def main(n_z, n_hidden, dataset, seed, comment, gfx=True):
# Initialize logdir
#---------------------
# Setasouto:
# Create the directory to save the outputs files and log.
#---------------------
import time
logdir = 'results/gpulearn_z_x_' + dataset + '_' + str(n_z) + '-' + str(
n_hidden) + '_' + comment + '_' + str(int(time.time())) + '/'
if not os.path.exists(logdir): os.makedirs(logdir)
print('logdir:', logdir)
print('gpulearn_z_x', n_z, n_hidden, dataset, seed)
with open(logdir + 'hook.txt', 'a') as f:
print(f, 'learn_z_x', n_z, n_hidden, dataset, seed)
np.random.seed(seed)
gfx_freq = 1
weight_decay = 0
f_enc, f_dec = lambda x: x, lambda x: x
# Init data
if dataset == 'mnist':
import anglepy.data.mnist as mnist
# MNIST
size = 28
train_x, train_y, valid_x, valid_y, test_x, test_y = mnist.load_numpy(
size)
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': valid_x.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32)}
L_valid = 1
dim_input = (size, size)
n_x = size * size
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 50000
n_batch = 1000
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch) / n_train
if dataset == 'mnist_binarized':
import anglepy.data.mnist_binarized as mnist_binarized
# MNIST
train_x, valid_x, test_x = mnist_binarized.load_numpy(28)
x = {'x': np.hstack((train_x, valid_x)).astype(np.float32)}
x_valid = {'x': test_x.astype(np.float32)}
L_valid = 1
dim_input = (28, 28)
n_x = 28 * 28
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'mog'
nonlinear = 'rectlin'
type_px = 'bernoulli'
n_train = 60000
n_batch = 1000
colorImg = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch) / n_train
elif dataset == 'freyface':
# Frey's face
import anglepy.data.freyface as freyface
n_train = 1600
train_x = freyface.load_numpy()
np.random.shuffle(train_x)
x = {'x': train_x.T[:, 0:n_train]}
x_valid = {'x': train_x.T[:, n_train:]}
L_valid = 1
dim_input = (28, 20)
n_x = 20 * 28
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'bounded01'
nonlinear = 'tanh' #tanh works better with freyface #'softplus'
n_batch = 100
colorImg = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch) / n_train
elif dataset == 'freyface_pca':
# Frey's face
import anglepy.data.freyface as freyface
n_train = 1600
train_x = freyface.load_numpy().T
np.random.shuffle(train_x.T)
f_enc, f_dec, _ = pp.PCA(train_x, 0.99)
train_x = f_enc(train_x)
x = {'x': train_x[:, 0:n_train].astype(np.float32)}
x_valid = {'x': train_x[:, n_train:].astype(np.float32)}
L_valid = 1
dim_input = (28, 20)
n_x = train_x.shape[0]
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 100
colorImg = False
bernoulli_x = False
byteToFloat = False
elif dataset == 'freyface_bernoulli':
# Frey's face
import anglepy.data.freyface as freyface
n_train = 1600
train_x = freyface.load_numpy().T
np.random.shuffle(train_x.T)
x = {'x': train_x[:, 0:n_train].astype(np.float32)}
x_valid = {'x': train_x[:, n_train:].astype(np.float32)}
L_valid = 1
dim_input = (28, 20)
n_x = train_x.shape[0]
type_pz = 'gaussianmarg'
type_px = 'bernoulli'
nonlinear = 'softplus'
n_batch = 100
colorImg = False
bernoulli_x = False
byteToFloat = False
elif dataset == 'norb':
# small NORB dataset
import anglepy.data.norb as norb
size = 48
train_x, train_y, test_x, test_y = norb.load_resized(size,
binarize_y=True)
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': test_x.astype(np.float32)}
L_valid = 1
n_x = train_x.shape[0]
dim_input = (size, size)
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 900 #23400/900 = 27
colorImg = False
#binarize = False
byteToFloat = False
bernoulli_x = False
weight_decay = float(n_batch) / train_x.shape[1]
elif dataset == 'norb_pca':
# small NORB dataset
import anglepy.data.norb as norb
size = 48
train_x, train_y, test_x, test_y = norb.load_resized(size,
binarize_y=True)
f_enc, f_dec, _ = pp.PCA(train_x, 0.999)
#f_enc, f_dec, _ = pp.normalize_random(train_x)
train_x = f_enc(train_x)
test_x = f_enc(test_x)
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': test_x.astype(np.float32)}
L_valid = 1
n_x = train_x.shape[0]
dim_input = (size, size)
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 900 #23400/900 = 27
colorImg = False
#binarize = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch) / train_x.shape[1]
elif dataset == 'norb_normalized':
# small NORB dataset
import anglepy.data.norb as norb
size = 48
train_x, train_y, test_x, test_y = norb.load_resized(size,
binarize_y=True)
#f_enc, f_dec, _ = pp.PCA(train_x, 0.99)
#f_enc, f_dec, _ = pp.normalize_random(train_x)
f_enc, f_dec, _ = pp.normalize(train_x)
train_x = f_enc(train_x)
test_x = f_enc(test_x)
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': test_x.astype(np.float32)}
L_valid = 1
n_x = train_x.shape[0]
dim_input = (size, size)
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 900 #23400/900 = 27
colorImg = False
#binarize = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch) / train_x.shape[1]
elif dataset == 'svhn':
# SVHN dataset
import anglepy.data.svhn as svhn
size = 32
train_x, train_y, test_x, test_y = svhn.load_numpy(
False, binarize_y=True) #norb.load_resized(size, binarize_y=True)
extra_x, extra_y = svhn.load_numpy_extra(False, binarize_y=True)
x = {
'x': np.hstack((train_x, extra_x)),
'y': np.hstack((train_y, extra_y))
}
ndict.shuffleCols(x)
print('Performing PCA, can take a few minutes... ',
f_enc,
f_dec,
pca_params=pp.PCA(x['x'][:, :10000], cutoff=600, toFloat=True))
ndict.savez(pca_params, logdir + 'pca_params')
print('Done.')
