Array to tensor pytorch

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Array to tensor pytorch

In this article, we will see be covering the following topics:What is the difference between numpy array and pytorch tensor?How to create numpy arrays and pytorch tensors. Furthermore, we will see how to perform the same operation on both data types.This blog is also part of assignment 1 of this awesome course ZerotoGans. I recommend everybody to check out this course who are starting with the pytorch.What is the difference between numpy array and pytorch tensor?The numpy arrays are the core functionality of the numpy package designed to support faster mathematical operations. Unlike python's inbuilt list data structure, they can only hold elements of a single data type. Library like pandas which is used for data preprocessing is built around the numpy array. Pytorch tensors are similar to numpy arrays, but can also be operated on CUDA-capable Nvidia GPU.Numpy arrays are mainly used in typical machine learning algorithms (such as k-means or Decision Tree in scikit-learn) whereas pytorch tensors are mainly used in deep learning which requires heavy matrix computation.Unlike numpy arrays, while creating pytorch tensor, it also accepts two other arguments called the device_type (whether the computation happens on CPU or GPU) and the requires_grad (which is used to compute the derivatives).While converting tensor to numpy note that the underlying storage remains the same as depicted belowRand functionThe rand function is used to create random samples from a uniform distribution on the interval [0,1]. The first argument is the size of the desired array. Below example create 2-d array of dimension 2*3 (2 rows and 3 columns)Seed functionUsing he seed function to ensure the reproducibility.Reshaping the arrayReshaping means changing the underlying size of the array. Specifying -1 in the reshape method tells the numpy or the pytorch to automatically infer that. This can be specified only once.In the case of pytorch tensorNote that there are two functions in pytorch to reshape the array. One is permute which basically permutes the dimensions without changing the data ordering and other is reshape which just changes the size to the desired size and so the ordering of elements get changed.As you can see below the reshape method has changed the order of elements.Slicing the arraysSlicing works the same way in both numpy array and pytorch tensorAdd a new dimensionTo add a new dimension in numpy array we use expand_dims.To expand dimension in pytorch we have to use unsqueeze method.I hope you like this article. Please let me know in comments if you found any issues.References Tensors are a specialized data structure that are very similar to arrays and matrices. In PyTorch, we use tensors to encode the inputs and outputs of a model, as well as the model's parameters. Tensors are similar to NumPy's ndarrays, except that tensors can run on GPUs or other specialized hardware to accelerate computing. If you're familiar with ndarrays, you'll be right at home with the Tensor API. If not, follow along in this quick API walkthrough. import torch import numpy as np Tensors can be initialized in various ways. Take a look at the following examples: Directly from data Tensors can be created directly from data. The data type is automatically inferred. data = [[1, 2],[3, 4]] x_data = torch.tensor(data) From a NumPy array Tensors can be created from NumPy arrays (and vice versa - see Bridge with NumPy). np_array = np.array(data) x_np = torch.from_numpy(np_array) From another tensor: The new tensor retains the properties (shape, datatype) of the argument tensor, unless explicitly overridden. x_ones = torch.ones_like(x_data) # retains the properties of x_data print(f"Ones Tensor: {x_ones} ") x_rand = torch.rand_like(x_data, dtype=torch.float) # overrides the datatype of x_data print(f"Random Tensor: {x_rand} ") Out: Ones Tensor: tensor([[1, 1], [1, 1]]) Random Tensor: tensor([[0.5339, 0.3113], [0.2810, 0.7914]]) With random or constant values: shape is a tuple of tensor dimensions. In the functions below, it determines the dimensionality of the output tensor. shape = (2,3,) rand_tensor = torch.rand(shape) ones_tensor = torch.ones(shape) zeros_tensor = torch.zeros(shape) print(f"Random Tensor: {rand_tensor} ") print(f"Ones Tensor: {ones_tensor} ") print(f"Zeros Tensor: {zeros_tensor}") Out: Random Tensor: tensor([[0.0410, 0.6006, 0.4993], [0.1657, 0.6892, 0.0666]]) Ones Tensor: tensor([[1., 1., 1.], [1., 1., 