Aquí os dejo con un ejemplo (red_neuronal_convolucional.py) muy sencillo y claro para realizar una red neuronal convolucional.
import theano
from theano import tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
import numpy as np
from Load import mnist
from theano.tensor.nnet.conv import conv2d
from theano.tensor.signal.downsample import max_pool_2d
srng = RandomStreams()
def floatX(X):
return np.asarray(X, dtype=theano.config.floatX)
def init_weights(shape):
return theano.shared(floatX(np.random.randn(*shape) * 0.01))
def rectify(X):
return T.maximum(X, 0.)
def softmax(X):
e_x = T.exp(X – X.max(axis=1).dimshuffle(0, ‘x’))
return e_x / e_x.sum(axis=1).dimshuffle(0, ‘x’)
def dropout(X, p=0.):
if p 0:
retain_prob = 1 – p
X *= srng.binomial(X.shape, p=retain_prob, dtype=theano.config.floatX)
X /= retain_prob
return X
def RMSprop(cost,