Aquí os dejo con un ejemplo (red_neuronal_moderna.py) muy sencillo y claro para realizar una red neuronal moderna.
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 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 RMSprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6): grads = T.grad(cost=cost, wrt=params) updates = [] for p, g in zip(params, grads): acc = theano.shared(p.get_value() * 0.) acc_new = rho * acc + (1 - rho) * g ** 2 gradient_scaling = T.sqrt(acc_new + epsilon) g = g / gradient_scaling updates.append((acc, acc_new)) updates.append((p, p - lr * g)) return updates 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 model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden): X = dropout(X, p_drop_input) h = rectify(T.dot(X, w_h)) h = dropout(h, p_drop_hidden) h2 = rectify(T.dot(h, w_h2)) h2 = dropout(h2, p_drop_hidden) py_x = softmax(T.dot(h2, w_o)) return h, h2, py_x trX, teX, trY, teY = mnist(onehot=True) X = T.fmatrix() Y = T.fmatrix() w_h = init_weights((784, 625)) w_h2 = init_weights((625, 625)) w_o = init_weights((625, 10)) noise_h, noise_h2, noise_py_x = model(X, w_h, w_h2, w_o, 0.2, 0.5) h, h2, py_x = model(X, w_h, w_h2, w_o, 0., 0.) y_x = T.argmax(py_x, axis=1) cost = T.mean(T.nnet.categorical_crossentropy(noise_py_x, Y)) params = [w_h, w_h2, w_o] updates = RMSprop(cost, params, lr=0.001) train = theano.function(inputs=[X, Y], outputs=cost, updates=updates, allow_input_downcast=True) predict = theano.function(inputs=[X], outputs=y_x, allow_input_downcast=True) for i in range(100): for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)): cost = train(trX[start:end], trY[start:end]) print(np.mean(np.argmax(teY, axis=1) == predict(teX)))
