https://github.com/mattjj/autodidact

A pedagogical implementation of Autograd
https://github.com/mattjj/autodidact

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A pedagogical implementation of Autograd

• Host: GitHub
• URL: https://github.com/mattjj/autodidact
• Owner: mattjj
• License: mit
• Created: 2018-01-30T14:01:12.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2020-05-26T14:11:59.000Z (over 4 years ago)
• Last Synced: 2024-10-30T03:43:08.359Z (14 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 62.5 KB
• Stars: 953
• Watchers: 17
• Forks: 100
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: license.txt

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README

        
# Autodidact: a pedagogical implementation of [Autograd](https://github.com/hips/autograd)

This is a tutorial implementation based on [the full version of

Autograd](https://github.com/hips/autograd).

Example use:

```python

>>> import autograd.numpy as np  # Thinly-wrapped numpy

>>> from autograd import grad    # The only autograd function you may ever need

>>>

>>> def tanh(x):                 # Define a function

...     y = np.exp(-2.0 * x)

...     return (1.0 - y) / (1.0 + y)

...

>>> grad_tanh = grad(tanh)       # Obtain its gradient function

>>> grad_tanh(1.0)               # Evaluate the gradient at x = 1.0

0.41997434161402603

>>> (tanh(1.0001) - tanh(0.9999)) / 0.0002  # Compare to finite differences

0.41997434264973155

```

We can continue to differentiate as many times as we like, and use numpy's

vectorization of scalar-valued functions across many different input values:

```python

>>> import matplotlib.pyplot as plt

>>> x = np.linspace(-7, 7, 200)

>>> plt.plot(x, tanh(x),

...          x, grad(tanh)(x),                                # first  derivative

...          x, grad(grad(tanh))(x),                          # second derivative

...          x, grad(grad(grad(tanh)))(x),                    # third  derivative

...          x, grad(grad(grad(grad(tanh))))(x),              # fourth derivative

...          x, grad(grad(grad(grad(grad(tanh)))))(x),        # fifth  derivative

...          x, grad(grad(grad(grad(grad(grad(tanh))))))(x))  # sixth  derivative

>>> plt.show()

```

Autograd was written by [Dougal Maclaurin](https://dougalmaclaurin.com),

[David Duvenaud](https://www.cs.toronto.edu/~duvenaud/)

and [Matt Johnson](http://people.csail.mit.edu/mattjj/).

See [the main page](https://github.com/hips/autograd) for more information.