Variational API quickstart#
The variational inference (VI) API is focused on approximating posterior distributions for Bayesian models. Common use cases to which this module can be applied include:
Sampling from model posterior and computing arbitrary expressions
Conduct Monte Carlo approximation of expectation, variance, and other statistics
Remove symbolic dependence on PyMC3 random nodes and evaluate expressions (using
eval
)Provide a bridge to arbitrary Theano code
Sounds good, doesn’t it?
The module provides an interface to a variety of inference methods, so you are free to choose what is most appropriate for the problem.
%matplotlib inline
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
import theano
np.random.seed(42)
pm.set_tt_rng(42)
Basic setup#
We do not need complex models to play with the VI API; let’s begin with a simple mixture model:
w = pm.floatX([0.2, 0.8])
mu = pm.floatX([0.3, 0.5])
sd = pm.floatX([0.1, 0.1])
with pm.Model() as model:
x = pm.NormalMixture("x", w=w, mu=mu, sigma=sd, dtype=theano.config.floatX)
x2 = x**2
sin_x = pm.math.sin(x)
We can’t compute analytical expectations for this model. However, we can obtain an approximation using Markov chain Monte Carlo methods; let’s use NUTS first.
To allow samples of the expressions to be saved, we need to wrap them in Deterministic
objects:
with model:
pm.Deterministic("x2", x2)
pm.Deterministic("sin_x", sin_x)
with model:
trace = pm.sample(50000)
Autoassigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [x]
Sampling 4 chains for 1_000 tune and 50_000 draw iterations (4_000 + 200_000 draws total) took 186 seconds.
The estimated number of effective samples is smaller than 200 for some parameters.
az.plot_trace(trace);
/dependencies/arviz/arviz/data/io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.
FutureWarning,
Above are traces for \(x^2\) and \(sin(x)\). We can see there is clear multimodality in this model. One drawback, is that you need to know in advance what exactly you want to see in trace and wrap it with Deterministic
.
The VI API takes an alternate approach: You obtain inference from model, then calculate expressions based on this model afterwards.
Let’s use the same model:
with pm.Model() as model:
x = pm.NormalMixture("x", w=w, mu=mu, sigma=sd, dtype=theano.config.floatX)
x2 = x**2
sin_x = pm.math.sin(x)
Here we will use automatic differentiation variational inference (ADVI).
with model:
mean_field = pm.fit(method="advi")
Finished [100%]: Average Loss = 2.2687
az.plot_posterior(mean_field.sample(1000), color="LightSeaGreen");
/dependencies/arviz/arviz/data/io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.
FutureWarning,
Notice that ADVI has failed to approximate the multimodal distribution, since it uses a Gaussian distribution that has a single mode.
Checking convergence#
help(pm.callbacks.CheckParametersConvergence)
Help on class CheckParametersConvergence in module pymc3.variational.callbacks:
class CheckParametersConvergence(Callback)
 CheckParametersConvergence(every=100, tolerance=0.001, diff='relative', ord=inf)

 Convergence stopping check

 Parameters
 
 every: int
 check frequency
 tolerance: float
 if diff norm < tolerance: break
 diff: str
 difference type one of {'absolute', 'relative'}
 ord: {nonzero int, inf, inf, 'fro', 'nuc'}, optional
 see more info in :func:`numpy.linalg.norm`

 Examples
 
 >>> with model:
 ... approx = pm.fit(
 ... n=10000, callbacks=[
 ... CheckParametersConvergence(
 ... every=50, diff='absolute',
 ... tolerance=1e4)
 ... ]
 ... )

 Method resolution order:
 CheckParametersConvergence
 Callback
 builtins.object

 Methods defined here:

 __call__(self, approx, _, i)
 Call self as a function.

 __init__(self, every=100, tolerance=0.001, diff='relative', ord=inf)
 Initialize self. See help(type(self)) for accurate signature.

