Downloaded 44 times
![Code Output
The two fundamental attributes of a tensor are:
Shape refers to the dimensionality of array or matrix
Rank refers to the number of dimensions present in tensor
# importing torch
import torch
# creating a tensors
t1=torch.tensor([1, 2, 3, 4])
t2=torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
# printing the tensors:
print("Tensor t1: n", t1)
print("nTensor t2: n", t2)
# rank of tensors
print("nRank of t1: ", len(t1.shape))
print("Rank of t2: ", len(t2.shape))
# shape of tensors
print("nRank of t1: ", t1.shape)
print("Rank of t2: ", t2.shape)
Tensor t1:
tensor ([1, 2, 3, 4])
Tensor t2:
tensor([[1, 2, 3, 4], [5,6,7,8], [9,10,11,12]])
Rank of t1: 1
Rank of t2: 2
Rank of t1: torch.Size([4])
Rank of t2: torch.Size([3,4])](https://image.slidesharecdn.com/group8divisionhpytorch-230221155649-a61693f1/85/PyTorch-Introduction-9-320.jpg)
![Code Output
# importing torch module
import torch
import numpy as np
# list of values to be stored as tensor
data1 = [1, 2, 3, 4, 5, 6]
data2 = np.array([1.5, 3.4, 6.8, 9.3, 7.0, 2.8])
# creating tensors and printing
t1 = torch.tensor(data1)
t2 = torch.Tensor(data1)
t3 = torch.as_tensor(data2)
t4 = torch.from_numpy(data2)
print("Tensor: ",t1, "Data type: ", t1.dtype,"n")
print("Tensor: ",t2, "Data type: ", t2.dtype,"n")
print("Tensor: ",t3, "Data type: ", t3.dtype,"n")
print("Tensor: ",t4, "Data type: ", t4.dtype,"n")
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, 2.80e0], dtype=torch.float64)
Data type: torch. float64
Tensor: tensor ([1.5000, 3.4000, 6.8000, 9.3000,
7.0000, 2.800e], dtype=torch.float64)
Data type: torch. float64](https://image.slidesharecdn.com/group8divisionhpytorch-230221155649-a61693f1/85/PyTorch-Introduction-11-320.jpg)
![Code Output
# import torch module
import torch
# defining tensor
t = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
# reshaping the tensor
print("Reshaping")
print(t.reshape(6, 2))
# resizing the tensor
print("nResizing")
print(t.resize(2, 6))
# transposing the tensor
print("nTransposing")
print(t.transpose(1, 0))
Reshaping
tensor([[ 1, 2], [ 3, 4], [ 5, 6], [ 7, 8], [ 9, 10], [11, 12]])
Resizing
tensor ([[ 1, 2, 3, 4, 5, 6], [ 7, 8, 9, 10, 11, 12]])
Transposing
tensor([[1, 5, 9], [ 2, 6, 10], [ 3, 7, 11], [ 4, 8, 12]])](https://image.slidesharecdn.com/group8divisionhpytorch-230221155649-a61693f1/85/PyTorch-Introduction-14-320.jpg)
![MATHEMATICAL OPERATIONS ON
TENSORS USING PYTORCH
We can perform various mathematical operations on tensors using Pytorch. The
code for performing Mathematical operations is the same as in the case with
NumPy arrays. Below is the code for performing the four basic operations in
tensors.
OUTPUT
CODE
# import torch module
import torch
# defining two tensors
t1 = torch.tensor([1, 2, 3, 4])
t2 = torch.tensor([5, 6, 7, 8])
# adding two tensors
print("tensor2 + tensor1")
print(torch.add(t2, t1))
# subtracting two tensor
print("ntensor2 - tensor1")
print(torch.sub(t2, t1))
# multiplying two tensors
print("ntensor2 * tensor1")
print(torch.mul(t2, t1))
# diving two tensors
print("ntensor2 / tensor1")
print(torch.div(t2, t1))
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, 2.80e0], dtype=torch.float64)
Data type: torch. float64
Tensor: tensor ([1.5000, 3.4000, 6.8000, 9.3000,
7.0000, 2.800e], dtype=torch.float64)
Data type: torch. float64](https://image.slidesharecdn.com/group8divisionhpytorch-230221155649-a61693f1/85/PyTorch-Introduction-15-320.jpg)
![Code Output
The two fundamental attributes of a tensor are:
Shape refers to the dimensionality of array or matrix
Rank refers to the number of dimensions present in tensor
# importing torch
import torch
# creating a tensors
t1=torch.tensor([1, 2, 3, 4])
t2=torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
# printing the tensors:
print("Tensor t1: n", t1)
print("nTensor t2: n", t2)
# rank of tensors
print("nRank of t1: ", len(t1.shape))
print("Rank of t2: ", len(t2.shape))
# shape of tensors
print("nRank of t1: ", t1.shape)
