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, 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 model(X, w, w2, w3, w4, p_drop_conv, p_drop_hidden): l1a = rectify(conv2d(X, w, border_mode='full')) l1 = max_pool_2d(l1a, (2, 2)) l1 = dropout(l1, p_drop_conv) l2a = rectify(conv2d(l1, w2)) l2 = max_pool_2d(l2a, (2, 2)) l2 = dropout(l2, p_drop_conv) l3a = rectify(conv2d(l2, w3)) l3b = max_pool_2d(l3a, (2, 2)) l3 = T.flatten(l3b, outdim=2) l3 = dropout(l3, p_drop_conv) l4 = rectify(T.dot(l3, w4)) l4 = dropout(l4, p_drop_hidden) pyx = softmax(T.dot(l4, w_o)) return l1, l2, l3, l4, pyx trX, teX, trY, teY = mnist(onehot=True) trX = trX.reshape(-1, 1, 28, 28) teX = teX.reshape(-1, 1, 28, 28) X = T.tensor4(dtype='float64') Y = T.matrix() w = init_weights((32, 1, 3, 3)) w2 = init_weights((64, 32, 3, 3)) w3 = init_weights((128, 64, 3, 3)) w4 = init_weights((128 * 3 * 3, 625)) w_o = init_weights((625, 10)) noise_l1, noise_l2, noise_l3, noise_l4, noise_py_x = model(X, w, w2, w3, w4, 0.2, 0.5) l1, l2, l3, l4, py_x = model(X, w, w2, w3, w4, 0., 0.) y_x = T.argmax(py_x, axis=1) cost = T.mean(T.nnet.categorical_crossentropy(noise_py_x, Y)) params = [w, w2, w3, w4, 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)))
