def test_storage(self):
path = "association_test_2"
store.create_path(path)
self.assertTrue(os.path.isdir('/'.join([store.data_dir, path])))
path = "association_test_3"
store.move_to(path)
self.assertTrue(os.getcwd() == '/'.join([store.data_dir, path]))
store.move_to_root()
self.assertTrue(os.getcwd() == store.root_dir)
store.move_to(path)
rbm = RBM.RBM()
store.store_object(rbm)
rbm2 = store.retrieve_object(str(rbm))
self.assertTrue(str(rbm) == str(rbm2))
dbn = DBN.DBN()
store.store_object(dbn)
dbn2 = store.retrieve_object(str(dbn))
self.assertTrue(str(dbn) == str(dbn2))
store.move_to_root()
def test_set_up(self):
tr = rbm_config.TrainParam(learning_rate=0.05,
momentum_type=rbm_config.CLASSICAL,
momentum=0.5,
weight_decay=0,
sparsity_constraint=False,
sparsity_target=0.1**9,
sparsity_cost=10**8,
sparsity_decay=0.9,
epochs=5)
config = rbm_config.RBMConfig()
config.v_n = 3
config.h_n = 2
config.v_unit = rbm_units.GaussianVisibleUnit
config.progress_logger = rbm_logger.ProgressLogger()
config.train_params = tr
np_rand = np.random.RandomState(123)
# Weights
W = np_rand.uniform(low=-1. / 10, high=-1. / 10,
size=(3, 2)).astype(np.float32)
vb = np.array([-0.1, 0, 0.1], dtype=np.float32)
hb = np.array([0.01, -0.01], dtype=np.float32)
Wt = theano.shared(W, name='W')
vbt = theano.shared(vb, name='vbias')
hbt = theano.shared(hb, name='hbias')
g_rbm = rbm.RBM(config, W=Wt, h_bias=hbt, v_bias=vbt)
self.assertTrue(g_rbm)
self.assertTrue(isinstance(g_rbm.v_unit,
rbm_units.GaussianVisibleUnit))
self.assertTrue(isinstance(g_rbm.h_unit, rbm_units.RBMUnit))
self.assertTrue(
np.count_nonzero(g_rbm.W.get_value(borrow=True) - W) == 0)
self.assertTrue(
np.count_nonzero(g_rbm.v_bias.get_value(borrow=True) - vb) == 0)
self.assertTrue(
np.count_nonzero(g_rbm.h_bias.get_value(borrow=True) - hb) == 0)
x = sklearn.preprocessing.scale(
np.array([[200.0, 188., 7.], [150.0, 128., 0.], [250.0, 98., 3.]],
dtype=theano.config.floatX))
v = theano.shared(x)
_, _, h = g_rbm.sample_h_given_v(v)
_, _, vs = g_rbm.sample_v_given_h(h)
_, _, hs = g_rbm.sample_h_given_v(vs)
dw = T.dot(v.T, h) - T.dot(vs.T, hs)
dv = T.sum(v - vs, axis=0)
dh = T.sum(h - hs, axis=0)
gr = g_rbm.get_partial_derivatives(v, None)['gradients']
gdw, gdv, gdh = gr[0], gr[1], gr[2]
print gdw, gdv, gdh
compute_derivative = theano.function([], [dw, dv, dh, gdw, gdv, gdh])
for i in xrange(20):
a, b, c, d, e, f = compute_derivative()
# print a, b, c
print d, e, f
def test_digits(self):
tr = rbm_config.TrainParam(learning_rate=0.05,
momentum_type=rbm_config.CLASSICAL,
momentum=0.5,
weight_decay=0,
sparsity_constraint=False,
sparsity_target=0.1**9,
sparsity_cost=10**8,
sparsity_decay=0.9,
epochs=5)
config = rbm_config.RBMConfig()
config.v_n = 784
config.h_n = 100
config.v_unit = rbm_units.GaussianVisibleUnit
config.h_unit = rbm_units.ReLUnit
config.progress_logger = rbm_logger.ProgressLogger()
config.train_params = tr
np_rand = np.random.RandomState(123)
# Weights
W = np_rand.uniform(low=-1. / 10, high=1. / 10,
size=(784, 100)).astype(np.float32)
vb = np.zeros(784, dtype=np.float32)
hb = np.array(100, dtype=np.float32)
Wt = theano.shared(W, name='W')
vbt = theano.shared(vb, name='vbias')
hbt = theano.shared(hb, name='hbias')
g_rbm = rbm.RBM(config, W=Wt, h_bias=hbt, v_bias=vbt)
self.assertTrue(g_rbm)
self.assertTrue(isinstance(g_rbm.v_unit,