1.]]) Zeros Tensor: tensor([[0., 0., 0.], [0., 0., 0.]]) Tensor attributes describe their shape, datatype, and the device on which they are stored. tensor = torch.rand(3,4) print(f"Shape of tensor: {tensor.shape}") print(f"Datatype of tensor: {tensor.dtype}") print(f"Device tensor is stored on: {tensor.device}") Out: Shape of tensor: torch.Size([3, 4]) Datatype of tensor: torch.float32 Device tensor is stored on: cpu Over 100 tensor operations, including transposing, indexing, slicing, mathematical operations, linear algebra, random sampling, and more are comprehensively described here. Each of them can be run on the GPU (at typically higher speeds than on a CPU). If you're using Colab, allocate a GPU by going to Edit > Notebook Settings. # We move our tensor to the GPU if available if torch.cuda.is_available(): tensor = tensor.to('cuda') Try out some of the operations from the list. If you're familiar with the NumPy API, you'll find the Tensor API a breeze to use. Standard numpy-like indexing and slicing: tensor = torch.ones(4, 4) tensor[:,1] = 0 print(tensor) Out: tensor([[1., 0., 1., 1.], [1., 0., 1., 1.], [1., 0., 1., 1.], [1., 0., 1., 1.]]) Joining tensors You can use torch.cat to concatenate a sequence of tensors along a given dimension. See also torch.stack, another tensor joining op that is subtly different from torch.cat. t1 = torch.cat([tensor, tensor, tensor], dim=1) print(t1) Out: tensor([[1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.], [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.], [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.], [1., 0., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1.]]) Multiplying tensors # This computes the element-wise product print(f"tensor.mul(tensor) {tensor.mul(tensor)} ") # Alternative syntax: print(f"tensor * tensor {tensor * tensor}") Out: tensor.mul(tensor) tensor([[1., 0., 1., 1.], [1., 0., 1., 1.], [1., 0., 1., 1.], [1., 0., 1., 1.]]) tensor * tensor tensor([[1., 0., 1., 1.], [1., 0., 1., 1.], [1., 0., 1., 1.], [1., 0., 1., 1.]]) This computes the matrix multiplication between two tensors print(f"tensor.matmul(tensor.T) {tensor.matmul(tensor.T)} ") # Alternative syntax: print(f"tensor @ tensor.T {tensor @ tensor.T}") Out: tensor.matmul(tensor.T) tensor([[3., 3., 3., 3.], [3., 3., 3., 3.], [3., 3., 3., 3.], [3., 3., 3., 3.]]) tensor @ tensor.T tensor([[3., 3., 3., 3.], [3., 3., 3., 3.], [3., 3., 3., 3.], [3., 3., 3., 3.]]) In-place operations Operations that have a _ suffix are in-place. For example: x.copy_(y), x.t_(), will change x. print(tensor, "") tensor.add_(5) print(tensor) Out: tensor([[1., 0., 1., 1.], [1., 0., 1., 1.], [1., 0., 1., 1.], [1., 0., 1., 1.]]) tensor([[6., 5., 6., 6.], [6., 5., 6., 6.], [6., 5., 6., 6.], [6., 5., 6., 6.]]) Note In-place operations save some memory, but can be problematic when computing derivatives because of an immediate loss of history. Hence, their use is discouraged. Tensors on the CPU and NumPy arrays can share their underlying memory locations, and changing one will change the other. t = torch.ones(5) print(f"t: {t}") n = t.numpy() print(f"n: {n}") Out: t: tensor([1., 1., 1., 1., 1.]) n: [1. 1. 1. 1. 1.] A change in the tensor reflects in the NumPy array. t.add_(1) print(f"t: {t}") print(f"n: {n}") Out: t: tensor([2., 2., 2., 2., 2.]) n: [2. 2. 2. 2. 2.] n = np.ones(5) t = torch.from_numpy(n) Changes in the NumPy array reflects in the tensor. np.add(n, 1, out=n) print(f"t: {t}") print(f"n: {n}") Out: t: tensor([2., 2., 2., 2., 2.], dtype=torch.float64) n: [2. 2. 2. 2. 2.] Total running time of the script: ( 0 minutes 2.372 seconds) Gallery generated by Sphinx-Gallery ? Copyright 2021, PyTorch. Built with Sphinx using a theme provided by Read the Docs. "How many Marks did you get in your data science class?" "10 out of tensor" | Photo by Ales Nesetril on UnsplashPyTorch proficiency is one of the most sought after skill when it comes to recruitment for data scientists. For those who don't know, PyTorch is a Python library with a wide variety of functions and operations, mostly used for deep learning. One of the most basic yet important parts of PyTorch is the ability to create Tensors. A tensor is a number, vector, matrix, or any n-dimensional array.Now the question might be, `why not use numpy arrays instead?'For Deep Learning, wewould need to compute the derivative of elements of the data. PyTorch provides the ability to compute derivatives automatically, which NumPy does not. This is called `Auto Grad.' PyTorch also provides built in support for fast