n_y = 10
x = {'x': f_enc(x['x']).astype(np.float32)}
x_valid = {'x': f_enc(test_x).astype(np.float32)}
L_valid = 1
n_x = x['x'].shape[0]
dim_input = (size, size)
n_batch = 5000
colorImg = True
bernoulli_x = False
byteToFloat = False
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
elif dataset == 'hyper':
# Hyperspectral images:
# Import 1 file of the dataset
# TODO: import more files: Edit hyperspectralData.py
#I added the hyperspectralData file in the anglepy library
from hyperspectralData import HyperspectralData
train_x, train_y, valid_x, valid_y, test_x, test_y = HyperspectralData(
).load_numpy(100000)
#Dim input: How it has to be written like an image. We said that is:
dim_input = (67, 4)
n_x = train_x.shape[0] #Dimension of our data vector.
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': valid_x.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32)}
L_valid = 1
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = train_x.shape[1]
n_batch = 1000
colorImg = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch) / n_train
#Write the hyperparameters used:
with open(logdir + 'AA_hyperparameters.txt', 'w') as file:
file.write("L_valid: " + str(L_valid) + '\n')
file.write("type_qz: " + type_qz + '\n')
file.write("type_pz: " + type_pz + '\n')
file.write("Nonlinear: " + nonlinear + '\n')
file.write("type_px: " + type_px + '\n')
file.write("n_train: " + str(n_train) + '\n')
file.write("n_batch: " + str(n_batch) + '\n')
file.write("colorImg: " + str(colorImg) + '\n')
file.write("bernoulli_x: " + str(bernoulli_x) + '\n')
file.write("byteToFloat: " + str(byteToFloat) + '\n')
file.close()
# Write the headers for the csv file output:
with open(logdir + 'AA_results.txt', 'w') as file:
# Like a csv file:
file.write("Step" + ',' + "TimeElapsed" + ',' +
"LowerboundMinibatch" + ',' + "LowerboundValid" + ',' +
"NumStepNotImproving" + '\n')
file.close()
# Construct model
from anglepy.models import GPUVAE_Z_X
updates = get_adam_optimizer(learning_rate=3e-4, weight_decay=weight_decay)
model = GPUVAE_Z_X(updates,
n_x,
n_hidden,
n_z,
n_hidden[::-1],
nonlinear,
nonlinear,
type_px,
type_qz=type_qz,
type_pz=type_pz,
prior_sd=100,
init_sd=1e-3)
#---------------
# SetaSouto:
# The [::-1] is to reverse the list.
#---------------
if False:
#dir = '/Users/dpkingma/results/learn_z_x_mnist_binarized_50-(500, 500)_mog_1412689061/'
#dir = '/Users/dpkingma/results/learn_z_x_svhn_bernoulli_300-(1000, 1000)_l1l2_sharing_and_1000HU_1412676966/'
#dir = '/Users/dpkingma/results/learn_z_x_svhn_bernoulli_300-(1000, 1000)_l1l2_sharing_and_1000HU_1412695481/'
#dir = '/Users/dpkingma/results/learn_z_x_mnist_binarized_50-(500, 500)_mog_1412695455/'
#dir = '/Users/dpkingma/results/gpulearn_z_x_svhn_pca_300-(500, 500)__1413904756/'
dir = '/home/ubuntu/results/gpulearn_z_x_mnist_50-[500, 500]__1414259423/'
w = ndict.loadz(dir + 'w_best.ndict.tar.gz')
v = ndict.loadz(dir + 'v_best.ndict.tar.gz')
ndict.set_value(model.w, w)
ndict.set_value(model.v, v)
# Some statistics for optimization
ll_valid_stats = [-1e99, 0]
# Progress hook
def hook(epoch, t, ll):
'''
Documented by SetaSouto, may contains errors.