print("Rank of t2: ", t2.shape)
Tensor t1:
tensor ([1, 2, 3, 4])
Tensor t2:
tensor([[1, 2, 3, 4], [5,6,7,8], [9,10,11,12]])
Rank of t1: 1
Rank of t2: 2
Rank of t1: torch.Size([4])
Rank of t2: torch.Size([3,4])](https://image.slidesharecdn.com/group8divisionhpytorch-230221155649-a61693f1/75/PyTorch-Introduction-9-2048.jpg)
![Code Output
# importing torch module
import torch
import numpy as np
# list of values to be stored as tensor
data1 = [1, 2, 3, 4, 5, 6]
data2 = np.array([1.5, 3.4, 6.8, 9.3, 7.0, 2.8])
# creating tensors and printing
t1 = torch.tensor(data1)
t2 = torch.Tensor(data1)
t3 = torch.as_tensor(data2)
t4 = torch.from_numpy(data2)
print("Tensor: ",t1, "Data type: ", t1.dtype,"n")
print("Tensor: ",t2, "Data type: ", t2.dtype,"n")
print("Tensor: ",t3, "Data type: ", t3.dtype,"n")
print("Tensor: ",t4, "Data type: ", t4.dtype,"n")
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, 2.80e0], dtype=torch.float64)
Data type: torch. float64
Tensor: tensor ([1.5000, 3.4000, 6.8000, 9.3000,
7.0000, 2.800e], dtype=torch.float64)
Data type: torch. float64](https://image.slidesharecdn.com/group8divisionhpytorch-230221155649-a61693f1/75/PyTorch-Introduction-11-2048.jpg)
![Code Output
# import torch module
import torch
# defining tensor
t = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
# reshaping the tensor
print("Reshaping")
print(t.reshape(6, 2))
# resizing the tensor
print("nResizing")
print(t.resize(2, 6))
# transposing the tensor
print("nTransposing")
print(t.transpose(1, 0))
Reshaping
tensor([[ 1, 2], [ 3, 4], [ 5, 6], [ 7, 8], [ 9, 10], [11, 12]])
Resizing
tensor ([[ 1, 2, 3, 4, 5, 6], [ 7, 8, 9, 10, 11, 12]])
Transposing
tensor([[1, 5, 9], [ 2, 6, 10], [ 3, 7, 11], [ 4, 8, 12]])](https://image.slidesharecdn.com/group8divisionhpytorch-230221155649-a61693f1/75/PyTorch-Introduction-14-2048.jpg)
![MATHEMATICAL OPERATIONS ON
TENSORS USING PYTORCH
We can perform various mathematical operations on tensors using Pytorch. The
code for performing Mathematical operations is the same as in the case with
NumPy arrays. Below is the code for performing the four basic operations in
tensors.
OUTPUT
CODE
# import torch module
import torch
# defining two tensors
t1 = torch.tensor([1, 2, 3, 4])
t2 = torch.tensor([5, 6, 7, 8])
# adding two tensors
print("tensor2 + tensor1")
print(torch.add(t2, t1))
# subtracting two tensor
print("ntensor2 - tensor1")
print(torch.sub(t2, t1))
# multiplying two tensors
print("ntensor2 * tensor1")
print(torch.mul(t2, t1))
# diving two tensors
print("ntensor2 / tensor1")
print(torch.div(t2, t1))
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, 2.80e0], dtype=torch.float64)
Data type: torch. float64
Tensor: tensor ([1.5000, 3.4000, 6.8000, 9.3000,
7.0000, 2.800e], dtype=torch.float64)
Data type: torch. float64](https://image.slidesharecdn.com/group8divisionhpytorch-230221155649-a61693f1/75/PyTorch-Introduction-15-2048.jpg)
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PyTorch Introduction
- 1. 43 Tanmay Borase46 Aryan Kshatriya 44 Yash Kawdiya 47 Rajveersingh Naruka 45 Harshal Kolhe 48 Rugved Salunke DY PATIL COLLEGE OF ENGINEERING FIRST YEAR ENGINEERING DEPARTMENT THE ACADEMIC YEAR 2022-23 SEMESTER 1 Presenters Subject Teacher : Swati Suryawanshi Mam
- 2. PYTORCH INTRODUCTION Group 8 |Division H
- 3. CONTENT OVERVIEW 1 / Whatis PyTorch 2 / Why PyTorch 3 / The PyTorch Explained 4 / The PyTorch Tensors 5 / Creating Tensors in PyTorch 6 / Restructuring Tensors in PyTorch 7 / Mathematical Operations on Tensors in PyTorch
- 4. CONTENT OVERVIEW 8 / PyTorchModules 9 / PyTorch Vs TensorFlow 10 / Key Features of Using PyTorch 11 / PyTorch Benefits 12 / Members and Governing Board 13 / PyTorch Use Cases
- 5. WHAT IS PYTORCH PyTorch isan open-source deep learning framework that’s known for its flexibility and ease-of-use. This is enabled in part by its compatibility with the popular Python high-level programming language favored by machine learning developers and data scientists.