execution using GPU. This is essential when it comes to training models.All Deep Learning projects using PyTorch start with creating a tensor. Let's see a few MUST HAVE functions which are the backbone of any Deep Learning project.torch.tensor()torch.from_numpy()torch.unbind()torch.where()torch.trapz()Before we begin, let's install and import PyTorchFunction 1 -- torch.tensorCreates a new tensor. Arguments taken are :Data: The actual data to be stored in the tensor.dtype: Type of data. Note that type of all the elements of a tensor must be the same.device: To tell if GPU or CPU should be used.requires_grad: If you want the tensor to be differentiable (to compute gradient), set to True.Returns a tensor object.Example 1 (Working Example) :This creates a tensor with shape (10,2)Example 2 (Working Example) :We can also create an empty tensor using the same function.As we can see, an empty tensor with size (0) is created.Example 3 (Error) :A tensor with string values cannot be created.Summary :torch.tensor() forms the core of any PyTorch project, quite literally, as it forms tensors.For those who work with NumPy Arrays, this is a vital function. Using this, one can convert a NumPy Array into a Tensor. It is to be noted that as the tensor and the NumPy Array share the same memory, any changes made to the tensor would be applicable to the NumPy Array and vice versa. The arguments taken are :Returns a tensor.Example 1 (Working Example) :A NumPy array has been converted into a PyTorch Tensor. We can verify that by checking the type of both a1 and t1 as shown above.Example 2 (Working Example) :We first created two NumPy Arrays which contained (height, weight) and (heart rate) of 3 students. We created a tuple of both the variables. Then using a for loop, we converted the elements of the tuple to a tensor one by one. In the end, we checked the type of the elements to verify if the function has worked.Example 3 (Error) :A NumPy array containing string elements cannot be converted to a tensor. The only supported types for a tensor are : float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.Summary :This function is extremely useful for people who are using NumPy Arrays for their projects. It shows the interoperability of different Python libraries for data science which makes the language a go to for the majority of data science enthusiasts.This function removes a dimension from a tensor and returns a tuple of slices of the tensor without the removed dimension. Arguments it takes are :A tensorDimension to be removed (0 by default)Returns a tuple of slices.Example 1(Working Example) :The 0th dimension from (0,1) is removed thus we get 3 slices of the tensor in a tupleExample 2 (Working Example) :As seen above, now the data is sliced into data of 10 students for each day and stored in a tuple of tensors, that is, 7 different tensors are created, each corresponding to a day of the week. This sliced data can be used to further apply logic based on that particular day of the week.Example 3(Error) :We had created a tensor with 2 dimensions. We provided a value of 2 to the tensor.unbind() function. As 2 corresponds to the 3rd dimension, which doesn't exist in our example, the error 'Dimension out of range' is produced.Summary:This is a powerful PyTorch function that could be useful when you want to work on particular slices of the data along a dimension of the tensor.This is a really useful conditional function which depending on a condition, a returns a tensor of elements selected from either x or y tensor. Arguments required are :A conditionx: Elements from this tensor are selected for indices where Condition is Truey: Elements from this tensor are selected for indices where Condition is FalseExample 1(Working Example) :Here we gave a condition where the elements from X which were positive, got selected. The elements which were not positive were replaced with elements from YExample 2 (Working Example):Here we compared head to head scores and got a tensor containing scores of the winners.Example 3(Working Example):It is vital for dimensions and/or size of both the tensors x and y to match for comparison. Otherwise, we get the above error.Summary :Conditional testing is very important for any data analysis. We might need a particular type of data from two sets of data based on a certain criteria. torch.where() is a useful function for such scenarios.This function estimates the definite integral of y with respect to x along the given dimension, based on 'Trapezoidal rule'.The arguments required