:epoch: Number of the current step.
:t: Time elapsed from the beginning.
:ll: Loglikelihood (?).
'''
if epoch % 10 != 0: return
ll_valid, _ = model.est_loglik(x_valid,
n_samples=L_valid,
n_batch=n_batch,
byteToFloat=byteToFloat)
# Log
ndict.savez(ndict.get_value(model.v), logdir + 'v')
ndict.savez(ndict.get_value(model.w), logdir + 'w')
if ll_valid > ll_valid_stats[0]:
ll_valid_stats[0] = ll_valid
ll_valid_stats[1] = 0
ndict.savez(ndict.get_value(model.v), logdir + 'v_best')
ndict.savez(ndict.get_value(model.w), logdir + 'w_best')
else:
ll_valid_stats[1] += 1
# Stop when not improving validation set performance in 100 iterations
if ll_valid_stats[1] > 100:
print("Finished")
with open(logdir + 'hook.txt', 'a') as f:
print(f, "Finished")
exit()
# This will be showing the current results and write them in a file:
with open(logdir + 'AA_results.txt', 'a') as file:
# Like a csv file:
file.write(
str(epoch) + ',' + str(t) + ',' + str(ll) + ',' +
str(ll_valid) + ',' + str(ll_valid_stats[1]) + '\n')
file.close()
print("-------------------------")
print("Current results:")
print(" ")
print("Step:", epoch)
print("Time elapsed:", t)
print("Loglikelihood minibatch:", ll)
print("Loglikelihood validSet:", ll_valid)
print("N not improving:", ll_valid_stats[1])
#print(epoch, t, ll, ll_valid, ll_valid_stats)
#This print the file where are written the stats.
#with open(logdir+'hook.txt', 'a') as f:
#print(f, epoch, t, ll, ll_valid, ll_valid_stats)
# Graphics
if gfx and epoch % gfx_freq == 0:
#tail = '.png'
tail = '-' + str(epoch) + '.png'
v = {i: model.v[i].get_value() for i in model.v}
w = {i: model.w[i].get_value() for i in model.w}
if 'pca' not in dataset and 'random' not in dataset and 'normalized' not in dataset:
if 'w0' in v:
image = paramgraphics.mat_to_img(f_dec(v['w0'][:].T),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'q_w0' + tail, 'PNG')
image = paramgraphics.mat_to_img(f_dec(w['out_w'][:]),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'out_w' + tail, 'PNG')
if 'out_unif' in w:
image = paramgraphics.mat_to_img(f_dec(
w['out_unif'].reshape((-1, 1))),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'out_unif' + tail, 'PNG')
if n_z == 2:
n_width = 10
import scipy.stats
z = {'z': np.zeros((2, n_width**2))}
for i in range(0, n_width):
for j in range(0, n_width):
z['z'][0, n_width * i + j] = scipy.stats.norm.ppf(
float(i) / n_width + 0.5 / n_width)
z['z'][1, n_width * i + j] = scipy.stats.norm.ppf(
float(j) / n_width + 0.5 / n_width)
x, _, _z = model.gen_xz({}, z, n_width**2)
if dataset == 'mnist':
x = 1 - _z['x']
image = paramgraphics.mat_to_img(f_dec(_z['x']), dim_input)
image.save(logdir + '2dmanifold' + tail, 'PNG')
else:
_x, _, _z_confab = model.gen_xz({}, {}, n_batch=144)
x_samples = _z_confab['x']
image = paramgraphics.mat_to_img(f_dec(x_samples),
dim_input,
colorImg=colorImg)
image.save(logdir + 'samples' + tail, 'PNG')
#x_samples = _x['x']
#image = paramgraphics.mat_to_img(x_samples, dim_input, colorImg=colorImg)
#image.save(logdir+'samples2'+tail, 'PNG')
else:
# Model with preprocessing
if 'w0' in v:
image = paramgraphics.mat_to_img(f_dec(v['w0'][:].T),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'q_w0' + tail, 'PNG')
image = paramgraphics.mat_to_img(f_dec(w['out_w'][:]),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'out_w' + tail, 'PNG')
_x, _, _z_confab = model.gen_xz({}, {}, n_batch=144)
x_samples = f_dec(_z_confab['x'])
x_samples = np.minimum(np.maximum(x_samples, 0), 1)
image = paramgraphics.mat_to_img(x_samples,
dim_input,
colorImg=colorImg)
image.save(logdir + 'samples' + tail, 'PNG')
# Optimize
#SFO
dostep = epoch_vae_adam(model,
x,
n_batch=n_batch,
bernoulli_x=bernoulli_x,
byteToFloat=byteToFloat)
loop_va(dostep, hook)
