I am having a weird "ValueError: mean must be 1 dimensional" when I am trying to build a Hierarchical GL-LVM model. Basically I'm trying to reproduce this paper: Hierarchical Gaussian Process Latent Variable Models using GPflow.
Therefore I implemented my own new model as follow:
class myGPLVM(gpflow.models.BayesianModel):
def __init__(self, data, latent_data, x_data_mean, kernel):
super().__init__()
print("GPLVM")
self.kernel0 = kernel[0]
self.kernel1 = kernel[1]
self.mean_function = Zero()
self.likelihood0 = gpflow.likelihoods.Gaussian(1.0)
self.likelihood1 = gpflow.likelihoods.Gaussian(1.0)
# make some parameters
self.data = (gpflow.Parameter(x_data_mean), gpflow.Parameter(latent_data), data)
def hierarchy_ll(self):
x, h, y = self.data
K = self.kernel0(x)
num_data = x.shape[0]
k_diag = tf.linalg.diag_part(K)
s_diag = tf.fill([num_data], self.likelihood0.variance)
ks = tf.linalg.set_diag(K, k_diag + s_diag)
L = tf.linalg.cholesky(ks)
m = self.mean_function(x)
return multivariate_normal(h, m, L)
def log_likelihood(self):
"""
Computes the log likelihood.
.. math::
\log p(Y | \theta).
"""
x, h, y = self.data
K = self.kernel1(h)
num_data = h.shape[0]
k_diag = tf.linalg.diag_part(K)
s_diag = tf.fill([num_data], self.likelihood1.variance)
ks = tf.linalg.set_diag(K, k_diag + s_diag)
L = tf.linalg.cholesky(ks)
m = self.mean_function(h)
# [R,] log-likelihoods for each independent dimension of Y
log_prob = multivariate_normal(y, m, L). # <- trows the error!
log_prob_h = self.hierarchy_ll()
log_likelihood = tf.reduce_sum(log_prob) + tf.reduce_sum(log_prob_h)
return log_likelihood
The model seems to work with a toy example:
from sklearn.datasets.samples_generator import make_blobs
X, y = make_blobs(n_samples=40, centers=3, n_features=12, random_state=2)
Y = tf.convert_to_tensor(X, dtype=default_float())
but fails and trough me the error when I am trying with a bvh file (the one from the paper actually). I also used Lawrence's code to read my bvh from mocap which I modified to fit python3
Anyway, it's been few a days and I am out of ideas. I tried multiple way to force my mean array "m" to be of one dimensional but nothing worked. I also tried with the "three_phase_oil_flow" dataset from the first GPLVM paper which works as well.
Therefore, I would assume that my model is correct, or at least I got some optimisation going on, and would think that perhaps the bvh reader could be the cause. But the data seems all fine to me... Especially I don't understand why when forcing multivariate function like:
m = np.zeros((np.shape(m)[0], 1))
log_prob = multivariate_normal(y, m, L)
or even with the gpflow Zero function
m = Zero(h)
log_prob = multivariate_normal(y, m, L)
it still trows me the error. Any help will be highly appreciated.
edited thanks to: Artem Artemev The rest of the code if anyone wants to try to reproduce: https://github.com/michaelStettler/h-GPLVM
error flow:
(venv) MacBookMichael2:stackOverflow michaelstettler$ python3 HGPLVM.py
(199, 96)
shape Y (199, 3, 38)
2020-01-26 17:00:48.104029: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-01-26 17:00:48.113609: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f8dd5ff5410 executing computations on platform Host. Devices:
2020-01-26 17:00:48.113627: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Host, Default Version
shape Y (199, 38)
Number of points: 199 and Number of dimensions: 38
shape x_mean_latent (199, 8)
shape x_mean_init (199, 2)
HGPLVM
gpr_data (199, 2) (199, 8) (199, 38)
2020-01-26 17:00:48.139003: W tensorflow/python/util/util.cc:299] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
shape m (199, 1)
Traceback (most recent call last):
File "HGPLVM.py", line 131, in <module>
_ = opt.minimize(closure, method="bfgs", variables=model.trainable_variables, options=dict(maxiter=maxiter))
File "/Users/michaelstettler/PycharmProjects/GPflow/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py", line 60, in minimize
**scipy_kwargs)
File "/Users/michaelstettler/PycharmProjects/GPflow/venv/lib/python3.6/site-packages/scipy/optimize/_minimize.py", line 594, in minimize
return _minimize_bfgs(fun, x0, args, jac, callback, **options)
File "/Users/michaelstettler/PycharmProjects/GPflow/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py", line 998, in _minimize_bfgs
gfk = myfprime(x0)
File "/Users/michaelstettler/PycharmProjects/GPflow/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py", line 327, in function_wrapper
return function(*(wrapper_args + args))
File "/Users/michaelstettler/PycharmProjects/GPflow/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py", line 73, in derivative
self(x, *args)
File "/Users/michaelstettler/PycharmProjects/GPflow/venv/lib/python3.6/site-packages/scipy/optimize/optimize.py", line 65, in __call__
fg = self.fun(x, *args)
File "/Users/michaelstettler/PycharmProjects/GPflow/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py", line 72, in _eval
loss, grads = _compute_loss_and_gradients(closure, variables)
File "/Users/michaelstettler/PycharmProjects/GPflow/venv/lib/python3.6/site-packages/gpflow/optimizers/scipy.py", line 116, in _compute_loss_and_gradients
loss = loss_cb()
File "HGPLVM.py", line 127, in closure
return - model.log_marginal_likelihood()
File "/Users/michaelstettler/PycharmProjects/GPflow/venv/lib/python3.6/site-packages/gpflow/models/model.py", line 45, in log_marginal_likelihood
return self.log_likelihood(*args, **kwargs) + self.log_prior()
File "HGPLVM.py", line 62, in log_likelihood
log_prob = multivariate_normal(y, m, L)
File "mtrand.pyx", line 3729, in numpy.random.mtrand.RandomState.multivariate_normal
ValueError: mean must be 1 dimensional
I would recommend posting a working MWE code. I have tried to use your code snippets, but it gives me errors.
I don't have issues with multivariate_normal function. If you have localised the issue correctly you can debug TF2.0 more thoroughly and find the place that causes that exception. Here is the code which I'm running:
In [2]: from sklearn.datasets.samples_generator import make_blobs
...: X, y = make_blobs(n_samples=40, centers=3, n_features=12, random_state=2)
In [10]: m = np.zeros((np.shape(y)[0], 1))
In [11]: m.shape
Out[11]: (40, 1)
In [12]: y.shape
Out[12]: (40,)
In [13]: L = np.eye(m.shape[0])
In [15]: gpflow.logdensities.multivariate_normal(y, m, L)
Out[15]:
<tf.Tensor: shape=(40,), dtype=float64, numpy=
array([ -56.75754133, ...])>