def test_vae():
minibatch_size = 10
data = pm.floatX(np.random.rand(100))
x_mini = pm.Minibatch(data, minibatch_size)
x_inp = tt.vector()
x_inp.tag.test_value = data[:minibatch_size]
ae = theano.shared(pm.floatX([.1, .1]))
be = theano.shared(pm.floatX(1.))
ad = theano.shared(pm.floatX(1.))
bd = theano.shared(pm.floatX(1.))
enc = x_inp.dimshuffle(0, 'x') * ae.dimshuffle('x', 0) + be
mu, rho = enc[:, 0], enc[:, 1]
with pm.Model():
# Hidden variables
zs = pm.Normal('zs', mu=0, sd=1, shape=minibatch_size)
dec = zs * ad + bd
# Observation model
pm.Normal('xs_', mu=dec, sd=0.1, observed=x_inp)
pm.fit(1, local_rv={zs: dict(mu=mu, rho=rho)},
more_replacements={x_inp: x_mini}, more_obj_params=[ae, be, ad, bd])
def __init__(self, rng, _input, n_in, n_out, w=None, b=None, activation=T.nnet.sigmoid):
self.input = _input
if w is None:
w_values = numpy.asarray(
rng.uniform(
low=-numpy.sqrt(6. / (n_in + n_out)),
high=numpy.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)
),
dtype=theano.config.floatX
)
if activation == theano.tensor.nnet.sigmoid:
w_values *= 4
w = theano.shared(value=w_values, name='w', borrow=True)
if b is None:
b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
b = theano.shared(value=b_values, name='b', borrow=True)
self.w = w
self.b = b
lin_output = T.dot(_input, self.w) + self.b
self.output = (
lin_output if activation is None
else activation(lin_output)
)
self.params = [self.w, self.b]
def aevb_model():
with pm.Model() as model:
pm.HalfNormal('x', shape=(2,), total_size=5)
pm.Normal('y', shape=(2,))
x = model.x
y = model.y
mu = theano.shared(x.init_value)
rho = theano.shared(np.zeros_like(x.init_value))
return {
'model': model,
'y': y,
'x': x,
'replace': dict(mu=mu, rho=rho)
}
def aevb_model():
with pm.Model() as model:
x = pm.Normal('x')
pm.Normal('y', x)
x = model.x
y = model.y
mu = theano.shared(x.init_value) * 2
rho = theano.shared(np.zeros_like(x.init_value))
return {
'model': model,
'y': y,
'x': x,
'replace': (mu, rho)
}
def test_aevb_empirical():
_, model, _ = models.exponential_beta(n=2)
x = model.x
mu = theano.shared(x.init_value)
rho = theano.shared(np.zeros_like(x.init_value))
with model:
inference = ADVI(local_rv={x: (mu, rho)})
approx = inference.approx
trace0 = approx.sample(10000)
approx = Empirical(trace0, local_rv={x: (mu, rho)})
trace1 = approx.sample(10000)
np.testing.assert_allclose(trace0['y'].mean(0), trace1['y'].mean(0), atol=0.02)
np.testing.assert_allclose(trace0['y'].var(0), trace1['y'].var(0), atol=0.02)
np.testing.assert_allclose(trace0['x'].mean(0), trace1['x'].mean(0), atol=0.02)
np.testing.assert_allclose(trace0['x'].var(0), trace1['x'].var(0), atol=0.02)
def test_cloning_available(self):
gop = generator(integers())
res = gop ** 2
shared = theano.shared(np.float32(10))
res1 = theano.clone(res, {gop: shared})
f = theano.function([], res1)
assert f() == np.float32(100)
def test_optimizer_minibatch_with_callback(self):
n = 1000
sd0 = 2.
mu0 = 4.
sd = 3.
mu = -5.
data = sd * np.random.randn(n) + mu
d = n / sd ** 2 + 1 / sd0 ** 2
mu_post = (n * np.mean(data) / sd ** 2 + mu0 / sd0 ** 2) / d
def create_minibatch(data):
while True:
data = np.roll(data, 100, axis=0)
yield data[:100]
minibatches = create_minibatch(data)
with Model():
data_t = theano.shared(next(minibatches))
def cb(*_):
data_t.set_value(next(minibatches))
mu_ = Normal('mu', mu=mu0, sd=sd0, testval=0)
Normal('x', mu=mu_, sd=sd, observed=data_t, total_size=n)
inf = self.inference()
approx = inf.fit(self.NITER * 3, callbacks=[cb], obj_n_mc=10, obj_optimizer=self.optimizer)
trace = approx.sample_vp(10000)
np.testing.assert_allclose(np.mean(trace['mu']), mu_post, rtol=0.4)
np.testing.assert_allclose(np.std(trace['mu']), np.sqrt(1. / d), rtol=0.4)
def join_nonshared_inputs(xs, vars, shared, make_shared=False):
"""
Takes a list of theano Variables and joins their non shared inputs into a single input.
