Simple way to create matrix of random numbers

You can drop the range(len()):

weights_h = [[random.random() for e in inputs[0]] for e in range(hiden_neurons)]

But really, you should probably use numpy.

In [9]: numpy.random.random((3, 3))
Out[9]:
array([[ 0.37052381,  0.03463207,  0.10669077],
       [ 0.05862909,  0.8515325 ,  0.79809676],
       [ 0.43203632,  0.54633635,  0.09076408]])

Take a look at numpy.random.rand:

Docstring: rand(d0, d1, ..., dn)

Random values in a given shape.

Create an array of the given shape and propagate it with random samples from a uniform distribution over [0, 1).

>>> import numpy as np
>>> np.random.rand(2,3)
array([[ 0.22568268,  0.0053246 ,  0.41282024],
       [ 0.68824936,  0.68086462,  0.6854153 ]])

use np.random.randint() as np.random.random_integers() is deprecated

random_matrix = np.random.randint(min_val,max_val,(<num_rows>,<num_cols>))

Looks like you are doing a Python implementation of the Coursera Machine Learning Neural Network exercise. Here's what I did for randInitializeWeights(L_in, L_out)

#get a random array of floats between 0 and 1 as Pavel mentioned 
W = numpy.random.random((L_out, L_in +1))

#normalize so that it spans a range of twice epsilon
W = W * 2 * epsilon

#shift so that mean is at zero
W = W - epsilon