by mattjj

mattjj / autodidact

A pedagogical implementation of Autograd

506 Stars 63 Forks Last release: Not found MIT License 13 Commits 0 Releases

Available items

No Items, yet!

The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:

Autodidact: a pedagogical implementation of Autograd

This is a tutorial implementation based on the full version of Autograd.

Example use:

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

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:

>>> 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

Autograd was written by Dougal Maclaurin, David Duvenaud and Matt Johnson. See the main page for more information.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.