 
 Static methods defined here:

 flatten_shared(shared_list)

 
 Data descriptors inherited from Callback:

 __dict__
 dictionary for instance variables (if defined)

 __weakref__
 list of weak references to the object (if defined)
Let’s use the default arguments for CheckParametersConvergence
as they seem to be reasonable.
from pymc3.variational.callbacks import CheckParametersConvergence
with model:
mean_field = pm.fit(method="advi", callbacks=[CheckParametersConvergence()])
Finished [100%]: Average Loss = 2.2763
We can access inference history via .hist
attribute.
plt.plot(mean_field.hist);
This is not a good convergence plot, despite the fact that we ran many iterations. The reason is that the mean of the ADVI approximation is close to zero, and therefore taking the relative difference (the default method) is unstable for checking convergence.
with model:
mean_field = pm.fit(
method="advi", callbacks=[pm.callbacks.CheckParametersConvergence(diff="absolute")]
)
Convergence achieved at 4700
Interrupted at 4,699 [46%]: Average Loss = 4.7996
plt.plot(mean_field.hist);
That’s much better! We’ve reached convergence after less than 5000 iterations.
Tracking parameters#
Another useful callback allows users to track parameters. It allows for the tracking of arbitrary statistics during inference, though it can be memoryhungry. Using the fit
function, we do not have direct access to the approximation before inference. However, tracking parameters requires access to the approximation. We can get around this constraint by using the objectoriented (OO) API for inference.
with model:
advi = pm.ADVI()
advi.approx
<pymc3.variational.approximations.MeanField at 0x7f7dcca2b090>
Different approximations have different hyperparameters. In meanfield ADVI, we have \(\rho\) and \(\mu\) (inspired by Bayes by BackProp).
advi.approx.shared_params
{'mu': mu, 'rho': rho}
There are convenient shortcuts to relevant statistics associated with the approximation. This can be useful, for example, when specifying a mass matrix for NUTS sampling:
advi.approx.mean.eval(), advi.approx.std.eval()
(array([0.34]), array([0.69314718]))
We can roll these statistics into the Tracker
callback.
tracker = pm.callbacks.Tracker(
mean=advi.approx.mean.eval, # callable that returns mean
std=advi.approx.std.eval, # callable that returns std
)
Now, calling advi.fit
will record the mean and standard deviation of the approximation as it runs.
approx = advi.fit(20000, callbacks=[tracker])
Finished [100%]: Average Loss = 1.9589
We can now plot both the evidence lower bound and parameter traces:
fig = plt.figure(figsize=(16, 9))
mu_ax = fig.add_subplot(221)
std_ax = fig.add_subplot(222)
hist_ax = fig.add_subplot(212)
mu_ax.plot(tracker["mean"])
mu_ax.set_title("Mean track")
std_ax.plot(tracker["std"])
std_ax.set_title("Std track")
hist_ax.plot(advi.hist)
hist_ax.set_title("Negative ELBO track");
Notice that there are convergence issues with the mean, and that lack of convergence does not seem to change the ELBO trajectory significantly. As we are using the OO API, we can run the approximation longer until convergence is achieved.
advi.refine(100000)
Finished [100%]: Average Loss = 1.8422
Let’s take a look:
fig = plt.figure(figsize=(16, 9))
mu_ax = fig.add_subplot(221)
std_ax = fig.add_subplot(222)
hist_ax = fig.add_subplot(212)
mu_ax.plot(tracker["mean"])
mu_ax.set_title("Mean track")
std_ax.plot(tracker["std"])
std_ax.set_title("Std track")
hist_ax.plot(advi.hist)
hist_ax.set_title("Negative ELBO track");
We still see evidence for lack of convergence, as the mean has devolved into a random walk. This could be the result of choosing a poor algorithm for inference. At any rate, it is unstable and can produce very different results even using different random seeds.
Let’s compare results with the NUTS output:
import seaborn as sns
ax = sns.kdeplot(trace["x"], label="NUTS")
sns.kdeplot(approx.sample(10000)["x"], label="ADVI");
Again, we see that ADVI is not able to cope with multimodality; we can instead use SVGD, which generates an approximation based on a large number of particles.
with model:
svgd_approx = pm.fit(
300,
method="svgd",
inf_kwargs=dict(n_particles=1000),
obj_optimizer=pm.sgd(learning_rate=0.01),
)
ax = sns.kdeplot(trace["x"], label="NUTS")
sns.kdeplot(approx.sample(10000)["x"], label="ADVI")
sns.kdeplot(svgd_approx.sample(2000)["x"], label="SVGD");
That did the trick, as we now have a multimodal approximation using SVGD.
With this, it is possible to calculate arbitrary functions of the parameters with this variational approximation. For example we can calculate \(x^2\) and \(sin(x)\), as with the NUTS model.
# recall x ~ NormalMixture
a = x**2
b = pm.math.sin(x)
To evaluate these expressions with the approximation, we need approx.sample_node
.
help(svgd_approx.sample_node)
Help on method sample_node in module pymc3.variational.opvi:
sample_node(node, size=None, deterministic=False, more_replacements=None) method of pymc3.variational.approximations.Empirical instance
Samples given node or nodes over shared posterior
Parameters

