https://blog.tensorflow.org/2019/03/regression-with-probabilistic-layers-in.html?hl=es_419

TensorFlow Probability

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marzo 12, 2019 —
*Posted by Pavel Sountsov, Chris Suter, Jacob Burnim, Joshua V. Dillon, and the TensorFlow Probability team*

BackgroundAt the 2019 TensorFlow Dev Summit, we announced Probabilistic Layers in TensorFlow Probability (TFP). Here, we demonstrate in more detail how to use TFP layers to manage the uncertainty inherent in regression predictions.

Regression and ProbabilityRegression is one of the most basic …

Regression with Probabilistic Layers in TensorFlow Probability

`negloglik = lambda y, p_y: -p_y.log_prob(y)`

We can use a variety of standard continuous and categorical and loss functions with this model of regression. Mean squared error loss for continuous labels, for example, means that In this post we will show how to use probabilistic layers in TensorFlow Probability (TFP) with Keras to build on that simple foundation, incrementally reasoning about progressively more uncertainty of the task at hand. You can follow along in this Google Colab.

```
import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
# Build model.
model = tf.keras.Sequential([
tf.keras.layers.Dense(1),
tfp.layers.DistributionLambda(lambda t: tfd.Normal(loc=t, scale=1)),
])
# Do inference.
model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.05), loss=negloglik)
model.fit(x, y, epochs=500, verbose=False)
# Make predictions.
yhat = model(x_tst)
```

The inference and prediction sections should be familiar to anyone who has used Keras before, but the model construction will look different. We make it explicit that we’re modeling the labels using a normal distribution with a scale of 1 centered on location (mean) that’s dependent on the inputs. The `tfp.layers.DistributionLambda`

layer in fact returns a special instance of `tfd.Distribution`

(see Appendix A for more details about this), so we are free to take its mean and plot it next to the data:`mean = yhat.mean()`

Thus, we managed to capture the overall trend of the data (blue circles) with the predicted mean of the distribution over labels. However, we can see that the data has more structure: it appears that the

We will assume that this variability has a known functional relationship to the value of

```
# Build model.
model = tfk.Sequential([
tf.keras.layers.Dense(1 + 1),
tfp.layers.DistributionLambda(
lambda t: tfd.Normal(loc=t[..., :1],
scale=1e-3 + tf.math.softplus(0.05 * t[..., 1:]))),
])
# Do inference.
model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.05), loss=negloglik)
model.fit(x, y, epochs=500, verbose=False)
# Make predictions.
yhat = model(x_tst)
```

Now, in addition to predicting the mean of the label distribution, we also predict its scale (standard deviation). After training and forming the predictions the same way, we can get meaningful predictions about the variability of ```
mean = yhat.mean()
stddev = yhat.stddev()
mean_plus_2_stddev = mean - 2. * stddev
mean_minus_2_stddev = mean + 2. * stddev
```

Much better! Our model is now less certain about what y should be as x gets larger. This kind of uncertainty is called

`Dense`

layer with TFP’s `DenseVariational`

layer.The

`DenseVariational`

layer uses a variational posterior For

Let’s put that all together:

```
# Build model.
model = tf.keras.Sequential([
tfp.layers.DenseVariational(1, posterior_mean_field, prior_trainable),
tfp.layers.DistributionLambda(lambda t: tfd.Normal(loc=t, scale=1)),
])
# Do inference.
model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.05), loss=negloglik)
model.fit(x, y, epochs=500, verbose=False)
# Make predictions.
yhats = [model(x_tst) for i in range(100)]
```

Despite the complexity of the algorithms involved, using the `DenseVariational`

layer is simple. One interesting aspect of the code above is that when we make predictions using a model with such a layer, we get a different answer every time we do so. This is because `DenseVariational`

essentially defines an ensemble of models. Let us see what this ensemble tells us about the parameters of our model.Each line represents a different random draw of the model parameters from the posterior distribution. As we can see, there is in fact quite a bit of uncertainty about the linear relationship. Even if we don’t care about the variability of

Note that in this example we are training both