pass
def main(n_z, n_hidden, dataset, seed, comment, gfx=True):
# Initialize logdir
import time
logdir = 'results/gpulearn_z_x_' + dataset + '_' + str(n_z) + '-' + str(
n_hidden) + '_' + comment + '_' + str(int(time.time())) + '/'
if not os.path.exists(logdir): os.makedirs(logdir)
print 'logdir:', logdir
print 'gpulearn_z_x', n_z, n_hidden, dataset, seed
with open(logdir + 'hook.txt', 'a') as f:
print >> f, 'learn_z_x', n_z, n_hidden, dataset, seed
np.random.seed(seed)
gfx_freq = 1
weight_decay = 0
f_enc, f_dec = lambda x: x, lambda x: x
# Init data
if dataset == 'mnist':
import anglepy.data.mnist as mnist
# MNIST
size = 28
train_x, train_y, valid_x, valid_y, test_x, test_y = mnist.load_numpy(
size)
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': valid_x.astype(np.float32)}
x_test = {'x': test_x.astype(np.float32)}
L_valid = 1
dim_input = (size, size)
n_x = size * size
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
nonlinear = 'softplus'
type_px = 'bernoulli'
n_train = 50000
n_batch = 1000
colorImg = False
bernoulli_x = True
byteToFloat = False
weight_decay = float(n_batch) / n_train
if dataset == 'mnist_binarized':
import anglepy.data.mnist_binarized as mnist_binarized
# MNIST
train_x, valid_x, test_x = mnist_binarized.load_numpy(28)
x = {'x': np.hstack((train_x, valid_x)).astype(np.float32)}
x_valid = {'x': test_x.astype(np.float32)}
L_valid = 1
dim_input = (28, 28)
n_x = 28 * 28
n_y = 10
type_qz = 'gaussianmarg'
type_pz = 'mog'
nonlinear = 'rectlin'
type_px = 'bernoulli'
n_train = 60000
n_batch = 1000
colorImg = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch) / n_train
elif dataset == 'freyface':
# Frey's face
import anglepy.data.freyface as freyface
n_train = 1600
train_x = freyface.load_numpy()
np.random.shuffle(train_x)
x = {'x': train_x.T[:, 0:n_train]}
x_valid = {'x': train_x.T[:, n_train:]}
L_valid = 1
dim_input = (28, 20)
n_x = 20 * 28
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'bounded01'
nonlinear = 'tanh' #tanh works better with freyface #'softplus'
n_batch = 100
colorImg = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch) / n_train
elif dataset == 'freyface_pca':
# Frey's face
import anglepy.data.freyface as freyface
n_train = 1600
train_x = freyface.load_numpy().T
np.random.shuffle(train_x.T)
f_enc, f_dec, _ = pp.PCA(train_x, 0.99)
train_x = f_enc(train_x)
x = {'x': train_x[:, 0:n_train].astype(np.float32)}
x_valid = {'x': train_x[:, n_train:].astype(np.float32)}
L_valid = 1
dim_input = (28, 20)
n_x = train_x.shape[0]
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 100
colorImg = False
bernoulli_x = False
byteToFloat = False
elif dataset == 'freyface_bernoulli':
# Frey's face
import anglepy.data.freyface as freyface
n_train = 1600
train_x = freyface.load_numpy().T
np.random.shuffle(train_x.T)
x = {'x': train_x[:, 0:n_train].astype(np.float32)}
x_valid = {'x': train_x[:, n_train:].astype(np.float32)}
L_valid = 1
dim_input = (28, 20)
n_x = train_x.shape[0]
type_pz = 'gaussianmarg'
type_px = 'bernoulli'
nonlinear = 'softplus'
n_batch = 100
colorImg = False
bernoulli_x = False
byteToFloat = False
elif dataset == 'norb':
# small NORB dataset
import anglepy.data.norb as norb
size = 48
train_x, train_y, test_x, test_y = norb.load_resized(size,
binarize_y=True)
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': test_x.astype(np.float32)}
L_valid = 1
n_x = train_x.shape[0]
dim_input = (size, size)
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 900 #23400/900 = 27
colorImg = False
#binarize = False
byteToFloat = False
bernoulli_x = False
weight_decay = float(n_batch) / train_x.shape[1]
elif dataset == 'norb_pca':
# small NORB dataset
import anglepy.data.norb as norb
size = 48