are :y : A tensor containing values of the function to integrate. (blue line in the illustration below)x : The points at which the function y is sampled. (x axis in the illustration below)Dimension for integrationReturns a tensor of the same shape as the input (except the dimension removed) where each elements represents the estimated integral along the given dimension.Example 1 (Working Example) :Here we have the estimated definite integration of y with respect to x.Example 2 (Working Example) :We can also provide a dx argument where sample points are spaced uniformly at a distance of dx.Example 3 (Error) :As we can see, the dimensions and size of both y and x should match, otherwise our code will produce the above error.Summary:Deep Learning requires a lot of calculus to know more about the models that are being used. torch.trapz() makes our job of finding estimated integral easy.This concludes our look at 5 important PyTorch functions. From basic tensor creation to advanced and lesser known functions with specific usecases like torch.trapz, PyTorch provides many such functions which make job of a data science enthusiast easier. This was a beginner friendly introduction to PyTorch. There is much more. From here it would be a good idea to explore the documentation and create your own tensors. Play around and have some fun. As we get a tighter grip on the basics, we can move forward to Neural Networks and Deep Learning.Check out the ongoing course regarding Deep Learning by jovian.ai, Zero To Gans.Also check out my beginner friendly data analysis of Kickstarter Projects using Pandas, Matplotlib and Seaborn.Check out similar 5 powerful functions for beginners provided by NumPy here.Page 2"How many Marks did you get in your data science class?" "10 out of tensor" | Photo by Ales Nesetril on UnsplashPyTorch proficiency is one of the most sought after skill when it comes to recruitment for data scientists. For those who don't know, PyTorch is a Python library with a wide variety of functions and operations, mostly used for deep learning. One of the most basic yet important parts of PyTorch is the ability to create Tensors. A tensor is a number, vector, matrix, or any n-dimensional array.Now the question might be, `why not use numpy arrays instead?'For Deep Learning, wewould need to compute the derivative of elements of the data. PyTorch provides the ability to compute derivatives automatically, which NumPy does not. This is called `Auto Grad.' PyTorch also provides built in support for fast execution using GPU. This is essential when it comes to training models.All Deep Learning projects using PyTorch start with creating a tensor. Let's see a few MUST HAVE functions which are the backbone of any Deep Learning project.torch.tensor()torch.from_numpy()torch.unbind()torch.where()torch.trapz()Before we begin, let's install and import PyTorchFunction 1 -- torch.tensorCreates a new tensor. Arguments taken are :Data: The actual data to be stored in the tensor.dtype: Type of data. Note that type of all the elements of a tensor must be the same.device: To tell if GPU or CPU should be used.requires_grad: If you want the tensor to be differentiable (to compute gradient), set to True.Returns a tensor object.Example 1 (Working Example) :This creates a tensor with shape (10,2)Example 2 (Working Example) :We can also create an empty tensor using the same function.As we can see, an empty tensor with size (0) is created.Example 3 (Error) :A tensor with string values cannot be created.Summary :torch.tensor() forms the core of any PyTorch project, quite literally, as it forms tensors.For those who work with NumPy Arrays, this is a vital function. Using this, one can convert a NumPy Array into a Tensor. It is to be noted that as the tensor and the NumPy Array share the same memory, any changes made to the tensor would be applicable to the NumPy Array and vice versa. The arguments taken are :Returns a tensor.Example 1 (Working Example) :A NumPy array has been converted into a PyTorch Tensor. We can verify that by checking the type of both a1 and t1 as shown above.Example 2 (Working Example) :We first created two NumPy Arrays which contained (height, weight) and (heart rate) of 3 students. We created a tuple of both the variables. Then using a for loop, we converted the elements of the tuple to a tensor one by one. In the end, we checked the type of the elements to verify if the function has worked.Example 3 (Error) :A NumPy array containing string elements cannot be converted to a tensor. The only