Parameters
----------
xs : list of theano tensors
vars : list of variables to join
Returns
-------
tensors, inarray
tensors : list of same tensors but with inarray as input
inarray : vector of inputs
"""
joined = theano.tensor.concatenate([var.ravel() for var in vars])
if not make_shared:
tensor_type = joined.type
inarray = tensor_type('inarray')
else:
inarray = theano.shared(joined.tag.test_value, 'inarray')
ordering = ArrayOrdering(vars)
inarray.tag.test_value = joined.tag.test_value
get_var = { var.name : var for var in vars}
replace = {
get_var[var] : reshape_t(inarray[slc], shp).astype(dtyp)
for var, slc, shp, dtyp in ordering.vmap }
replace.update(shared)
xs_special = [theano.clone(x, replace, strict=False) for x in xs]
return xs_special, inarray
def _test_aevb(self):
# add to inference that supports aevb
with pm.Model() as model:
x = pm.Normal('x')
pm.Normal('y', x)
x = model.x
y = model.y
mu = theano.shared(x.init_value) * 2
rho = theano.shared(np.zeros_like(x.init_value))
with model:
inference = self.inference(local_rv={x: (mu, rho)})
approx = inference.fit(3, obj_n_mc=2, obj_optimizer=self.optimizer)
approx.sample_vp(10)
approx.apply_replacements(
y,
more_replacements={x: np.asarray([1, 1], dtype=x.dtype)}
).eval()
def test_aevb_histogram(self):
_, model, _ = models.exponential_beta(n=2)
x = model.x
mu = theano.shared(x.init_value)
rho = theano.shared(np.zeros_like(x.init_value))
with model:
inference = ADVI(local_rv={x: (mu, rho)})
approx = inference.approx
trace0 = approx.sample_vp(10000)
histogram = Histogram(trace0, local_rv={x: (mu, rho)})
trace1 = histogram.sample_vp(10000)
histogram.random(no_rand=True)
histogram.random_fn(no_rand=True)
np.testing.assert_allclose(trace0['y'].mean(0), trace1['y'].mean(0), atol=0.02)
np.testing.assert_allclose(trace0['y'].var(0), trace1['y'].var(0), atol=0.02)
np.testing.assert_allclose(trace0['x'].mean(0), trace1['x'].mean(0), atol=0.02)
np.testing.assert_allclose(trace0['x'].var(0), trace1['x'].var(0), atol=0.02)
def test_observed_type(self):
X_ = np.random.randn(100, 5)
X = pm.floatX(theano.shared(X_))
with pm.Model():
x1 = pm.Normal('x1', observed=X_)
x2 = pm.Normal('x2', observed=X)
assert x1.type == X.type
assert x2.type == X.type
def test_gen_cloning_with_shape_change(self):
data = floatX(np.random.uniform(size=(1000, 10)))
minibatches = DataSampler(data, batchsize=50)
gen = generator(minibatches)
gen_r = tt_rng().normal(size=gen.shape).T
X = gen.dot(gen_r)
res, _ = theano.scan(lambda x: x.sum(), X, n_steps=X.shape[0])
assert res.eval().shape == (50,)
shared = theano.shared(data)
res2 = theano.clone(res, {gen: shared**2})
assert res2.eval().shape == (1000,)
def __init__(self, cost, grad_vars, extra_vars=None, dtype=None,
casting='no', **kwargs):
if extra_vars is None:
extra_vars = []
names = [arg.name for arg in grad_vars + extra_vars]
if any(name is None for name in names):
raise ValueError('Arguments must be named.')
if len(set(names)) != len(names):
raise ValueError('Names of the arguments are not unique.')
if cost.ndim > 0:
raise ValueError('Cost must be a scalar.')
self._grad_vars = grad_vars
self._extra_vars = extra_vars
self._extra_var_names = set(var.name for var in extra_vars)
self._cost = cost
self._ordering = ArrayOrdering(grad_vars)
self.size = self._ordering.size
self._extra_are_set = False
if dtype is None:
dtype = theano.config.floatX
self.dtype = dtype
for var in self._grad_vars:
if not np.can_cast(var.dtype, self.dtype, casting):
raise TypeError('Invalid dtype for variable %s. Can not '
'cast to %s with casting rule %s.'
% (var.name, self.dtype, casting))
if not np.issubdtype(var.dtype, float):
raise TypeError('Invalid dtype for variable %s. Must be '
'floating point but is %s.'