node: Theano Variables (or Theano expressions)
size: None or scalar
number of samples
more_replacements: `dict`
add custom replacements to graph, e.g. change input source
deterministic: bool
whether to use zeros as initial distribution
if True  zero initial point will produce constant latent variables
Returns

sampled node(s) with replacements
a_sample = svgd_approx.sample_node(a)
a_sample.eval()
array(0.20617133)
a_sample.eval()
array(0.23059109)
a_sample.eval()
array(0.01689826)
Every call yields a different value from the same theano node. This is because it is stochastic.
By applying replacements, we are now free of the dependence on the PyMC3 model; instead, we now depend on the approximation. Changing it will change the distribution for stochastic nodes:
sns.kdeplot(np.array([a_sample.eval() for _ in range(2000)]))
plt.title("$x^2$ distribution");
There is a more convenient way to get lots of samples at once: sample_node
a_samples = svgd_approx.sample_node(a, size=1000)
sns.kdeplot(a_samples.eval())
plt.title("$x^2$ distribution");
The sample_node
function includes an additional dimension, so taking expectations or calculating variance is specified by axis=0
.
a_samples.var(0).eval() # variance
array(0.0963961)
a_samples.mean(0).eval() # mean
array(0.24696937)
A symbolic sample size can also be specified:
i = theano.tensor.iscalar("i")
i.tag.test_value = 1
a_samples_i = svgd_approx.sample_node(a, size=i)
a_samples_i.eval({i: 100}).shape
(100,)
a_samples_i.eval({i: 10000}).shape
(10000,)
Unfortunately the size must be a scalar value.
Converting a Trace to an Approximation#
We can convert a MCMC trace into an Approximation. It will have the same API as approximations above with same sample_node
methods:
trace_approx = pm.Empirical(trace, model=model)
trace_approx
<pymc3.variational.approximations.Empirical at 0x7f7e3a00af10>
We can then draw samples from the Emipirical
object:
az.plot_posterior(trace_approx.sample(10000));
/dependencies/arviz/arviz/data/io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.
FutureWarning,
Multilabel logistic regression#
Let’s illustrate the use of Tracker
with the famous Iris dataset. We’ll attempy multilabel classification and compute the expected accuracy score as a diagnostic.
import pandas as pd
import theano.tensor as tt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
X, y = load_iris(True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
/env/miniconda3/lib/python3.7/sitepackages/sklearn/utils/validation.py:71: FutureWarning: Pass return_X_y=True as keyword args. From version 0.25 passing these as positional arguments will result in an error
FutureWarning)
A relatively simple model will be sufficient here because the classes are roughly linearly separable; we are going to fit multinomial logistic regression.
Xt = theano.shared(X_train)
yt = theano.shared(y_train)
with pm.Model() as iris_model:
# Coefficients for features
β = pm.Normal("β", 0, sigma=1e2, shape=(4, 3))
# Transoform to unit interval
a = pm.Flat("a", shape=(3,))
p = tt.nnet.softmax(Xt.dot(β) + a)
observed = pm.Categorical("obs", p=p, observed=yt)
/env/miniconda3/lib/python3.7/sitepackages/theano/tensor/subtensor.py:2197: FutureWarning: Using a nontuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
rval = inputs[0].__getitem__(inputs[1:])
/env/miniconda3/lib/python3.7/sitepackages/theano/tensor/subtensor.py:2197: FutureWarning: Using a nontuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
rval = inputs[0].__getitem__(inputs[1:])
Applying replacements in practice#
PyMC3 models have symbolic inputs for latent variables. To evaluate an espression that requires knowledge of latent variables, one needs to provide fixed values. We can use values approximated by VI for this purpose. The function sample_node
removes the symbolic dependenices.
sample_node
will use the whole distribution at each step, so we will use it here. We can apply more replacements in single function call using the more_replacements
keyword argument in both replacement functions.
HINT: You can use
more_replacements
argument when callingfit
too:
pm.fit(more_replacements={full_data: minibatch_data})
inference.fit(more_replacements={full_data: minibatch_data})
with iris_model:
# We'll use SVGD
inference = pm.SVGD(n_particles=500, jitter=1)
# Local reference to approximation
approx = inference.approx
# Here we need `more_replacements` to change train_set to test_set
test_probs = approx.sample_node(p, more_replacements={Xt: X_test}, size=100)
# For train set no more replacements needed
train_probs = approx.sample_node(p)
By applying the code above, we now have 100 sampled probabilities (default number for sample_node