- 6. WHY PYTORCH Image referencehttps://pytorch.org/features/
- 7. THE PYTORCH EXPLAINED PyTorch is afully featured framework for building deep learning models, which is a type of machine learning that’s commonly used in applications like image recognition and language processing.
- 8. PYTORCH TENSORS The Pytorchis used to process the tensors. Tensors are multidimensional arrays like n-dimensional NumPy array. However, tensors can be used in GPUs as well, which is not in the case of NumPy array. PyTorch accelerates the scientific computation of tensors as it has various inbuilt functions. A vector is a one-dimensional tensor, and a matrix is a two-dimensional tensor. One significant difference between the Tensor and multidimensional array used in C, C++, and Java is tensors should have the same size of columns in all dimensions. Also, the tensors can contain only numeric data types. 1.0 4.0 7.0 2.0 5.0 8.0 3.0 6.0 9.0 1.0 2.0 3.0 VECTOR 1 D TENSOR MATRIX 2 D TENSOR
- 9. Code Output The twofundamental attributes of a tensor are: Shape refers to the dimensionality of array or matrix Rank refers to the number of dimensions present in tensor # importing torch import torch # creating a tensors t1=torch.tensor([1, 2, 3, 4]) t2=torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) # printing the tensors: print("Tensor t1: n", t1) print("nTensor t2: n", t2) # rank of tensors print("nRank of t1: ", len(t1.shape)) print("Rank of t2: ", len(t2.shape)) # shape of tensors print("nRank of t1: ", t1.shape) print("Rank of t2: ", t2.shape) Tensor t1: tensor ([1, 2, 3, 4]) Tensor t2: tensor([[1, 2, 3, 4], [5,6,7,8], [9,10,11,12]]) Rank of t1: 1 Rank of t2: 2 Rank of t1: torch.Size([4]) Rank of t2: torch.Size([3,4])
- 10. CREATING TENSOR INPYTORCH 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.
- 11. Code Output # importingtorch module import torch import numpy as np # list of values to be stored as tensor data1 = [1, 2, 3, 4, 5, 6] data2 = np.array([1.5, 3.4, 6.8, 9.3, 7.0, 2.8]) # creating tensors and printing t1 = torch.tensor(data1) t2 = torch.Tensor(data1) t3 = torch.as_tensor(data2) t4 = torch.from_numpy(data2) print("Tensor: ",t1, "Data type: ", t1.dtype,"n") print("Tensor: ",t2, "Data type: ", t2.dtype,"n") print("Tensor: ",t3, "Data type: ", t3.dtype,"n") print("Tensor: ",t4, "Data type: ", t4.dtype,"n") 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, 2.80e0], dtype=torch.float64) Data type: torch. float64 Tensor: tensor ([1.5000, 3.4000, 6.8000, 9.3000, 7.0000, 2.800e], dtype=torch.float64) Data type: torch. float64
- 12. RESTRUCTURING TENSORS INPYTORCH We can modify the shape and size of a tensor as desired in PyTorch. We can also create a transpose of an n-d tensor. Below are three common ways to change the structure of your tensor as desired: .reshape(a, b) returns a new tensor with size a,b .resize(a, b) returns the same tensor with the size a,b .transpose(a, b) returns a tensor transposed in a and b dimension
- 13. 1 4 2 5 36 1 2 3 4 5 6 1 2 3 4 5 6 Reshape Transpose Original Reshape and Transpose Difference A 2*3 matrix has been reshaped and transposed to 3*2. We can visualize the change in the arrangement of the elements in the tensor in both cases.
