n_hidden = [500, 500]
n_epochs = 200
x_dist = 'Bernoulli'
temp_epochs, start_temp = None, 0.0
l2_reg, initVar, alpha = float(sys.argv[2]), -10., 1.1
batchnorm, mc_samps = True, int(sys.argv[6])
eval_samps = 1000
binarize, logging, verbose = True, False, 2
test_ll, test_ac = [], []
for betaNum, beta in enumerate(betaset):
print("beta = {:5.3f}".format(beta))
for run in range(runs):
seed = seeds[run]
print("Starting work on run: {}".format(run))
data = mnist(target, threshold=threshold)
data.create_semisupervised(num_labeled)
epoch2steps, epoch_decay = data.n_train / u_bs, 50
Data = SSL_DATA(data.x_unlabeled,
data.y_unlabeled,
x_test=data.x_test,
y_test=data.y_test,
x_labeled=data.x_labeled,
y_labeled=data.y_labeled,
dataset='mnist',
seed=seed)
n_x, n_y = Data.INPUT_DIM, Data.NUM_CLASSES
if modelName == 'm2':
# standard M2: Kingma et al. (2014)
model = m2(n_x,
def load_dataset(dataset_name, preproc=True, threshold=0.1, greyscale=False):
""" load up data, either mnist, cifar10, cifar100 or svhn
from mnist, optional arguments:
'threshold' keeps only elements of data where over the dataset their
variance > threshold. This is to prevent perfect matching to
pixels in e.g. the corners of the image that are always =0
for svhn, optional arguments:
'preproc' to do optional PCA extraction of data
'greyscale' to convert to greyscale images
"""
if dataset_name == 'mnist':
target = '/jmain01/home/JAD017/sjr01/mxw35-sjr01/Projects/generativeSSL-master/data/mnist.pkl.gz'
data = mnist(target, threshold=threshold)
x_train, y_train = data.x_train, data.y_train
x_test, y_test = data.x_test, data.y_test
binarize = True
x_dist = 'Bernoulli'
n_y = 10
f_enc, f_dec = lambda x: x, lambda x: x
n_x = x_train.shape[1]
elif dataset_name == 'cifar10':
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
binarize = False
x_dist = 'Gaussian'
n_y = 10
f_enc, f_dec = lambda x: x, lambda x: x
n_x = x_train.shape[1]
elif dataset_name == 'cifar100':
(x_train, y_train), (x_test, y_test) = cifar100.load_data()
binarize = False
x_dist = 'Gaussian'
n_y = 100
f_enc, f_dec = lambda x: x, lambda x: x
n_x = x_train.shape[1]
elif dataset_name == 'svhn':
train = scipy.io.loadmat('/jmain01/home/JAD017/sjr01/mxw35-sjr01/SVHN/train_32x32.mat')
train_x = train['X'].swapaxes(0, 1).T.reshape((train['X'].shape[3], -1)).T
train_y = train['y'].reshape((-1)) - 1
test = scipy.io.loadmat('/jmain01/home/JAD017/sjr01/mxw35-sjr01/SVHN/test_32x32.mat')
test_x = test['X'].swapaxes(0, 1).T.reshape((test['X'].shape[3], -1)).T
test_y = test['y'].reshape((-1)) - 1
extra = scipy.io.loadmat('/jmain01/home/JAD017/sjr01/mxw35-sjr01/SVHN/extra_32x32.mat')
extra_x = extra['X'].swapaxes(0, 1).T.reshape((extra['X'].shape[3], -1)).T
extra_y = extra['y'].reshape((-1)) - 1
train_x = train_x.astype(np.float32) / 256.
test_x = test_x.astype(np.float32) / 256.
extra_x = extra_x.astype(np.float32) / 256.
y_train = np_utils.to_categorical(train_y)
y_test = np_utils.to_categorical(test_y)
y_extra = np_utils.to_categorical(extra_y)
x_train = np.hstack((train_x, extra_x))
y_train = np.vstack((y_train, y_extra))
if greyscale is False:
binarize = False
x_dist = 'Gaussian'
elif greyscale is True:
x_train = rgb2gray(x_train).astype(np.float32)
test_x = rgb2gray(test_x).astype(np.float32)
n_y = 10
if preproc is True:
f_enc, f_dec, pca_params = pp.PCA(train_x[:, :10000],
cutoff=1000, toFloat=False)
x_train = f_enc(x_train).astype(np.float32).T
x_test = f_enc(test_x).astype(np.float32).T
else:
x_train = np.transpose(x_train)
x_test = np.transpose(test_x)
f_enc, f_dec = lambda x: x, lambda x: x
n_x = x_train.shape[1]
return x_train, y_train, x_test, y_test, binarize, x_dist, n_y, n_x, f_enc, f_dec
from collections import Counter
from matplotlib import rc
import pickle, sys, pdb, gzip, cPickle
import numpy as np
from sklearn.metrics import log_loss
import tensorflow as tf
from data.Container import Container as Data
from data.mnist import mnist
from models.bnn import bnn
### Script to run a BNN experiment over the moons data
seed = np.random.randint(1, 23523452)
threshold = 0.1
## load data
data = mnist(threshold=threshold)
data = Data(data.x_train,
data.y_train,
x_test=data.x_test,
y_test=data.y_test,
dataset='mnist',
seed=seed)
n_x, n_y = data.INPUT_DIM, data.NUM_CLASSES
## Specify model parameters
lr = (3e-4, )
n_hidden = [512, 512]
n_epochs, batchsize = 30, 64
initVar, eval_samps = -10.0, None
batchnorm = 'standard'