def test_dbm_loader():
""" Loads an example model and some data and makes
sure that inference gets the same result as Ruslan
Salakhutdino's demo code. """
pylearn2_path = pylearn2.__path__[0]
dbm_path = pylearn2_path + '/models/tests/dbm.mat'
data_path = pylearn2_path + '/models/tests/dbm_data.mat'
dbm = load_matlab_dbm(dbm_path)
d = io.loadmat(data_path)
for key in d:
try:
d[key] = np.cast[config.floatX](d[key])
except:
pass
ip = InferenceProcedure(layer_schedule=[0, 1] * 10)
ip.register_model(dbm)
V = T.matrix()
if config.compute_test_value != 'off':
V.tag.test_value = d['data']
h1_theano, h2_theano = ip.infer(V)['H_hat']
f = function([V], [h1_theano, h2_theano])
data = d['data']
h1_numpy, h2_numpy = f(data)
assert np.allclose(d['h1'], h1_numpy)
assert np.allclose(d['h2'], h2_numpy)
def test_dbm_loader():
""" Loads an example model and some data and makes
sure that inference gets the same result as Ruslan
Salakhutdino's demo code. """
pylearn2_path = pylearn2.__path__[0]
dbm_path = pylearn2_path + '/models/tests/dbm.mat'
data_path = pylearn2_path + '/models/tests/dbm_data.mat'
dbm = load_matlab_dbm(dbm_path)
d = io.loadmat( data_path )
for key in d:
try:
d[key] = np.cast[config.floatX](d[key])
except:
pass
ip = InferenceProcedure( layer_schedule = [0,1] * 10 )
ip.register_model(dbm)
V = T.matrix()
if config.compute_test_value != 'off':
V.tag.test_value = d['data']
h1_theano, h2_theano = ip.infer(V)['H_hat']
f = function([V],[h1_theano,h2_theano])
data = d['data']
h1_numpy, h2_numpy = f(data)
assert np.allclose(d['h1'],h1_numpy)
assert np.allclose(d['h2'],h2_numpy)
rows = 10
cols = 10
from pylearn2.models.dbm import load_matlab_dbm
model = load_matlab_dbm('after_joint_train.mat', num_chains=rows * cols)
#model = load_matlab_dbm('after_backprop.mat', num_chains = rows * cols)
dbm = model
from theano import function
import theano.tensor as T
sample_func = function([], updates=model.get_sampling_updates())
render_func = function(
[], T.nnet.sigmoid(T.dot(dbm.H_chains[0], dbm.W[0].T) + dbm.bias_vis))
from pylearn2.datasets.mnist import MNIST
dataset = MNIST(which_set='train')
X = dataset.get_batch_design(rows * cols)
model.V_chains.set_value(X)
for i in xrange(200):
print i
sample_func()
from pylearn2.gui.patch_viewer import make_viewer
pv = make_viewer(dataset.adjust_for_viewer(render_func()))
#arg1: layer 1 model
#arg2: layer 2 model
import sys
from pylearn2.utils import serial
import numpy as np
if len(sys.argv) == 3:
l1, l2= sys.argv[1:]
l1 = serial.load(l1)
dataset_yaml_src = l1.dataset_yaml_src
l2 = serial.load(l2)
else:
if sys.argv[1].endswith('.mat'):
from pylearn2.models.dbm import load_matlab_dbm
model = load_matlab_dbm(sys.argv[1])
model.dataset_yaml_src = "!obj:pylearn2.datasets.mnist.MNIST { which_set : 'train' }"
else:
model = serial.load(sys.argv[1])
dataset_yaml_src = model.dataset_yaml_src
try:
l1 = model.s3c
l2 = model.dbm.rbms[0]
except:
l1 , l2 = model.rbms
l1.W ,= l1.transformer.get_params()
W1 = l1.W.get_value()
l2_weights ,= l2.transformer.get_params()
W2 = l2_weights.get_value()
rows = 10
cols = 10
from pylearn2.models.dbm import load_matlab_dbm
model = load_matlab_dbm('joint_trained_dbm_230.mat', num_chains=rows * cols)
dbm = model
from theano import function
import theano.tensor as T
sample_func = function([], updates=model.get_sampling_updates())
render_func = function(
[], T.nnet.sigmoid(T.dot(dbm.H_chains[0], dbm.W[0].T) + dbm.bias_vis))
from pylearn2.datasets.mnist import MNIST
dataset = MNIST(which_set='train')
X = dataset.get_batch_design(rows * cols)
model.V_chains.set_value(X)
for i in xrange(200):
print i
sample_func()
from pylearn2.gui.patch_viewer import make_viewer
pv = make_viewer(dataset.adjust_for_viewer(render_func()))
pv.show()
rows = 10
cols = 10
from pylearn2.models.dbm import load_matlab_dbm
model = load_matlab_dbm('joint_trained_dbm_interm.mat', num_chains = rows * cols)
dbm = model
from theano import function
import theano.tensor as T
sample_func = function([],updates = model.get_sampling_updates())
render_func = function([],T.nnet.sigmoid(T.dot(dbm.H_chains[0],dbm.W[0].T)+dbm.bias_vis))
from pylearn2.datasets.mnist import MNIST
dataset = MNIST(which_set = 'train')
X = dataset.get_batch_design(rows*cols)
model.V_chains.set_value(X)
for i in xrange(200):
print i
sample_func()
from pylearn2.gui.patch_viewer import make_viewer
rows = 10
cols = 10
from pylearn2.models.dbm import load_matlab_dbm
model = load_matlab_dbm('after_joint_train.mat', num_chains = rows * cols)
#model = load_matlab_dbm('after_backprop.mat', num_chains = rows * cols)
dbm = model
from theano import function
import theano.tensor as T
sample_func = function([],updates = model.get_sampling_updates())
render_func = function([],T.nnet.sigmoid(T.dot(dbm.H_chains[0],dbm.W[0].T)+dbm.bias_vis))
from pylearn2.datasets.mnist import MNIST
dataset = MNIST(which_set = 'train')
X = dataset.get_batch_design(rows*cols)
model.V_chains.set_value(X)
for i in xrange(200):
print i
sample_func()
from pylearn2.gui.patch_viewer import make_viewer
rows = 10
cols = 10
from pylearn2.models.dbm import load_matlab_dbm
model = load_matlab_dbm('fullmnist_dbm.mat', num_chains = rows * cols)
dbm = model
from theano import function
import theano.tensor as T
sample_func = function([],updates = model.get_sampling_updates())
render_func = function([],T.nnet.sigmoid(T.dot(dbm.H_chains[0],dbm.W[0].T)+dbm.bias_vis))
from pylearn2.datasets.mnist import MNIST
dataset = MNIST(which_set = 'train')
X = dataset.get_batch_design(rows*cols)
model.V_chains.set_value(X)
for i in xrange(20000):
print i
sample_func()
from pylearn2.gui.patch_viewer import make_viewer