- 14. Code Output # importtorch module import torch # defining tensor t = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]) # reshaping the tensor print("Reshaping") print(t.reshape(6, 2)) # resizing the tensor print("nResizing") print(t.resize(2, 6)) # transposing the tensor print("nTransposing") print(t.transpose(1, 0)) Reshaping tensor([[ 1, 2], [ 3, 4], [ 5, 6], [ 7, 8], [ 9, 10], [11, 12]]) Resizing tensor ([[ 1, 2, 3, 4, 5, 6], [ 7, 8, 9, 10, 11, 12]]) Transposing tensor([[1, 5, 9], [ 2, 6, 10], [ 3, 7, 11], [ 4, 8, 12]])
- 15. MATHEMATICAL OPERATIONS ON TENSORSUSING PYTORCH We can perform various mathematical operations on tensors using Pytorch. The code for performing Mathematical operations is the same as in the case with NumPy arrays. Below is the code for performing the four basic operations in tensors. OUTPUT CODE # import torch module import torch # defining two tensors t1 = torch.tensor([1, 2, 3, 4]) t2 = torch.tensor([5, 6, 7, 8]) # adding two tensors print("tensor2 + tensor1") print(torch.add(t2, t1)) # subtracting two tensor print("ntensor2 - tensor1") print(torch.sub(t2, t1)) # multiplying two tensors print("ntensor2 * tensor1") print(torch.mul(t2, t1)) # diving two tensors print("ntensor2 / tensor1") print(torch.div(t2, t1)) 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, 2.80e0], dtype=torch.float64) Data type: torch. float64 Tensor: tensor ([1.5000, 3.4000, 6.8000, 9.3000, 7.0000, 2.800e], dtype=torch.float64) Data type: torch. float64
- 16. PYTORCH MODULES The PyTorchlibrary modules are essential to create and train neural networks. The three main library modules are Autograd, Optim, and nn. # 1. Autograd Module The autograd provides the functionality of easy calculation of gradients without the explicitly manual implementation of forward and backward pass for all layers. For training any neural network we perform backpropagation to calculate the gradient. By calling the .backward() function we can calculate every gradient from the root to the leaf. # 2. Optim Module PyTorch Optium Module which helps in the implementation of various optimization algorithms. This package contains the most commonly used algorithms like Adam, SGD, and RMS-Prop. To use torch.optim we first need to construct an Optimizer object which will keep the parameters and update it accordingly. First, we define the Optimizer by providing the optimizer algorithm we want to use. We set the gradients to zero before backpropagation. Then for updation of parameters the optimizer.step() is called.
- 17. PYTORCH MODULES # 3.nn Module This package helps in the construction of neural networks. It is used to build layers. For creating a model with a single layer we can simply define it by using nn.Sequential(). For the implementation of the model which is not in a single sequence, we define a model by subclassing the nn.Module class.
- 18. PyTorch TensorFlow PyTorch isclosely related to the lua-based Torch framework which is actively used in Facebook. TensorFlow is developed by Google Brain and actively used at Google. PyTorch is relatively new compared to other competitive technologies. TensorFlow is not new and is considered as a to-go tool by many researchers and industry professionals. PyTorch includes everything in imperative and dynamic manner. TensorFlow includes static and dynamic graphs as a combination. Computation graph in PyTorch is defined during runtime. TensorFlow do not include any run time option. PYTORCH VS TENSORFLOW
- 19. KEY FEATURES OFUSING PYTORCH TENSOR COMPUTATION TORCHSCRIPT AUTOMATIC DIFFERENTIATION Tensors are generic n- dimensional arrays used for arbitrary numeric computation and are accelerated by graphics processing units. These multidimensional structures can be operated on and manipulated with application program interfaces (APIs). This is the production environment of PyTorch that enables users to seamlessly transition between modes. TorchScript optimizes functionality, speed, ease of use and flexibility. This technique is used for creating and training neural networks. It numerically computes the derivative of a function by making backward passes in neural networks.
- 20. KEY FEATURES OFUSING PYTORCH DYNAMIC GRAPH COMPUTATION PYTHON SUPPORT This feature lets users change network behavior on the fly, rather than waiting for all the code to be executed. Because PyTorch is based on Python, it can be used with popular libraries and packages such as NumPy, SciPy, Numba and Cynthon.
- 21. PYTORCH BENIFITS Offers developers aneasy-to-learn, simple-to-code structure that's based on Python. Enables easy debugging with popular Python tools. Offers scalability and is well-supported on major cloud platforms. Provides a small community focused on open source. Exports learning models to the Open Neural Network Exchange (ONNX) standard format. Offers a user-friendly interface. Provides a C++ front-end interface option. Includes a rich set of powerful APIs that extend the PyTorch library.
- 22.
- 23. PYTORCH USE CASES Natural LanguageProcessing (NLP): From Siri to Google Translate, deep neural networks have enabled breakthroughs in machine understanding of natural language. Most of these models treat language as a flat sequence of words or characters and use a kind of model called a recurrent neural network (RNN) to process this sequence. But many linguists think that language is best understood as a hierarchical tree of phrases, so a significant amount of research has gone into deep learning models known as recursive neural networks that take this structure into account. While these models are notoriously hard to implement and inefficient to run, PyTorch makes these and other complex natural language processing models a lot easier. Salesforce is using PyTorch for NLP and multi-task learning.
- 24. PYTORCH USE CASES Research: PyTorchis a popular choice for research due to its ease of use, flexibility and rapid prototyping. Stanford University is using PyTorch’s flexibility to efficiently research new algorithmic approaches. Education: Udacity is educating AI innovators using PyTorch.
- 25.