blue-sky - 1 year ago 308

Python Question

Here is a Python representation of a Neural Network Neuron that I'm trying to understand

`class Network(object):`

def __init__(self, sizes):

self.num_layers = len(sizes)

self.sizes = sizes

self.biases = [np.random.randn(y, 1) for y in sizes[1:]]

self.weights = [np.random.randn(y, x)

for x, y in zip(sizes[:-1], sizes[1:])]

Here is my current understanding :

- : Return the number of items in sizes
`self.num_layers = len(sizes)`

- : assign self instance sizes to function parameter sizes
`self.sizes = sizes`

- : generate an array of elements from the standard normal distribution (indicated by
`self.biases = sizes`

)`np.random.randn(y, 1)`

What is the following line computing?

`self.weights = [np.random.randn(y, x)`

for x, y in zip(sizes[:-1], sizes[1:])]

I'm new to Python. Can this code be used within a Python shell so I can gain a better understanding by invoking each line separately ?

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Answer Source

The `zip()`

function pairs up elements from each iterable; `zip('foo', 'bar')`

for example, would produce `[('f', 'b'), ('o', 'a'), ('o', 'r')]`

; each element in the two strings has been paired up into three new tuples.

`zip(sizes[:-1], sizes[1:])`

then, creates pairs of elements in the sequence `sizes`

with the *next* element, because you pair up all elements except the last (`sizes[:-1]`

) with all elements except the first (`sizes[1:]`

). This pairs up the first and second element together, then the second and third, etc. all the way to the last two elements.

For each such pair a random sample is produced, using a list comprehension. So for each `x, y`

pair, a new 2-dimensional numpy matrix is produced with random values divided over `y`

rows and `x`

columns.

Note that the `biases`

value only uses `sizes[1:]`

, all but the first, to produce `y`

-by-1 matrices for each such size.

Quick demo of these concepts:

```
>>> zip('foo', 'bar')
[('f', 'b'), ('o', 'a'), ('o', 'r')]
>>> zip('foo', 'bar', 'baz') # you can add more sequences
[('f', 'b', 'b'), ('o', 'a', 'a'), ('o', 'r', 'z')]
>>> sizes = [5, 12, 18, 23, 42]
>>> zip(sizes[:-1], sizes[1:]) # a sliding window of pairs
[(5, 12), (12, 18), (18, 23), (23, 42)]
# 0, 1 .. 1, 2 .. 2, 3 .. 3, 4 element indices into sizes
>>>
```

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