train_x, train_y, test_x, test_y = norb.load_resized(size,
binarize_y=True)
f_enc, f_dec, _ = pp.PCA(train_x, 0.999)
#f_enc, f_dec, _ = pp.normalize_random(train_x)
train_x = f_enc(train_x)
test_x = f_enc(test_x)
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': test_x.astype(np.float32)}
L_valid = 1
n_x = train_x.shape[0]
dim_input = (size, size)
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 900 #23400/900 = 27
colorImg = False
#binarize = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch) / train_x.shape[1]
elif dataset == 'norb_normalized':
# small NORB dataset
import anglepy.data.norb as norb
size = 48
train_x, train_y, test_x, test_y = norb.load_resized(size,
binarize_y=True)
#f_enc, f_dec, _ = pp.PCA(train_x, 0.99)
#f_enc, f_dec, _ = pp.normalize_random(train_x)
f_enc, f_dec, _ = pp.normalize(train_x)
train_x = f_enc(train_x)
test_x = f_enc(test_x)
x = {'x': train_x.astype(np.float32)}
x_valid = {'x': test_x.astype(np.float32)}
L_valid = 1
n_x = train_x.shape[0]
dim_input = (size, size)
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
n_batch = 900 #23400/900 = 27
colorImg = False
#binarize = False
bernoulli_x = False
byteToFloat = False
weight_decay = float(n_batch) / train_x.shape[1]
elif dataset == 'svhn':
# SVHN dataset
import anglepy.data.svhn as svhn
size = 32
train_x, train_y, test_x, test_y = svhn.load_numpy(
False, binarize_y=True) #norb.load_resized(size, binarize_y=True)
extra_x, extra_y = svhn.load_numpy_extra(False, binarize_y=True)
x = {
'x': np.hstack((train_x, extra_x)),
'y': np.hstack((train_y, extra_y))
}
ndict.shuffleCols(x)
print 'Performing PCA, can take a few minutes... ',
f_enc, f_dec, pca_params = pp.PCA(x['x'][:, :10000],
cutoff=600,
toFloat=True)
ndict.savez(pca_params, logdir + 'pca_params')
print 'Done.'
n_y = 10
x = {'x': f_enc(x['x']).astype(np.float32)}
x_valid = {'x': f_enc(test_x).astype(np.float32)}
L_valid = 1
n_x = x['x'].shape[0]
dim_input = (size, size)
n_batch = 5000
colorImg = True
bernoulli_x = False
byteToFloat = False
type_qz = 'gaussianmarg'
type_pz = 'gaussianmarg'
type_px = 'gaussian'
nonlinear = 'softplus'
# Construct model
from anglepy.models import GPUVAE_Z_X
updates = get_adam_optimizer(learning_rate=3e-4, weight_decay=weight_decay)
model = GPUVAE_Z_X(updates,
n_x,
n_hidden,
n_z,
n_hidden[::-1],
nonlinear,
nonlinear,
type_px,
type_qz=type_qz,
type_pz=type_pz,
prior_sd=100,
init_sd=1e-3)
if False:
#dir = '/Users/dpkingma/results/learn_z_x_mnist_binarized_50-(500, 500)_mog_1412689061/'
#dir = '/Users/dpkingma/results/learn_z_x_svhn_bernoulli_300-(1000, 1000)_l1l2_sharing_and_1000HU_1412676966/'
#dir = '/Users/dpkingma/results/learn_z_x_svhn_bernoulli_300-(1000, 1000)_l1l2_sharing_and_1000HU_1412695481/'
#dir = '/Users/dpkingma/results/learn_z_x_mnist_binarized_50-(500, 500)_mog_1412695455/'
#dir = '/Users/dpkingma/results/gpulearn_z_x_svhn_pca_300-(500, 500)__1413904756/'
dir = '/home/ubuntu/results/gpulearn_z_x_mnist_50-[500, 500]__1414259423/'
w = ndict.loadz(dir + 'w_best.ndict.tar.gz')
v = ndict.loadz(dir + 'v_best.ndict.tar.gz')
ndict.set_value(model.w, w)
ndict.set_value(model.v, v)
# Some statistics for optimization
ll_valid_stats = [-1e99, 0]
# Progress hook
def hook(epoch, t, ll):
if epoch % 10 != 0: return
ll_valid, _ = model.est_loglik(x_valid,
n_samples=L_valid,
n_batch=n_batch,
byteToFloat=byteToFloat)
# Log
ndict.savez(ndict.get_value(model.v), logdir + 'v')
ndict.savez(ndict.get_value(model.w), logdir + 'w')
if ll_valid > ll_valid_stats[0]:
ll_valid_stats[0] = ll_valid
ll_valid_stats[1] = 0
ndict.savez(ndict.get_value(model.v), logdir + 'v_best')
ndict.savez(ndict.get_value(model.w), logdir + 'w_best')
else:
ll_valid_stats[1] += 1
# Stop when not improving validation set performance in 100 iterations
if ll_valid_stats[1] > 1000:
print "Finished"
with open(logdir + 'hook.txt', 'a') as f:
print >> f, "Finished"
exit()
print epoch, t, ll, ll_valid, ll_valid_stats
with open(logdir + 'hook.txt', 'a') as f:
print >> f, epoch, t, ll, ll_valid, ll_valid_stats
# Graphics
if gfx and epoch % gfx_freq == 0:
#tail = '.png'
tail = '-' + str(epoch) + '.png'
v = {i: model.v[i].get_value() for i in model.v}
w = {i: model.w[i].get_value() for i in model.w}
if 'pca' not in dataset and 'random' not in dataset and 'normalized' not in dataset:
if 'w0' in v:
image = paramgraphics.mat_to_img(f_dec(v['w0'][:].T),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'q_w0' + tail, 'PNG')
image = paramgraphics.mat_to_img(f_dec(w['out_w'][:]),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'out_w' + tail, 'PNG')
if 'out_unif' in w:
image = paramgraphics.mat_to_img(f_dec(
w['out_unif'].reshape((-1, 1))),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'out_unif' + tail, 'PNG')
if n_z == 2:
n_width = 10
import scipy.stats
z = {'z': np.zeros((2, n_width**2))}
for i in range(0, n_width):
for j in range(0, n_width):
z['z'][0, n_width * i + j] = scipy.stats.norm.ppf(
float(i) / n_width + 0.5 / n_width)
z['z'][1, n_width * i + j] = scipy.stats.norm.ppf(
float(j) / n_width + 0.5 / n_width)
x, _, _z = model.gen_xz({}, z, n_width**2)
if dataset == 'mnist':
x = 1 - _z['x']
image = paramgraphics.mat_to_img(f_dec(_z['x']), dim_input)
image.save(logdir + '2dmanifold' + tail, 'PNG')
else:
_x, _, _z_confab = model.gen_xz({}, {}, n_batch=144)
x_samples = _z_confab['x']
image = paramgraphics.mat_to_img(f_dec(x_samples),
dim_input,
colorImg=colorImg)
image.save(logdir + 'samples' + tail, 'PNG')
#x_samples = _x['x']
#image = paramgraphics.mat_to_img(x_samples, dim_input, colorImg=colorImg)
#image.save(logdir+'samples2'+tail, 'PNG')
else:
# Model with preprocessing
if 'w0' in v:
image = paramgraphics.mat_to_img(f_dec(v['w0'][:].T),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'q_w0' + tail, 'PNG')
image = paramgraphics.mat_to_img(f_dec(w['out_w'][:]),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'out_w' + tail, 'PNG')
_x, _, _z_confab = model.gen_xz({}, {}, n_batch=144)
x_samples = f_dec(_z_confab['x'])
x_samples = np.minimum(np.maximum(x_samples, 0), 1)
image = paramgraphics.mat_to_img(x_samples,
dim_input,
colorImg=colorImg)
image.save(logdir + 'samples' + tail, 'PNG')
# Optimize
#SFO
dostep = epoch_vae_adam(model,
x,
n_batch=n_batch,
bernoulli_x=bernoulli_x,
byteToFloat=byteToFloat)
loop_va(dostep, hook)
pass
def main(n_z, n_hidden, dataset, seed, gfx=True, _size=None):
'''Learn a variational auto-encoder with generative model p(x,y,z)=p(y)p(z)p(x|y,z).
x and y are (always) observed.
I.e. this cannot be used for semi-supervised learning
'''
assert (type(n_hidden) == tuple or type(n_hidden) == list)
assert type(n_z) == int
assert isinstance(dataset, str)
print('gpulearn_yz_x', n_z, n_hidden, dataset, seed)
import time
logdir = 'results/gpulearn_yz_x_' + dataset + '_' + str(n_z) + '-' + str(
n_hidden) + '-' + str(int(time.time())) + '/'
if not os.path.exists(logdir): os.makedirs(logdir)
print('logdir:', logdir)
np.random.seed(seed)
# Init data
if dataset == 'mnist':
'''
What works well:
100-2-100 (Generated digits stay bit shady)
1000-2-1000 (Needs pretty long training)
'''
import anglepy.data.mnist as mnist
# MNIST
size = 28
train_x, train_y, valid_x, valid_y, test_x, test_y = mnist.load_numpy(
size, binarize_y=True)
f_enc, f_dec = lambda x: x, lambda x: x
x = {
'x': train_x[:, :].astype(np.float32),
'y': train_y[:, :].astype(np.float32)
}
x_valid = {
'x': valid_x.astype(np.float32),
'y': valid_y.astype(np.float32)
}
L_valid = 1
dim_input = (size, size)
n_x = size * size
n_y = 10