% (var.name, var.dtype))
givens = []
self._extra_vars_shared = {}
for var in extra_vars:
shared = theano.shared(var.tag.test_value, var.name + '_shared__')
self._extra_vars_shared[var.name] = shared
givens.append((var, shared))
self._vars_joined, self._cost_joined = self._build_joined(
self._cost, grad_vars, self._ordering.vmap)
grad = tt.grad(self._cost_joined, self._vars_joined)
grad.name = '__grad'
inputs = [self._vars_joined]
self._theano_function = theano.function(
inputs, [self._cost_joined, grad], givens=givens, **kwargs)
def __init__(self, _input, n_in, n_out, activation=T.nnet.sigmoid):
self.input = _input
self.w = theano.shared(
value=numpy.zeros(
(n_in, n_out),
dtype=theano.config.floatX
),
name='w',
borrow=True
)
self.b = theano.shared(
value=numpy.zeros(
(n_out,),
dtype=theano.config.floatX
),
name='b',
borrow=True
)
self.prediction = activation(T.dot(_input, self.w) + self.b)
self.params = [self.w, self.b]
def test_gradient_with_scaling(self):
with pm.Model() as model1:
genvar = generator(gen1())
m = Normal('m')
Normal('n', observed=genvar, total_size=1000)
grad1 = theano.function([m], tt.grad(model1.logpt, m))
with pm.Model() as model2:
m = Normal('m')
shavar = theano.shared(np.ones((1000, 100)))
Normal('n', observed=shavar)
grad2 = theano.function([m], tt.grad(model2.logpt, m))
for i in range(10):
shavar.set_value(np.ones((100, 100)) * i)
g1 = grad1(1)
g2 = grad2(1)
np.testing.assert_almost_equal(g1, g2)
def make_shared_replacements(vars, model):
"""
Makes shared replacements for all *other* variables than the ones passed.
This way functions can be called many times without setting unchanging variables. Allows us
to use func.trust_input by removing the need for DictToArrayBijection and kwargs.
Parameters
----------
vars : list of variables not to make shared
model : model
Returns
-------
Dict of variable -> new shared variable
"""
othervars = set(model.vars) - set(vars)
return {var : theano.shared(var.tag.test_value, var.name + '_shared') for var in othervars }
class _approx:
params = (theano.shared(np.asarray([1, 2, 3])), )
def shared_zeros(shape, dtype=theano.config.floatX, name='', n=None):
shape = shape if n is None else (n, ) + shape
return theano.shared(numpy.zeros(shape, dtype=dtype), name=name)
def aevb_initial():
return theano.shared(np.random.rand(3, 7).astype('float32'))
def aevb_initial():
return theano.shared(np.random.rand(3, 7).astype('float32'))
from __future__ import print_function
import numpy
from theano import theano
import theano.tensor as T
import pickle, gzip
import timeit
data_dir = "/sharedfiles/"
print("Using device", theano.config.device)
print("Loading data")
with gzip.open(data_dir + "mnist.pkl.gz", 'rb') as f:
train_set, valid_set, test_set = pickle.load(f)
train_set_x = theano.shared(
numpy.asarray(train_set[0], dtype=theano.config.floatX))
train_set_y = theano.shared(numpy.asarray(train_set[1], dtype='int32'))
print("Building model")
batch_size = 600
n_in = 28 * 28
n_hidden = 500
n_out = 10
x = T.matrix('x')
y = T.ivector('y')
def shared_zeros(shape, dtype=theano.config.floatX, name='', n=None):
shape = shape if n is None else (n, ) + shape
def __init__(self, input, filter_shape, image_shape, poolsize=(2, 2)):
"""
Allocate a NetConvPoolLayer with shared variable internal parameters.
:type input: theano.tensor.dtensor4
:param input: symbolic image tensor, of shape image_shape
:type filter_shape: tuple or list of length 4
:param filter_shape: (number of filters, num input feature maps,
filter height, filter width)
:type image_shape: tuple or list of length 4
:param image_shape: (batch size, num input feature maps,
image height, image width)
:type poolsize: tuple or list of length 2
:param poolsize: the downsampling (pooling) factor (#rows, #cols)
"""
assert image_shape[1] == filter_shape[1]
self.input = input
# there are "num input feature maps * filter height * filter width"
# inputs to each hidden unit
fan_in = np.prod(filter_shape[1:])
# each unit in the lower layer receives a gradient from:
# "num output feature maps * filter height * filter width" /
# pooling size
fan_out = (filter_shape[0] * np.prod(filter_shape[2:]) //
np.prod(poolsize))
# initialize weights with random weights
W_bound = np.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(
np.random.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX, name = 'W')
# the bias is a 1D tensor -- one bias per output feature map
b_values = np.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
# convolve input feature maps with filters
self.conv_out = conv2d(
input=input,
filters=self.W,
filter_shape=filter_shape,
image_shape=image_shape
)
# downsample each feature map individually, using maxpooling
self.pooled_out = downsample.max_pool_2d(
input=self.conv_out,
ds=poolsize,
ignore_border=True
)
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
self.output = T.tanh(self.pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
# store parameters of this layer
self.params = [self.W, self.b]
# keep track of model input
self.input = input
