-
-
Save lxuechen/03106c21745ffc7c7970ad0883a39ce0 to your computer and use it in GitHub Desktop.
Theano implementation of Bayes-by-Backprop algorithm from "Weight uncertainty in neural networks" paper
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import theano | |
import theano.tensor as T | |
from theano.tensor.shared_randomstreams import RandomStreams | |
from theano.sandbox.rng_mrg import MRG_RandomStreams | |
from lasagne.updates import adam | |
from lasagne.utils import collect_shared_vars | |
from sklearn.datasets import fetch_mldata | |
from sklearn.cross_validation import train_test_split | |
from sklearn import preprocessing | |
import numpy as np | |
rnd = RandomStreams(seed=123) | |
gpu_rnd = MRG_RandomStreams(seed=123) | |
def nonlinearity(x): | |
return T.nnet.relu(x) | |
def log_gaussian(x, mu, sigma): | |
return -0.5 * np.log(2 * np.pi) - T.log(T.abs_(sigma)) - (x - mu) ** 2 / (2 * sigma ** 2) | |
def log_gaussian_logsigma(x, mu, logsigma): | |
return -0.5 * np.log(2 * np.pi) - logsigma / 2. - (x - mu) ** 2 / (2. * T.exp(logsigma)) | |
def _shared_dataset(data_xy, borrow=True): | |
data_x, data_y = data_xy | |
shared_x = theano.shared(np.asarray(data_x, dtype=theano.config.floatX), borrow=borrow) | |
shared_y = theano.shared(np.asarray(data_y, dtype=theano.config.floatX), borrow=borrow) | |
return shared_x, shared_y | |
def init(shape): | |
return np.asarray( | |
np.random.normal(0, 0.05, size=shape), | |
dtype=theano.config.floatX | |
) | |
def get_random(shape, avg, std): | |
return gpu_rnd.normal(shape, avg=avg, std=std) | |
if __name__ == '__main__': | |
mnist = fetch_mldata('MNIST original') | |
# prepare data | |
N = 5000 | |
data = np.float32(mnist.data[:]) / 255. | |
idx = np.random.choice(data.shape[0], N) | |
data = data[idx] | |
target = np.int32(mnist.target[idx]).reshape(N, 1) | |
train_idx, test_idx = train_test_split(np.array(range(N)), test_size=0.05) | |
train_data, test_data = data[train_idx], data[test_idx] | |
train_target, test_target = target[train_idx], target[test_idx] | |
train_target = np.float32(preprocessing.OneHotEncoder(sparse=False).fit_transform(train_target)) | |
# inputs | |
x = T.matrix('x') | |
y = T.matrix('y') | |
n_input = train_data.shape[1] | |
M = train_data.shape[0] | |
sigma_prior = T.exp(-3) | |
n_samples = 3 | |
learning_rate = 0.001 | |
n_epochs = 100 | |
# weights | |
# L1 | |
n_hidden_1 = 200 | |
W1_mu = theano.shared(value=init((n_input, n_hidden_1))) | |
W1_logsigma = theano.shared(value=init((n_input, n_hidden_1))) | |
b1_mu = theano.shared(value=init((n_hidden_1,))) | |
b1_logsigma = theano.shared(value=init((n_hidden_1,))) | |
# L2 | |
n_hidden_2 = 200 | |
W2_mu = theano.shared(value=init((n_hidden_1, n_hidden_2))) | |
W2_logsigma = theano.shared(value=init((n_hidden_1, n_hidden_2))) | |
b2_mu = theano.shared(value=init((n_hidden_2,))) | |
b2_logsigma = theano.shared(value=init((n_hidden_2,))) | |
# L3 | |
n_output = 10 | |
W3_mu = theano.shared(value=init((n_hidden_2, n_output))) | |
W3_logsigma = theano.shared(value=init((n_hidden_2, n_output))) | |
b3_mu = theano.shared(value=init((n_output,))) | |
b3_logsigma = theano.shared(value=init((n_output,))) | |
all_params = [ | |
W1_mu, W1_logsigma, b1_mu, b1_logsigma, | |
W2_mu, W2_logsigma, b2_mu, b2_logsigma, | |
W3_mu, W3_logsigma, b3_mu, b3_logsigma | |
] | |
all_params = collect_shared_vars(all_params) | |