is None
) for each observation.
Next we create symbolic expressions for sampled accuracy scores:
test_ok = tt.eq(test_probs.argmax(1), y_test)
train_ok = tt.eq(train_probs.argmax(1), y_train)
test_accuracy = test_ok.mean(1)
train_accuracy = train_ok.mean(1)
Tracker expects callables so we can pass .eval
method of theano node that is function itself.
Calls to this function are cached so they can be reused.
eval_tracker = pm.callbacks.Tracker(
test_accuracy=test_accuracy.eval, train_accuracy=train_accuracy.eval
)
inference.fit(100, callbacks=[eval_tracker]);
/env/miniconda3/lib/python3.7/sitepackages/theano/tensor/subtensor.py:2197: FutureWarning: Using a nontuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
rval = inputs[0].__getitem__(inputs[1:])
/env/miniconda3/lib/python3.7/sitepackages/theano/tensor/subtensor.py:2197: FutureWarning: Using a nontuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
rval = inputs[0].__getitem__(inputs[1:])
_, ax = plt.subplots(1, 1)
df = pd.DataFrame(eval_tracker["test_accuracy"]).T.melt()
sns.lineplot(x="variable", y="value", data=df, color="red", ax=ax)
ax.plot(eval_tracker["train_accuracy"], color="blue")
ax.set_xlabel("epoch")
plt.legend(["test_accuracy", "train_accuracy"])
plt.title("Training Progress");
Training does not seem to be working here. Let’s use a different optimizer and boost the learning rate.
inference.fit(400, obj_optimizer=pm.adamax(learning_rate=0.1), callbacks=[eval_tracker]);
/env/miniconda3/lib/python3.7/sitepackages/theano/tensor/subtensor.py:2197: FutureWarning: Using a nontuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
rval = inputs[0].__getitem__(inputs[1:])
_, ax = plt.subplots(1, 1)
df = pd.DataFrame(np.asarray(eval_tracker["test_accuracy"])).T.melt()
sns.lineplot(x="variable", y="value", data=df, color="red", ax=ax)
ax.plot(eval_tracker["train_accuracy"], color="blue")
ax.set_xlabel("epoch")
plt.legend(["test_accuracy", "train_accuracy"])
plt.title("Training Progress");
This is much better!
So, Tracker
allows us to monitor our approximation and choose good training schedule.
Minibatches#
When dealing with large datasets, using minibatch training can drastically speed up and improve approximation performance. Large datasets impose a hefty cost on the computation of gradients.
There is a nice API in pymc3 to handle these cases, which is available through the pm.Minibatch
class. The minibatch is just a highly specialized Theano tensor:
issubclass(pm.Minibatch, theano.tensor.TensorVariable)
True
To demonstrate, let’s simulate a large quantity of data:
# Raw values
data = np.random.rand(40000, 100)
# Scaled values
data *= np.random.randint(1, 10, size=(100,))
# Shifted values
data += np.random.rand(100) * 10
For comparison, let’s fit a model without minibatch processing:
with pm.Model() as model:
mu = pm.Flat("mu", shape=(100,))
sd = pm.HalfNormal("sd", shape=(100,))
lik = pm.Normal("lik", mu, sd, observed=data)
Just for fun, let’s create a custom special purpose callback to halt slow optimization. Here we define a callback that causes a hard stop when approximation runs too slowly:
def stop_after_10(approx, loss_history, i):
if (i > 0) and (i % 10) == 0:
raise StopIteration("I was slow, sorry")
with model:
advifit = pm.fit(callbacks=[stop_after_10])
I was slow, sorry
Interrupted at 9 [0%]: Average Loss = 5.6736e+08
Inference is too slow, taking several seconds per iteration; fitting the approximation would have taken hours!
Now let’s use minibatches. At every iteration, we will draw 500 random values:
Remember to set
total_size
in observed
total_size is an important parameter that allows pymc3 to infer the right way of rescaling densities. If it is not set, you are likely to get completely wrong results. For more information please refer to the comprehensive documentation of pm.Minibatch
.
X = pm.Minibatch(data, batch_size=500)
with pm.Model() as model:
mu = pm.Flat("mu", shape=(100,))
sd = pm.HalfNormal("sd", shape=(100,))
likelihood = pm.Normal("likelihood", mu, sd, observed=X, total_size=data.shape)
/dependencies/pymc3/pymc3/data.py:307: FutureWarning: Using a nontuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
self.shared = theano.shared(data[in_memory_slc])
with model:
advifit = pm.fit()
Finished [100%]: Average Loss = 1.5452e+05
plt.plot(advifit.hist);
Minibatch inference is dramatically faster. Multidimensional minibatches may be needed for some corner cases where you do matrix factorization or model is very wide.
Here is the docstring for Minibatch
to illustrate how it can be customized.
print(pm.Minibatch.__doc__)
Multidimensional minibatch that is pure TensorVariable
Parameters