supported types for a tensor are : float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.Summary :This function is extremely useful for people who are using NumPy Arrays for their projects. It shows the interoperability of different Python libraries for data science which makes the language a go to for the majority of data science enthusiasts.This function removes a dimension from a tensor and returns a tuple of slices of the tensor without the removed dimension. Arguments it takes are :A tensorDimension to be removed (0 by default)Returns a tuple of slices.Example 1(Working Example) :The 0th dimension from (0,1) is removed thus we get 3 slices of the tensor in a tupleExample 2 (Working Example) :As seen above, now the data is sliced into data of 10 students for each day and stored in a tuple of tensors, that is, 7 different tensors are created, each corresponding to a day of the week. This sliced data can be used to further apply logic based on that particular day of the week.Example 3(Error) :We had created a tensor with 2 dimensions. We provided a value of 2 to the tensor.unbind() function. As 2 corresponds to the 3rd dimension, which doesn't exist in our example, the error 'Dimension out of range' is produced.Summary:This is a powerful PyTorch function that could be useful when you want to work on particular slices of the data along a dimension of the tensor.This is a really useful conditional function which depending on a condition, a returns a tensor of elements selected from either x or y tensor. Arguments required are :A conditionx: Elements from this tensor are selected for indices where Condition is Truey: Elements from this tensor are selected for indices where Condition is FalseExample 1(Working Example) :Here we gave a condition where the elements from X which were positive, got selected. The elements which were not positive were replaced with elements from YExample 2 (Working Example):Here we compared head to head scores and got a tensor containing scores of the winners.Example 3(Working Example):It is vital for dimensions and/or size of both the tensors x and y to match for comparison. Otherwise, we get the above error.Summary :Conditional testing is very important for any data analysis. We might need a particular type of data from two sets of data based on a certain criteria. torch.where() is a useful function for such scenarios.This function estimates the definite integral of y with respect to x along the given dimension, based on 'Trapezoidal rule'.The arguments required are :y : A tensor containing values of the function to integrate. (blue line in the illustration below)x : The points at which the function y is sampled. (x axis in the illustration below)Dimension for integrationReturns a tensor of the same shape as the input (except the dimension removed) where each elements represents the estimated integral along the given dimension.Example 1 (Working Example) :Here we have the estimated definite integration of y with respect to x.Example 2 (Working Example) :We can also provide a dx argument where sample points are spaced uniformly at a distance of dx.Example 3 (Error) :As we can see, the dimensions and size of both y and x should match, otherwise our code will produce the above error.Summary:Deep Learning requires a lot of calculus to know more about the models that are being used. torch.trapz() makes our job of finding estimated integral easy.This concludes our look at 5 important PyTorch functions. From basic tensor creation to advanced and lesser known functions with specific usecases like torch.trapz, PyTorch provides many such functions which make job of a data science enthusiast easier. This was a beginner friendly introduction to PyTorch. There is much more. From here it would be a good idea to explore the documentation and create your own tensors. Play around and have some fun. As we get a tighter grip on the basics, we can move forward to Neural Networks and Deep Learning.Check out the ongoing course regarding Deep Learning by jovian.ai, Zero To Gans.Also check out my beginner friendly data analysis of Kickstarter Projects using Pandas, Matplotlib and Seaborn.Check out similar 5 powerful functions for beginners provided by NumPy here. numpy array to tensor pytorch. convert numpy array to tensor pytorch. convert array to tensor pytorch. list of numpy array to tensor pytorch. convert numpy array to float tensor pytorch. pytorch c++ array to tensor. convert cuda tensor to numpy array pytorch

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