n_batch = 1000
colorImg = False
bernoulli_x = True
byteToFloat = False
mosaic_w = 5
mosaic_h = 2
type_px = 'bernoulli'
elif dataset == 'norb':
# resized NORB dataset, reshuffled
import anglepy.data.norb as norb
size = _size #48
train_x, train_y, test_x, test_y = norb.load_resized(size,
binarize_y=True)
_x = {'x': train_x, 'y': train_y}
ndict.shuffleCols(_x)
train_x = _x['x']
train_y = _x['y']
# Do PCA
f_enc, f_dec, pca_params = pp.PCA(_x['x'][:, :10000],
cutoff=2000,
toFloat=False)
ndict.savez(pca_params, logdir + 'pca_params')
x = {
'x': f_enc(train_x).astype(np.float32),
'y': train_y.astype(np.float32)
}
x_valid = {
'x': f_enc(test_x).astype(np.float32),
'y': test_y.astype(np.float32)
}
L_valid = 1
n_x = x['x'].shape[0]
n_y = 5
dim_input = (size, size)
n_batch = 1000 #23400/900 = 27
colorImg = False
bernoulli_x = False
byteToFloat = False
mosaic_w = 5
mosaic_h = 1
type_px = 'gaussian'
elif dataset == 'norb_instances':
# resized NORB dataset with the instances as classes
import anglepy.data.norb2 as norb2
size = _size #48
x, y = norb2.load_numpy_subclasses(size, binarize_y=True)
_x = {'x': x, 'y': y}
ndict.shuffleCols(_x)
# Do pre=processing
if True:
# Works
f_enc, f_dec, pca_params = pp.PCA(_x['x'][:, :10000],
cutoff=600,
global_sd=True,
toFloat=True)
ndict.savez(pca_params, logdir + 'pca_params')
elif False:
# Doesn't work
f_enc, f_dec, pp_params = pp.normalize_noise(_x['x'][:, :50000],
noise_sd=0.01,
global_sd=True,
toFloat=True)
else:
# Doesn't work
f_enc, f_dec, params = pp.normalize_random(x=x[:, :10000],
global_sd=True,
toFloat=True)
ndict.savez(params, logdir + 'normalize_random_params')
n_valid = 5000
x = {
'x': f_enc(_x['x'][:, :-n_valid]).astype(np.float32),
'y': _x['y'][:, :-n_valid].astype(np.float32)
}
x_valid = {
'x': f_enc(_x['x'][:, :n_valid]).astype(np.float32),
'y': _x['y'][:, :n_valid].astype(np.float32)
}
L_valid = 1
n_x = x['x'].shape[0]
n_y = 50
dim_input = (size, size)
n_batch = 5000 #23400/900 = 27
colorImg = False
bernoulli_x = False
byteToFloat = False
mosaic_w = 5
mosaic_h = 1
type_px = 'gaussian'
elif dataset == 'svhn':
# SVHN dataset
import anglepy.data.svhn as svhn
size = 32
train_x, train_y, test_x, test_y = svhn.load_numpy(
False, binarize_y=True) #norb.load_resized(size, binarize_y=True)
extra_x, extra_y = svhn.load_numpy_extra(False, binarize_y=True)
x = {
'x': np.hstack((train_x, extra_x)),
'y': np.hstack((train_y, extra_y))
}
ndict.shuffleCols(x)
#f_enc, f_dec, (x_sd, x_mean) = pp.preprocess_normalize01(train_x, True)
f_enc, f_dec, pca_params = pp.PCA(x['x'][:, :10000],
cutoff=1000,
toFloat=True)
ndict.savez(pca_params, logdir + 'pca_params')
n_y = 10
x = {
'x': f_enc(x['x']).astype(np.float32),
'y': x['y'].astype(np.float32)
}
x_valid = {
'x': f_enc(test_x).astype(np.float32),
'y': test_y.astype(np.float32)
}
L_valid = 1
n_x = x['x'].shape[0]
dim_input = (size, size)
n_batch = 5000
colorImg = True
bernoulli_x = False
byteToFloat = False
mosaic_w = 5
mosaic_h = 2
type_px = 'gaussian'
# Init model
n_hidden_q = n_hidden
n_hidden_p = n_hidden
from anglepy.models import GPUVAE_YZ_X
updates = get_adam_optimizer(alpha=3e-4,
beta1=0.9,
beta2=0.999,
weight_decay=0)
model = GPUVAE_YZ_X(updates,
n_x,
n_y,
n_hidden_q,
n_z,
n_hidden_p[::-1],
'softplus',
'softplus',
type_px=type_px,
type_qz='gaussianmarg',
type_pz='gaussianmarg',
prior_sd=1,
uniform_y=True)
if False:
dir = '/home/ubuntu/results/gpulearn_yz_x_svhn_300-(500, 500)-1414094291/'
dir = '/home/ubuntu/results/gpulearn_yz_x_svhn_300-(500, 500)-1414163488/'
w = ndict.loadz(dir + 'w_best.ndict.tar.gz')
v = ndict.loadz(dir + 'v_best.ndict.tar.gz')
ndict.set_value(model.w, w)
ndict.set_value(model.v, v)
# Some statistics for optimization
ll_valid_stats = [-1e99, 0]