data: np.ndarray
initial data
batch_size: ``int`` or ``List[inttuple(size, random_seed)]``
batch size for inference, random seed is needed
for child random generators
dtype: ``str``
cast data to specific type
broadcastable: tuple[bool]
change broadcastable pattern that defaults to ``(False, ) * ndim``
name: ``str``
name for tensor, defaults to "Minibatch"
random_seed: ``int``
random seed that is used by default
update_shared_f: ``callable``
returns :class:`ndarray` that will be carefully
stored to underlying shared variable
you can use it to change source of
minibatches programmatically
in_memory_size: ``int`` or ``List[intsliceEllipsis]``
data size for storing in ``theano.shared``
Attributes

shared: shared tensor
Used for storing data
minibatch: minibatch tensor
Used for training
Notes

Below is a common use case of Minibatch with variational inference.
Importantly, we need to make PyMC3 "aware" that a minibatch is being used in inference.
Otherwise, we will get the wrong :math:`logp` for the model.
the density of the model ``logp`` that is affected by Minibatch. See more in the examples below.
To do so, we need to pass the ``total_size`` parameter to the observed node, which correctly scales
the density of the model ``logp`` that is affected by Minibatch. See more in the examples below.
Examples

Consider we have `data` as follows:
>>> data = np.random.rand(100, 100)
if we want a 1d slice of size 10 we do
>>> x = Minibatch(data, batch_size=10)
Note that your data is cast to ``floatX`` if it is not integer type
But you still can add the ``dtype`` kwarg for :class:`Minibatch`
if you need more control.
If we want 10 sampled rows and columns
``[(size, seed), (size, seed)]`` we can use
>>> x = Minibatch(data, batch_size=[(10, 42), (10, 42)], dtype='int32')
>>> assert str(x.dtype) == 'int32'
Or, more simply, we can use the default random seed = 42
``[size, size]``
>>> x = Minibatch(data, batch_size=[10, 10])
In the above, `x` is a regular :class:`TensorVariable` that supports any math operations:
>>> assert x.eval().shape == (10, 10)
You can pass the Minibatch `x` to your desired model:
>>> with pm.Model() as model:
... mu = pm.Flat('mu')
... sd = pm.HalfNormal('sd')
... lik = pm.Normal('lik', mu, sd, observed=x, total_size=(100, 100))
Then you can perform regular Variational Inference out of the box
>>> with model:
... approx = pm.fit()
Important note: :class:``Minibatch`` has ``shared``, and ``minibatch`` attributes
you can call later:
>>> x.set_value(np.random.laplace(size=(100, 100)))
and minibatches will be then from new storage
it directly affects ``x.shared``.
A less convenient convenient, but more explicit, way to achieve the same
thing:
>>> x.shared.set_value(pm.floatX(np.random.laplace(size=(100, 100))))
