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| #coding: utf8 | |
| """ | |
| 1. Download this gist. | |
| 2. Get the MNIST data. | |
| wget http://deeplearning.net/data/mnist/mnist.pkl.gz | |
| 3. Run this code. | |
| python autoencoder.py 100 -e 1 -b 20 -v | |
| Wait about a minute ... and get a vialization of weights. | |
| """ | |
| import numpy | |
| import argparse | |
| import cPickle as pickle | |
| import utils | |
| class Autoencoder(object): | |
| def __init__(self, n_visible = 784, n_hidden = 784, \ | |
| W1 = None, W2 = None, b1 =None, b2 = None, | |
| noise = 0.0, untied = False): | |
| self.rng = numpy.random.RandomState(1) | |
| r = numpy.sqrt(6. / (n_hidden + n_visible + 1)) | |
| if W1 == None: | |
| self.W1 = self.random_init(r, (n_hidden, n_visible)) | |
| if W2 == None: | |
| if untied: | |
| W2 = self.random_init(r, (n_visible, n_hidden)) | |
| else: | |
| W2 = self.W1.T | |
| self.W2 = W2 | |
| if b1 == None: | |
| self.b1 = numpy.zeros(n_hidden) | |
| if b2 == None: | |
| self.b2 = numpy.zeros(n_visible) | |
| self.n_visible = n_visible | |
| self.n_hidden = n_hidden | |
| self.alpha = 0.1 | |
| self.noise = noise | |
| self.untied = untied | |
| def random_init(self, r, size): | |
| return numpy.array(self.rng.uniform(low = -r, high = r, size=size)) | |
| def sigmoid(self, x): | |
| return 1. / (1. + numpy.exp(-x)) | |
| def sigmoid_prime(self, x): | |
| return x * (1. - x) | |
| def corrupt(self, x, noise): | |
| return self.rng.binomial(size = x.shape, n = 1, p = 1.0 - noise) * x | |
| def encode(self, x): | |
| return self.sigmoid(numpy.dot(self.W1, x) + self.b1) | |
| def decode(self, y): | |
| return self.sigmoid(numpy.dot(self.W2, y) + self.b2) | |
| def get_cost(self, x, z): | |
| eps = 1e-10 | |
| return - numpy.sum((x * numpy.log(z + eps) + (1.-x) * numpy.log(1.-z + eps))) | |
| def get_cost_and_grad(self, x_batch, dnum): | |
| cost = 0. | |
| grad_W1 = numpy.zeros(self.W1.shape) | |
| grad_W2 = numpy.zeros(self.W2.shape) | |
| grad_b1 = numpy.zeros(self.b1.shape) | |
| grad_b2 = numpy.zeros(self.b2.shape) | |
| for x in x_batch: | |
| tilde_x = self.corrupt(x, self.noise) | |
| p = self.encode(tilde_x) | |
| y = self.decode(p) | |
| cost += self.get_cost(x,y) | |
| delta1 = - (x - y) | |
| if self.untied: | |
| grad_W2 += numpy.outer(delta1, p) | |
| else: | |
| grad_W1 += numpy.outer(delta1, p).T | |
| grad_b2 += delta1 | |
| delta2 = numpy.dot(self.W2.T, delta1) * self.sigmoid_prime(p) | |
| grad_W1 += numpy.outer(delta2, tilde_x) | |
| grad_b1 += delta2 | |
| cost /= len(x_batch) | |
| grad_W1 /= len(x_batch) | |
| grad_W2 /= len(x_batch) | |
| grad_b1 /= len(x_batch) | |
| grad_b2 /= len(x_batch) | |
| return cost, grad_W1, grad_W2, grad_b1, grad_b2 | |
| def train(self, X, epochs = 15, batch_size = 20): | |
| batch_num = len(X) / batch_size | |
| for epoch in range(epochs): | |
| total_cost = 0.0 | |
| for i in range(batch_num): | |
| batch = X[i*batch_size : (i+1)*batch_size] | |
| cost, gradW1, gradW2, gradb1, gradb2 = \ | |
| self.get_cost_and_grad(batch, len(X)) | |
| total_cost += cost | |
| self.W1 -= self.alpha * gradW1 | |
| self.W2 -= self.alpha * gradW2 | |
| self.b1 -= self.alpha * gradb1 | |
| self.b2 -= self.alpha * gradb2 | |
| grad_sum = gradW1.sum() + gradW2.sum() + gradb1.sum() + gradb2.sum() | |
| print epoch, | |
| print (1. / batch_num) * total_cost | |
| def dump_weights(self, save_path): | |
| with open(save_path, 'w') as f: | |
| d = { | |
| "W1" : self.W1, | |
| "W2" : self.W2, | |
| "b1" : self.b1, | |
| "b2" : self.b2, | |
| } | |
| pickle.dump(d, f) | |
| def visualize_weights(self): | |
| tile_size = (int(numpy.sqrt(self.W1[0].size)), int(numpy.sqrt(self.W1[0].size))) | |
| panel_shape = (10, 10) | |
| return utils.visualize_weights(self.W1, panel_shape, tile_size) | |
| #panel_shape = (int(numpy.sqrt(self.W1.shape[0])), int(numpy.sqrt(self.W1.shape[0]))) | |
| #return utils.visualize_weights(self.W1, panel_shape, tile_size) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("n_hidden", type = int) | |
| parser.add_argument("-e", "--epochs", type = int, default = 15) | |
| parser.add_argument("-b", "--batch_size", type = int, default = 20) | |
| parser.add_argument("-n", "--noise", type=float, choices=[i/10. for i in xrange(11)], default = 0.0) | |
| parser.add_argument('-o', '--output', type = unicode) | |
| parser.add_argument('-v', '--visualize', action = "store_true") | |
| parser.add_argument('-u', '--untied', action = "store_true") | |
| args = parser.parse_args() | |
| train_data, test_data, valid_data = utils.load_data() | |
| ae = Autoencoder(n_hidden = args.n_hidden, noise = args.noise, untied = args.untied) | |
| try: | |
| ae.train(train_data[0], epochs = args.epochs, batch_size = args.batch_size) | |
| except KeyboardInterrupt: | |
| exit() | |
| pass | |
| save_name = args.output | |
| if save_name == None: | |
| save_name = '%sh%d_e%d_b%d_n%d'%( | |
| 'untied_' if args.untied else 'tied_', | |
| args.n_hidden, | |
| args.epochs, | |
| args.batch_size, | |
| args.noise*100, | |
| ) | |
| img = ae.visualize_weights() | |
| img.save(save_name + ".bmp") | |
| if args.visualize: | |
| img.show() | |
| ae.dump_weights(save_name + '.pkl') | |
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