rbm_units.GaussianVisibleUnit))
self.assertTrue(isinstance(g_rbm.h_unit, rbm_units.RBMUnit))
self.assertTrue(
np.count_nonzero(g_rbm.W.get_value(borrow=True) - W) == 0)
self.assertTrue(
np.count_nonzero(g_rbm.v_bias.get_value(borrow=True) - vb) == 0)
self.assertTrue(
np.count_nonzero(g_rbm.h_bias.get_value(borrow=True) - hb) == 0)
tr, vl, te = mnist_loader.load_digits(n=[5, 10, 10],
pre={'scale': True})
v = tr[0]
# print 'inputs:'
# table = ss.itemfreq(v.get_value(borrow=True))
# x = [pt[0] for pt in table]
# y = [pt[1] for pt in table]
# plt.plot(x, y)
# plt.show()
# v = theano.shared(x)
_, _, h = g_rbm.sample_h_given_v(v)
_, _, vs = g_rbm.sample_v_given_h(h)
_, _, hs = g_rbm.sample_h_given_v(vs)
dw = T.dot(v.T, h) - T.dot(vs.T, hs)
dv = T.sum(v - vs, axis=0)
dh = T.sum(h - hs, axis=0)
gr = g_rbm.get_partial_derivatives(v, None)['gradients']
gdw, gdv, gdh = gr[0], gr[1], gr[2]
print gdw, gdv, gdh
compute_derivative = theano.function([], [dw, dv, dh, gdw, gdv, gdh])
for i in xrange(1):
a, b, c, d, e, f = compute_derivative()
# print a, b, c
print 'unfold'
print a[0], b[1:5], c[1:5]
print 'rbm'
print d[0], e[1:5], f[1:5]
def test_kanades_grrbm(self):
nvis = 625
nhid = 1000
train_n = 10000
batch_n = 20
tr = rbm_config.TrainParam(learning_rate=0.05,
momentum_type=rbm_config.CLASSICAL,
momentum=0.5,
weight_decay=0,
sparsity_constraint=False,
sparsity_target=0.1**9,
sparsity_cost=10**8,
sparsity_decay=0.9,
epochs=5,
batch_size=batch_n)
config = rbm_config.RBMConfig()
config.v_n = nvis
config.h_n = nhid
config.v_unit = rbm_units.GaussianVisibleUnit
config.h_unit = rbm_units.ReLUnit
config.progress_logger = rbm_logger.ProgressLogger()
config.train_params = tr
np_rand = np.random.RandomState(123)
# Weights
W = np_rand.normal(0, 0.01, size=(nvis, nhid)).astype(np.float32)
vb = np.zeros(nvis, dtype=np.float32)
hb = np.zeros(nhid, dtype=np.float32)
Wt = theano.shared(W, name='W')
vbt = theano.shared(vb, name='vbias')
hbt = theano.shared(hb, name='hbias')
g_rbm = rbm.RBM(config, W=Wt, h_bias=hbt, v_bias=vbt)
self.assertTrue(g_rbm)
self.assertTrue(isinstance(g_rbm.v_unit,
rbm_units.GaussianVisibleUnit))
self.assertTrue(isinstance(g_rbm.h_unit, rbm_units.RBMUnit))
self.assertTrue(
np.count_nonzero(g_rbm.W.get_value(borrow=True) - W) == 0)
self.assertTrue(
np.count_nonzero(g_rbm.v_bias.get_value(borrow=True) - vb) == 0)
self.assertTrue(
np.count_nonzero(g_rbm.h_bias.get_value(borrow=True) - hb) == 0)
tr, vl, te = k_loader.load_kanade(n=batch_n, pre={'scale': True})
v = tr[0]
vv = v.get_value(borrow=True).ravel()
table = ss.itemfreq(vv)
print table
x = [pt[0] for pt in table]
y = [pt[1] for pt in table]
plt.plot(x, y)
plt.show()
# v = theano.shared(x)
h = g_rbm.h_unit.activate(g_rbm.h_unit.scale(T.dot(v, W) + hb))
_, _, h = g_rbm.sample_h_given_v(v)
_, _, vs = g_rbm.sample_v_given_h(h)
_, _, hs = g_rbm.sample_h_given_v(vs)
dw = (T.dot(v.T, h) - T.dot(vs.T, hs)) / batch_n
dv = T.mean(v - vs, axis=0)
dh = T.mean(h - hs, axis=0)
gr = g_rbm.get_partial_derivatives(v, None)['gradients']
gdw, gdv, gdh = gr[0], gr[1], gr[2]
print gdw, gdv, gdh
compute_derivative = theano.function([], [dw, dv, dh, gdw, gdv, gdh])
for i in xrange(1):
a, b, c, d, e, f = compute_derivative()
# print a, b, c
print 'unfold'
print a[0][0:5], b[1:5], c[1:5]
print 'rbm'
print d[0][0:5], e[1:5], f[1:5]