I am using the LSTM tutorial for Theano (http://deeplearning.net/tutorial/lstm.html). In the lstm.py (http://deeplearning.net/tutorial/code/lstm.py) file, I don't understand the following line:
c = m_[:, None] * c + (1. - m_)[:, None] * c_
What does m_[:, None]
mean? In this case m_
is the theano vector while c
is a matrix.
This question has been asked and answered on the Theano mailing list, but is actually about the basics of numpy indexing.
Here are the question and answer
https://groups.google.com/forum/#!topic/theano-users/jq92vNtkYUI
For completeness, here is another explanation: slicing with None
adds an axis to your array, see the relevant numpy documentation, because it behaves the same in both numpy and Theano:
http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#numpy.newaxis
Note that np.newaxis is None
:
import numpy as np
a = np.arange(30).reshape(5, 6)
print a.shape # yields (5, 6)
print a[np.newaxis, :, :].shape # yields (1, 5, 6)
print a[:, np.newaxis, :].shape # yields (5, 1, 6)
print a[:, :, np.newaxis].shape # yields (5, 6, 1)
Typically this is used to adjust shapes to be able to broadcast to higher dimensions. E.g. tiling 7 times in the middle axis can be achieved as
b = a[:, np.newaxis] * np.ones((1, 7, 1))
print b.shape # yields (5, 7, 6), 7 copies of a along the second axis
I think the Theano vector's __getitem__
method expects a tuple as an argument! like this:
class Vect (object):
def __init__(self,data):
self.data=list(data)
def __getitem__(self,key):
return self.data[key[0]:key[1]+1]
a=Vect('hello')
print a[0,2]
Here print a[0,2]
when a
is an ordinary list will raise an exception:
>>> a=list('hello')
>>> a[0,2]
Traceback (most recent call last):
File "<string>", line 1, in <module>
TypeError: list indices must be integers, not tuple
But here the __getitem__
method is different and it accepts a tuple as an argument.
You can pass the :
sign to __getitem__
like this as :
means slice:
class Vect (object):
def __init__(self,data):
self.data=list(data)
def __getitem__(self,key):
return self.data[0:key[1]+1]+list(key[0].indices(key[1]))
a=Vect('hello')
print a[:,2]
Speaking about None
, it can be used when indexing in plain Python as well:
>>> 'hello'[None:None]
'hello'