# 2.7. Documentation¶ Open the notebook in Colab

Due to constraints on the length of this book, we cannot possibly introduce every single MXNet function and class (and you probably would not want us to). The API documentation and additional tutorials and examples provide plenty of documentation beyond the book. In this section we provide you with some guidance to exploring the MXNet API.

## 2.7.1. Finding All the Functions and Classes in a Module¶

In order to know which functions and classes can be called in a module, we invoke the dir function. For instance, we can query all properties in the np.random module as follows:

from mxnet import np
print(dir(np.random))

['__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_mx_nd_np', 'absolute_import', 'choice', 'multinomial', 'normal', 'rand', 'randint', 'shuffle', 'uniform']


Generally, we can ignore functions that start and end with __ (special objects in Python) or functions that start with a single _(usually internal functions). Based on the remaining function or attribute names, we might hazard a guess that this module offers various methods for generating random numbers, including sampling from the uniform distribution (uniform), normal distribution (normal), and multinomial distribution (multinomial).

## 2.7.2. Finding the Usage of Specific Functions and Classes¶

For more specific instructions on how to use a given function or class, we can invoke the help function. As an example, let us explore the usage instructions for ndarray’s ones_like function.

help(np.ones_like)

Help on function ones_like in module mxnet.numpy:

ones_like(a)
Return an array of ones with the same shape and type as a given array.

Parameters
----------
a : ndarray
The shape and data-type of a define these same attributes of
the returned array.

Returns
-------
out : ndarray
Array of ones with the same shape and type as a.

Examples
--------
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0., 1., 2.],
[3., 4., 5.]])
>>> np.ones_like(x)
array([[1., 1., 1.],
[1., 1., 1.]])

>>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.], dtype=float64)
>>>
>>> np.ones_like(y)
array([1., 1., 1.], dtype=float64)

From the documentation, we can see that the ones_like function creates a new array with the same shape as the supplied ndarray and sets all the elements to 1. Whenever possible, you should run a quick test to confirm your interpretation:

x = np.array([[0, 0, 0], [2, 2, 2]])
np.ones_like(x)

array([[1., 1., 1.],
[1., 1., 1.]])


In the Jupyter notebook, we can use ? to display the document in another window. For example, np.random.uniform? will create content that is almost identical to help(np.random.uniform), displaying it in a new browser window. In addition, if we use two question marks, such as np.random.uniform??, the code implementing the function will also be displayed.

## 2.7.3. API Documentation¶

For further details on the API details check the MXNet website at http://mxnet.apache.org/. You can find the details under the appropriate headings (also for programming languages other than Python).

## 2.7.4. Summary¶

• The official documentation provides plenty of descriptions and examples that are beyond this book.

• We can look up documentation for the usage of MXNet API by calling the dir and help functions, or checking the MXNet website.

## 2.7.5. Exercises¶

1. Look up ones_like and autograd on the MXNet website.

2. What are all the possible outputs after running np.random.choice(4, 2)?

3. Can you rewrite np.random.choice(4, 2) by using the np.random.randint function?

## 2.7.6. Discussions¶ 