# tinygrad

![Unit Tests](https://github.com/geohot/tinygrad/workflows/Unit%20Tests/badge.svg)

For something in between a [pytorch](https://github.com/pytorch/pytorch) and a [karpathy/micrograd](https://github.com/karpathy/micrograd)

This may not be the best deep learning framework, but it is a deep learning framework.

The Tensor class is a wrapper around a numpy array, except it does Tensor things.

### Example

```python
import numpy as np
from tinygrad.tensor import Tensor

x = Tensor(np.eye(3))
y = Tensor(np.array([[2.0,0,-2.0]]))
z = y.dot(x).sum()
z.backward()

print(x.grad)  # dz/dx
print(y.grad)  # dz/dy
```

### Same example in torch

```python
import torch

x = torch.eye(3, requires_grad=True)
y = torch.tensor([[2.0,0,-2.0]], requires_grad=True)
z = y.matmul(x).sum()
z.backward()

print(x.grad)  # dz/dx
print(y.grad)  # dz/dy
```

### Neural networks?

It turns out, a decent autograd tensor library is 90% of what you need for neural networks. Add an optimizer (SGD and Adam implemented) from tinygrad.optim, write some boilerplate minibatching code, and you have all you need.

### Neural network example (from test/mnist.py)

```python
from tinygrad.tensor import Tensor
import tinygrad.optim as optim
from tinygrad.utils import layer_init_uniform

class TinyBobNet:
  def __init__(self):
    self.l1 = Tensor(layer_init_uniform(784, 128))
    self.l2 = Tensor(layer_init_uniform(128, 10))

  def forward(self, x):
    return x.dot(self.l1).relu().dot(self.l2).logsoftmax()

model = TinyBobNet()
optim = optim.SGD([model.l1, model.l2], lr=0.001)

# ... and complete like pytorch, with (x,y) data

out = model.forward(x)
loss = out.mul(y).mean()
loss.backward()
optim.step()
```

### TODO (to make real neural network library)

* Implement gradcheck (numeric)
* Make convolutions not slow while removing lines of code (NO CYTHON!)