00mjk/pytorch-python

💎 Introduction to machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab

Pytorch Python

Introduction to machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook's AI Research lab

Installation

To install pytorch on windows run the following command:

pip install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio===0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

OR visit the Installation Docs for your machine:

TORCH

• The torch package contains data structures for multi-dimensional tensors and defines mathematical operations over these tensors. Additionally, it provides many utilities for efficient serializing of Tensors and arbitrary types, and other useful utilities.

• Docs

Examples in the Jupyter Notebook

Math Operations

Pointwise Ops

a = torch.tensor([2, 8, -9, 0, -17, 67])
b = torch.tensor([1, 2, 3, 4, 5])
c = torch.tensor([9, 8, 7, 6, 5])
d = torch.tensor([1,1, 0, 1, 0, 0, 0, 1])
e = torch.tensor([0,0, 0, 1, 0, 0, 0, 1])
f = torch.rand(12)

1. torch.abs()

• Computes the absolute value of each element in input.

# abs()
print(torch.abs(a))

2. torch.absolute()

• Alias for torch.abs()

# absolute()
print(torch.absolute(a))

3. torch.acos() || torch.acosh()

• Computes the inverse cosine of each element in input.

print(torch.arccos(a))

4. torch.add() || torch.div() [torch.divide()] || torch.mul() [torch.multiply()] || torch.sub()[torch.subtract()]

• performs the scalar other to each element of the input input and returns a new resulting tensor.

print(torch.add(b, c))
print(c + b)

5. torch.max() || torch.min()

• returns the maximum and minimum values in a tensor

print(torch.max(a), torch.min(a))
print(a.min(), a.max())

6. torch.bitwise_not() || torch.bitwise_and() || torch.bitwise_or() || torch.bitwise_xor()

• Computes the bitwise operations on tensors

print(torch.bitwise_or(d, e))
print(torch.bitwise_xor(d, e))
print(torch.bitwise_and(d, e))
print(torch.bitwise_not(d))

7. torch.ceil() || touch.floor()

• rounds up or down a tensor and return a result.

print(torch.ceil(f))
print(torch.floor(f))

8. torch.clamp() || clip()

• Clamp all elements in input into the range [ min, max ].

print(torch.clamp(a.float(), 0, 3))

9. torch.conj()

• Computes the element-wise conjugate of the given input tensor.

print(torch.conj(a.reshape(3, -1)))

There are a lot of these Pointwise Ops in the documentation

Reduction Ops

1. torch.abs()

• Computes the absolute value of each element in input.

1. torch.all() || torch.any()

• Tests if all or one element(s) in input evaluate to True.

print(torch.all(d))
print(torch.any(d))

2. torch.mean() || torch.mode() || torch.median() || torch.std() || torch.var()

• Computes the absolute value of each element in input.

print(torch.mean(b.float()))
print(torch.mode(b))
print(torch.median(b))

3. torch.unique()

• Returns the unique elements of the input tensor.

print(torch.unique(a))
print(a.unique())

4. torch.argmax() || torch.argmin()

• Returns the indices of the maximum || minimum value of all elements in the input tensor.

print(torch.argmax(a), torch.argmin(a))

4. torch.argmax() || torch.argmin()

• Returns the indices of the maximum || minimum value of all elements in the input tensor.

print(torch.argmax(a), torch.argmin(a))

4. torch.argmax() || torch.argmin()

• Returns the indices of the maximum || minimum value of all elements in the input tensor.

print(torch.argmax(a), torch.argmin(a))

There are a lot of these in the documentation

• Comparison Ops
• Spectral Ops
• Other Operations
• BLAS and LAPACK Operations
• Utilities
• Documentation Reference

TOUCH.NN

1. torch.abs()

• Computes the absolute value of each element in input.

1. torch.abs()

• Computes the absolute value of each element in input.

1. torch.abs()

• Computes the absolute value of each element in input.

1. torch.abs()

• Computes the absolute value of each element in input.

1. torch.abs()

• Computes the absolute value of each element in input.

1. torch.abs()

• Computes the absolute value of each element in input.

1. torch.abs()

• Computes the absolute value of each element in input.

1. torch.abs()

• Computes the absolute value of each element in input.

print(to