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Tensors, for human consumption
| Date | Stars |
|---|---|
| 2026-07-24 | 1391 |
| 2026-07-25 | 1391 |
| 2026-07-28 | 1391 |
| 2026-07-30 | 1391 |
| 2026-08-06 | 1391 |
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# ❤️ Lovely Tensors
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
[](https://alexey.work?ref=lt-md)
## [Read full docs](https://xl0.github.io/lovely-tensors)
### More lovely stuff
##### Working with numbers
- [Numpy](https://numpy.org/): ❤️ [Lovely
NumPy](https://github.com/xl0/lovely-numpy)
- [JAX](https://jax.readthedocs.io/): 💘 [Lovely
`JAX`](https://github.com/xl0/lovely-jax)
- [TinyGrad](https://github.com/tinygrad/tinygrad): 🫀 [Lovely
Grad](https://github.com/xl0/lovely-grad)
##### Community
- [Discord](https://discord.gg/qBaqauUWXP)
## Install
``` sh
pip install lovely-tensors
```
or
``` sh
mamba install lovely-tensors
```
or
``` sh
conda install -c conda-forge lovely-tensors
```
## How to use
How often do you find yourself debugging PyTorch code? You dump a tensor
to the cell output, and see this:
``` python
numbers
```
tensor([[[-0.3541, -0.3369, -0.4054, ..., -0.5596, -0.4739, 2.2489],
[-0.4054, -0.4226, -0.4911, ..., -0.9192, -0.8507, 2.1633],
[-0.4739, -0.4739, -0.5424, ..., -1.0390, -1.0390, 2.1975],
...,
[-0.9020, -0.8335, -0.9363, ..., -1.4672, -1.2959, 2.2318],
[-0.8507, -0.7822, -0.9363, ..., -1.6042, -1.5014, 2.1804],
[-0.8335, -0.8164, -0.9705, ..., -1.6555, -1.5528, 2.1119]],
[[-0.1975, -0.1975, -0.3025, ..., -0.4776, -0.3725, 2.4111],
[-0.2500, -0.2325, -0.3375, ..., -0.7052, -0.6702, 2.3585],
[-0.3025, -0.2850, -0.3901, ..., -0.7402, -0.8102, 2.3761],
...,
[-0.4251, -0.2325, -0.3725, ..., -1.0903, -1.0203, 2.4286],
[-0.3901, -0.2325, -0.4251, ..., -1.2304, -1.2304, 2.4111],
[-0.4076, -0.2850, -0.4776, ..., -1.2829, -1.2829, 2.3410]],
[[-0.6715, -0.9853, -0.8807, ..., -0.9678, -0.6890, 2.3960],
[-0.7238, -1.0724, -0.9678, ..., -1.2467, -1.0201, 2.3263],
[-0.8284, -1.1247, -1.0201, ..., -1.2641, -1.1596, 2.3786],
...,
[-1.2293, -1.4733, -1.3861, ..., -1.5081, -1.2641, 2.5180],
[-1.1944, -1.4559, -1.4210, ..., -1.6476, -1.4733, 2.4308],
[-1.2293, -1.5256, -1.5081, ..., -1.6824, -1.5256, 2.3611]]])
Was it really useful for you, as a human, to see all these numbers?
What is the shape? The size?
What are the statistics?
Are any of the values `nan` or `inf`?
Is it an image of a man holding a tench?
``` python
import lovely_tensors as lt
```
``` python
lt.monkey_patch()
```
## Summary
``` python
numbers # torch.Tensor
```
tensor[3, 196, 196] n=115248 (0.4 MiB) x∈[-2.118 |▂▅█▇▂▁▁▂▂▁| 2.640] μ=-0.388 σ=1.073
``` python
numbers.rgb
```
``` python
numbers.plt
```
Better, huh?
``` python
numbers[1,:6,1] # Still shows values if there are not too many.
```
tensor[6] x∈[-0.443, -0.197] μ=-0.311 σ=0.091 [-0.197, -0.232, -0.285, -0.373, -0.443, -0.338]
``` python
spicy = numbers[0,:12,0].clone()
spicy[0] *= 10000
spicy[1] /= 10000
spicy[2] = float('inf')
spicy[3] = float('-inf')
spicy[4] = float('nan')
spicy = spicy.reshape((2,6))
spicy # Spicy stuff
```
tensor[2, 6] n=12 x∈[-3.541e+03, -4.054e-05] μ=-393.842 σ=1.180e+03 +Inf! -Inf! NaN!
``` python
torch.zeros(10, 10) # A zero tensor - make it obvious
```
tensor[10, 10] n=100 all_zeros
``` python
spicy.v # Verbose
```
tensor[2, 6] n=12 x∈[-3.541e+03, -4.054e-05] μ=-393.842 σ=1.180e+03 +Inf! -Inf! NaN!
tensor([[-3.5405e+03, -4.0543e-05, inf, -inf, nan, -6.1093e-01],
[-6.1093e-01, -5.9380e-01, -5.9380e-01, -5.4243e-01, -5.4243e-01, -5.4243e-01]])
``` python
spicy.p # The plain old way
```
tensor([[-3.5405e+03, -4.0543e-05, inf, -inf, nan, -6.1093e-01],
[-6.1093e-01, -5.9380e-01,Excerpt of 16,402 characters
Read on GitHub
Alexey Zaytsev
136
5
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:fc00daf5e9743e1d, topic:deep-learning, topic:pytorch