# building the objective | |
# remember, we're evaluating by samples | |
log_pw, log_qw, log_likelihood = 0., 0., 0. | |
for _ in xrange(n_samples): | |
epsilon_w1 = get_random((n_input, n_hidden_1), avg=0., std=sigma_prior) | |
epsilon_b1 = get_random((n_hidden_1,), avg=0., std=sigma_prior) | |
W1 = W1_mu + T.log(1. + T.exp(W1_logsigma)) * epsilon_w1 | |
b1 = b1_mu + T.log(1. + T.exp(b1_logsigma)) * epsilon_b1 | |
epsilon_w2 = get_random((n_hidden_1, n_hidden_2), avg=0., std=sigma_prior) | |
epsilon_b2 = get_random((n_hidden_2,), avg=0., std=sigma_prior) | |
W2 = W2_mu + T.log(1. + T.exp(W2_logsigma)) * epsilon_w2 | |
b2 = b2_mu + T.log(1. + T.exp(b2_logsigma)) * epsilon_b2 | |
epsilon_w3 = get_random((n_hidden_2, n_output), avg=0., std=sigma_prior) | |
epsilon_b3 = get_random((n_output,), avg=0., std=sigma_prior) | |
W3 = W3_mu + T.log(1. + T.exp(W3_logsigma)) * epsilon_w3 | |
b3 = b3_mu + T.log(1. + T.exp(b3_logsigma)) * epsilon_b3 | |
a1 = nonlinearity(T.dot(x, W1) + b1) | |
a2 = nonlinearity(T.dot(a1, W2) + b2) | |
h = T.nnet.softmax(nonlinearity(T.dot(a2, W3) + b3)) | |
sample_log_pw, sample_log_qw, sample_log_likelihood = 0., 0., 0. | |
for W, b, W_mu, W_logsigma, b_mu, b_logsigma in [(W1, b1, W1_mu, W1_logsigma, b1_mu, b1_logsigma), | |
(W2, b2, W2_mu, W2_logsigma, b2_mu, b2_logsigma), | |
(W3, b3, W3_mu, W3_logsigma, b3_mu, b3_logsigma)]: | |
# first weight prior | |
sample_log_pw += log_gaussian(W, 0., sigma_prior).sum() | |
sample_log_pw += log_gaussian(b, 0., sigma_prior).sum() | |
# then approximation | |
sample_log_qw += log_gaussian_logsigma(W, W_mu, W_logsigma * 2).sum() | |
sample_log_qw += log_gaussian_logsigma(b, b_mu, b_logsigma * 2).sum() | |
# then the likelihood | |
sample_log_likelihood = log_gaussian(y, h, sigma_prior).sum() | |
log_pw += sample_log_pw | |
log_qw += sample_log_qw | |
log_likelihood += sample_log_likelihood | |
log_qw /= n_samples | |
log_pw /= n_samples | |
log_likelihood /= n_samples | |
batch_size = 100 | |
n_batches = M / float(batch_size) | |
objective = ((1. / n_batches) * (log_qw - log_pw) - log_likelihood).sum() / float(batch_size) | |
# updates | |
updates = adam(objective, all_params, learning_rate=learning_rate) | |
i = T.iscalar() | |
train_data = theano.shared(np.asarray(train_data, dtype=theano.config.floatX)) | |
train_target = theano.shared(np.asarray(train_target, dtype=theano.config.floatX)) | |
train_function = theano.function( | |
inputs=[i], | |
outputs=objective, | |
updates=updates, | |
givens={ | |
x: train_data[i * batch_size: (i + 1) * batch_size], | |
y: train_target[i * batch_size: (i + 1) * batch_size] | |
} | |
) | |
a1_mu = nonlinearity(T.dot(x, W1_mu) + b1_mu) | |
a2_mu = nonlinearity(T.dot(a1_mu, W2_mu) + b2_mu) | |
h_mu = T.nnet.softmax(nonlinearity(T.dot(a2_mu, W3_mu) + b3_mu)) | |
output_function = theano.function([x], T.argmax(h_mu, axis=1)) | |
n_train_batches = int(train_data.get_value().shape[0] / float(batch_size)) | |
# and finally, training loop | |
for e in xrange(n_epochs): | |
errs = [] | |
for b in xrange(n_train_batches): | |
batch_err = train_function(b) | |
errs.append(batch_err) | |
out = output_function(test_data) | |
acc = np.count_nonzero(output_function(test_data) == np.int32(test_target.ravel())) / float(test_data.shape[0]) | |
print 'epoch', e, 'cost', np.mean(errs), 'Accuracy', acc |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment