an inventory of integers to tensor, apply torch.tensor() constructor. As an example, we’ll take an inventory of integers and convert it to varied tensor objects.
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int_to_tensor = torch.tensor([10, 11, 12, 13]) print(“Tensor object sort after conversion: “, int_to_tensor.dtype) print(“Tensor object sort after conversion: “, int_to_tensor.sort()) |
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Tensor object sort after conversion: torch.int64 Tensor object sort after conversion: torch.LongTensor |
Additionally, you’ll be able to apply the identical methodology torch.tensor() to transform a float listing to a float tensor.
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float_to_tensor = torch.tensor([10.0, 11.0, 12.0, 13.0]) print(“Tensor object sort after conversion: “, float_to_tensor.dtype) print(“Tensor object sort after conversion: “, float_to_tensor.sort()) |
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Tensor object sort after conversion: torch.float32 Tensor object sort after conversion: torch.FloatTensor |
Notice that parts of an inventory that must be transformed right into a tensor should have the identical sort. Furthermore, if you wish to convert an inventory to a sure tensor sort, torch additionally means that you can try this. The code strains beneath, for instance, will convert an inventory of integers to a float tensor.
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int_list_to_float_tensor = torch.FloatTensor([10, 11, 12, 13]) int_list_to_float_tensor.sort() print(“Tensor sort after conversion: “, int_list_to_float_tensor.sort()) |
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Tensor sort after conversion: torch.FloatTensor |
Equally, dimension() and ndimension() strategies help you discover the scale and dimensions of a tensor object.
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print(“Measurement 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()) |
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Measurement of the int_list_to_float_tensor: torch.Measurement([4]) Dimensions of the int_list_to_float_tensor: 1 |
For reshaping a tensor object, view() methodology may be utilized. It takes rows and columns as arguments. For instance, let’s use this methodology to reshape int_list_to_float_tensor.
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reshaped_tensor = int_list_to_float_tensor.view(4, 1) print(“Authentic Measurement of the tensor: “, reshaped_tensor) print(“New dimension of the tensor: “, reshaped_tensor) |
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Authentic Measurement 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() methodology has modified the scale of the tensor to torch.Measurement([4, 1]), with 4 rows and 1 column.
Whereas the variety of parts in a tensor object ought to stay fixed after view() methodology is utilized, you need to use -1 (resembling reshaped_tensor.view(-1, 1)) to reshape a dynamic-sized tensor.
Changing Numpy Arrays to Tensors
Pytorch additionally means that you can convert NumPy arrays to tensors. You should use torch.from_numpy for this operation. Let’s take a NumPy array and apply the operation.
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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(“sort of the tensor: “, from_numpy_to_tensor.sort()) |
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dtype of the tensor: torch.float64 sort 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 indicate the way it’s accomplished.
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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) |
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again to numpy from tensor: [10. 11. 12. 13.] dtype of transformed numpy array: float64 |
Changing Pandas Sequence to Tensors
You can even convert a pandas collection to a tensor. For this, first you’ll have to retailer the pandas collection with values() perform utilizing a NumPy array.
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pandas_series=pd.Sequence([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(“sort of saved tensor: “, store_with_numpy.sort()) |
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Saved tensor in numpy array: tensor([ 1.0000, 0.2000, 3.0000, 13.1000], dtype=torch.float64) dtype of saved tensor: torch.float64 sort of saved tensor: torch.DoubleTensor |
Moreover, the Pytorch framework permits us to do so much with tensors resembling its merchandise() methodology returns a python quantity from a tensor and tolist() methodology returns an inventory.
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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) |
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the second merchandise is 11 tensor: tensor([10, 11, 12, 13]) listing: [10, 11, 12, 13] |
Indexing and Slicing in One-Dimensional Tensors
Indexing and slicing operations are nearly the identical in Pytorch as python. Due to this fact, the primary index all the time begins at 0 and the final index is lower than the entire size of the tensor. Use sq. brackets to entry any quantity in a tensor.
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tensor_index = torch.tensor([0, 1, 2, 3]) print(“Examine worth at index 0:”,tensor_index[0]) print(“Examine worth at index 3:”,tensor_index[3]) |
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Examine worth at index 0: tensor(0) Examine worth at index 3: tensor(3) |
Like an inventory in python, you can too carry out slicing operations on the values in a tensor. Furthermore, the Pytorch library means that you can change sure values in a tensor as effectively.
Let’s take an instance to verify how these operations may be utilized.
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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) |
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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:
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print(“worth at index 3 of instance tensor:”, example_tensor[3]) example_tensor[3] = 0 print(“new tensor:”, example_tensor) |
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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 overview 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.
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sample_tensor = torch.tensor([5, 4, 3, 2, 1]) min_value = sample_tensor.min() max_value = sample_tensor.max() print(“verify minimal worth within the tensor: “, min_value) print(“verify most worth within the tensor: “, max_value) |
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verify minimal worth within the tensor: tensor(1) verify most worth within the tensor: tensor(5) |
Imply and Normal Deviation
Imply and commonplace deviation are sometimes used whereas doing statistical operations on tensors. You may apply these two metrics utilizing .imply() and .std() capabilities in Pytorch.
Let’s use an instance to see how these two metrics are calculated.
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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(“commonplace deviation of mean_std_tensor: “, std_dev) |
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imply of mean_std_tensor: tensor(0.) commonplace deviation of mean_std_tensor: tensor(1.8257) |
Easy Addition and Multiplication Operations on One-Dimensional Tensors
Addition and Multiplication operations may be simply utilized on tensors in Pytorch. On this part, we’ll create two one-dimensional tensors to reveal how these operations can be utilized.
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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) |
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addition of two tensors: tensor([3, 3]) multiplication of two tensors: tensor([2, 2]) |
To your comfort, beneath is all of the examples above tying collectively so you’ll be able to strive them in a single shot:
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import torch import numpy as np import pandas as pd
int_to_tensor = torch.tensor([10, 11, 12, 13]) print(“Tensor object sort after conversion: “, int_to_tensor.dtype) print(“Tensor object sort after conversion: “, int_to_tensor.sort())
float_to_tensor = torch.tensor([10.0, 11.0, 12.0, 13.0]) print(“Tensor object sort after conversion: “, float_to_tensor.dtype) print(“Tensor object sort after conversion: “, float_to_tensor.sort())
int_list_to_float_tensor = torch.FloatTensor([10, 11, 12, 13]) int_list_to_float_tensor.sort() print(“Tensor sort after conversion: “, int_list_to_float_tensor.sort())
print(“Measurement 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(“Authentic Measurement 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(“sort of the tensor: “, from_numpy_to_tensor.sort())
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.Sequence([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(“sort of saved tensor: “, store_with_numpy.sort())
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(“Examine worth at index 0:”,tensor_index[0]) print(“Examine 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(“verify minimal worth within the tensor: “, min_value) print(“verify 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(“commonplace 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 an easier syntax till TensorFlow adopted Keras in its 2.x model. To study the fundamentals of PyTorch, you might need to learn the PyTorch tutorials:
Particularly the fundamentals of PyTorch tensor may be discovered within the Tensor tutorial web page:
There are additionally fairly a couple of books on PyTorch which can be appropriate for freshmen. A extra not too long ago revealed guide 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 sorts and shapes and how you can carry out tensor slicing and indexing operations
- How one can apply some strategies on tensor objects, resembling imply, commonplace deviation, addition, and multiplication