The programmatic way to change storage is as follows
I import ``partial`` for simplicity
>>> from functools import partial
>>> datagen = partial(np.random.laplace, size=(100, 100))
>>> x = Minibatch(datagen(), batch_size=10, update_shared_f=datagen)
>>> x.update_shared()
To be more concrete about how we create a minibatch, here is a demo:
1. create a shared variable
>>> shared = theano.shared(data)
2. take a random slice of size 10:
>>> ridx = pm.tt_rng().uniform(size=(10,), low=0, high=data.shape[0]1e10).astype('int64')
3) take the resulting slice:
>>> minibatch = shared[ridx]
That's done. Now you can use this minibatch somewhere else.
You can see that the implementation does not require a fixed shape
for the shared variable. Feel free to use that if needed.
*FIXME: What is "that" which we can use here? A fixed shape? Should this say
"but feel free to put a fixed shape on the shared variable, if appropriate?"*
Suppose you need to make some replacements in the graph, e.g. change the minibatch to testdata
>>> node = x ** 2 # arbitrary expressions on minibatch `x`
>>> testdata = pm.floatX(np.random.laplace(size=(1000, 10)))
Then you should create a `dict` with replacements:
>>> replacements = {x: testdata}
>>> rnode = theano.clone(node, replacements)
>>> assert (testdata ** 2 == rnode.eval()).all()
*FIXME: In the following, what is the **reason** to replace the Minibatch variable with
its shared variable? And in the following, the `rnode` is a **new** node, not a modification
of a previously existing node, correct?*
To replace a minibatch with its shared variable you should do
the same things. The Minibatch variable is accessible through the `minibatch` attribute.
For example
>>> replacements = {x.minibatch: x.shared}
>>> rnode = theano.clone(node, replacements)
For more complex slices some more code is needed that can seem not so clear
>>> moredata = np.random.rand(10, 20, 30, 40, 50)
The default ``total_size`` that can be passed to ``PyMC3`` random node
is then ``(10, 20, 30, 40, 50)`` but can be less verbose in some cases
1. Advanced indexing, ``total_size = (10, Ellipsis, 50)``
>>> x = Minibatch(moredata, [2, Ellipsis, 10])
We take the slice only for the first and last dimension
>>> assert x.eval().shape == (2, 20, 30, 40, 10)
2. Skipping a particular dimension, ``total_size = (10, None, 30)``:
>>> x = Minibatch(moredata, [2, None, 20])
>>> assert x.eval().shape == (2, 20, 20, 40, 50)
3. Mixing both of these together, ``total_size = (10, None, 30, Ellipsis, 50)``:
>>> x = Minibatch(moredata, [2, None, 20, Ellipsis, 10])
>>> assert x.eval().shape == (2, 20, 20, 40, 10)
%load_ext watermark
%watermark n u v iv w
pandas 1.0.4
pymc3 3.9.0
theano 1.0.4
numpy 1.18.5
seaborn 0.10.1
last updated: Mon Jun 15 2020
CPython 3.7.7
IPython 7.15.0
watermark 2.0.2