# Fixed sample for visualisation
z_sample = {
'z':
np.repeat(np.random.standard_normal(size=(n_z, 12)), 12,
axis=1).astype(np.float32)
}
y_sample = {
'y':
np.tile(
np.random.multinomial(1, [1. / n_y] * n_y, size=12).T, (1, 12))
}
# Progress hook
def hook(epoch, t, ll):
if epoch % 10 != 0:
return
ll_valid, _ = model.est_loglik(x_valid,
n_samples=L_valid,
n_batch=n_batch,
byteToFloat=byteToFloat)
if math.isnan(ll_valid):
print("NaN detected. Reverting to saved best parameters")
ndict.set_value(model.v, ndict.loadz(logdir + 'v.ndict.tar.gz'))
ndict.set_value(model.w, ndict.loadz(logdir + 'w.ndict.tar.gz'))
return
if ll_valid > ll_valid_stats[0]:
ll_valid_stats[0] = ll_valid
ll_valid_stats[1] = 0
ndict.savez(ndict.get_value(model.v), logdir + 'v_best')
ndict.savez(ndict.get_value(model.w), logdir + 'w_best')
else:
ll_valid_stats[1] += 1
# Stop when not improving validation set performance in 100 iterations
if False and ll_valid_stats[1] > 1000:
print("Finished")
with open(logdir + 'hook.txt', 'a') as f:
print("Finished", file=f)
exit()
# Log
ndict.savez(ndict.get_value(model.v), logdir + 'v')
ndict.savez(ndict.get_value(model.w), logdir + 'w')
print(epoch, t, ll, ll_valid)
with open(logdir + 'hook.txt', 'a') as f:
print(t, ll, ll_valid, file=f)
if gfx:
# Graphics
v = {i: model.v[i].get_value() for i in model.v}
w = {i: model.w[i].get_value() for i in model.w}
tail = '-' + str(epoch) + '.png'
image = paramgraphics.mat_to_img(f_dec(v['w0x'][:].T),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'q_w0x' + tail, 'PNG')
image = paramgraphics.mat_to_img(f_dec(w['out_w'][:]),
dim_input,
True,
colorImg=colorImg)
image.save(logdir + 'out_w' + tail, 'PNG')
_x = {'y': np.random.multinomial(1, [1. / n_y] * n_y, size=144).T}
_, _, _z_confab = model.gen_xz(_x, {}, n_batch=144)
image = paramgraphics.mat_to_img(f_dec(_z_confab['x']),
dim_input,
colorImg=colorImg)
image.save(logdir + 'samples' + tail, 'PNG')
_, _, _z_confab = model.gen_xz(y_sample, z_sample, n_batch=144)
image = paramgraphics.mat_to_img(f_dec(_z_confab['x']),
dim_input,
colorImg=colorImg)
image.save(logdir + 'samples_fixed' + tail, 'PNG')
if n_z == 2:
import Image
import ImageFont
import ImageDraw
n_width = 10
submosaic_offset = 15
submosaic_width = (dim_input[1] * n_width)
submosaic_height = (dim_input[0] * n_width)
mosaic = Image.new(
"RGB", (submosaic_width * mosaic_w,
submosaic_offset + submosaic_height * mosaic_h))
for digit in range(0, n_y):
if digit >= mosaic_h * mosaic_w: continue
_x = {}
n_batch_plot = n_width * n_width
_x['y'] = np.zeros((n_y, n_batch_plot))
_x['y'][digit, :] = 1
_z = {'z': np.zeros((2, n_width**2))}
for i in range(0, n_width):
for j in range(0, n_width):
_z['z'][0, n_width * i + j] = scipy.stats.norm.ppf(
float(i) / n_width + 0.5 / n_width)
_z['z'][1, n_width * i + j] = scipy.stats.norm.ppf(
float(j) / n_width + 0.5 / n_width)
_x, _, _z_confab = model.gen_xz(_x,
_z,
n_batch=n_batch_plot)
x_samples = _z_confab['x']
image = paramgraphics.mat_to_img(f_dec(x_samples),
dim_input,
colorImg=colorImg,
tile_spacing=(0, 0))
#image.save(logdir+'samples_digit_'+str(digit)+'_'+tail, 'PNG')
mosaic_x = (digit % mosaic_w) * submosaic_width
mosaic_y = submosaic_offset + int(
digit / mosaic_w) * submosaic_height
mosaic.paste(image, (mosaic_x, mosaic_y))
draw = ImageDraw.Draw(mosaic)
draw.text((1, 1),
"Epoch #" + str(epoch) + " Loss=" + str(int(ll)))
#plt.savefig(logdir+'mosaic'+tail, format='PNG')
mosaic.save(logdir + 'mosaic' + tail, 'PNG')
#x_samples = _x['x']
#image = paramgraphics.mat_to_img(f_dec(x_samples), dim_input, colorImg=colorImg)
#image.save(logdir+'samples2'+tail, 'PNG')
# Optimize
dostep = epoch_vae_adam(model,
x,
n_batch=n_batch,
bernoulli_x=bernoulli_x,
byteToFloat=byteToFloat)
loop_va(dostep, hook)
pass