```
# Build model.
model = tf.keras.Sequential([
tfp.layers.DenseVariational(1 + 1, posterior_mean_field, prior_trainable),
tfp.layers.DistributionLambda(
lambda t: tfd.Normal(loc=t[..., :1],
scale=1e-3 + tf.math.softplus(0.01 * t[..., 1:]))),
])
# Do inference.
model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.05), loss=negloglik)
model.fit(x, y, epochs=500, verbose=False);
# Make predictions.
yhats = [model(x_tst) for _ in range(100)]
```

The only change we’ve made to the previous model is that we added an extra output to `DenseVariational`

layer to also model the scale of the label distribution. As in our previous solution, we get an ensemble of models, but this time they all also report the variability of Note the qualitative difference between the predictions of this model compared to those from the model that considered only aleatoric uncertainty: this model predicts more variability as

`negloglik`

function that implements the negative log-likelihood, while making local alterations to the model to handle more and more types of uncertainty. The API also lets you freely switch between Maximum Likelihood learning, Type-II Maximum Likelihood and and a full Bayesian treatment. We believe that this API significantly simplifies construction of probabilistic models and are excited to share it with the world.This API will be ready to use in the next stable release, TensorFlow Probability 0.7.0, and is already available in the nightly version. Please join us on the tfprobability@tensorflow.org forum for the latest TensorFlow Probability announcements and other TFP discussions.

The standard tool for doing regression while making these sorts of assumptions is the Gaussian Process. This powerful model uses a kernel function to encode the smoothness assumptions (and other global function properties) about what form the relationship between the inputs and labels should take. Conditioned on the data, it forms a probability distribution over

TFP provides the

`VariationalGaussianProcess`

layer, which uses a variational approximation (similar in spirit to what we did in case 3 and 4 above) to a full Gaussian Process for an efficient yet flexible regression model. For simplicity, we’ll be considering only the epistemic uncertainty about the form of the relationship between inputs and labels. In terms of the assumptions we’ll be making, we’ll simply assume that the function we’re fitting is locally smooth: it can vary as much as it wants across the entire dataset, but if two inputs are close, it’ll return similar values.```
num_inducing_points = 40
model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=[1], dtype=x.dtype),
tf.keras.layers.Dense(1, kernel_initializer='ones', use_bias=False),
tfp.layers.VariationalGaussianProcess(
num_inducing_points=num_inducing_points,
kernel_provider=RBFKernelFn(dtype=x.dtype),
event_shape=[1],
inducing_index_points_initializer=tf.constant_initializer(
np.linspace(*x_range, num=num_inducing_points,
dtype=x.dtype)[..., np.newaxis]),
unconstrained_observation_noise_variance_initializer=(
tf.constant_initializer(
np.log(np.expm1(1.)).astype(x.dtype))),
),
])
# Do inference.
batch_size = 32
loss = lambda y, rv_y: rv_y.variational_loss(
y, kl_weight=np.array(batch_size, x.dtype) / x.shape[0])
model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01), loss=loss)
model.fit(x, y, batch_size=batch_size, epochs=1000, verbose=False)
# Make predictions.
yhats = [model(x_tst) for _ in range(100)]
```

Due to its power, the definition of this model is significantly more complex: we need to define a new loss function, and there are more parameters to specify. In the near future, the TFP team will be working to simplify this model further. This added complexity is worth it, however, as evidenced by the results:The VariationalGaussianProcess has discovered a periodic structure in the training set! Indeed, that structure was present in the data used throughout this post all along — did you notice it before the model did? Importantly, the model discovered this structure without us telling it that there was any such periodicity in the data. And, as advertised, it is still giving us a measure of uncertainty. For example, close to 0, the periodic structure is not as apparent, so the model does not commit to any such relationship in that region.

`DistributionLambda`

is a special Keras layer that uses a Python lambda to construct a distribution conditioned on the layer inputs:```
layer = tfp.layers.DistributionLambda(lambda t: tfd.Normal(t, 1.))
distribution = layer(2.)
assert isinstance(distribution, tfd.Normal)
distribution.loc
# ==> 2.
distribution.stddev()
# ==> 1.
```

This layer enables us to write the `negloglik`

loss function as we did, because Keras passes the output of the final layer of the model into the loss function, and for the models in this post, all those layers return distributions. See the Variational Autoencoders with Tensorflow Probability Layers post for more ways to use these layers.`DenseVariational`

layer enables learning a distribution over its weights using variational inference. This is done by maximizing the ELBO (Evidence Lower BOund) objective:ELBO uses three distributions:

*P(w)*is the prior over the weights. It is the distribution we assume the weights to follow before we trained the model.*Q(w; θ)*is the variational posterior parameterized by parameters θ. This is an approximation to the distribution of the weights after we have trained the model.*P(Y | X, w)*is the likelihood function relating all inputs*X*, all labels*Y*and the weights. When used as a probability distribution over*Y*, it specifies the variation in*Y*given*X*and the weights.

`DenseVariational`

computes the two terms of the ELBO separately. The first term is computed by approximating it with a single random sample from The second term is computed analytically, and then added to the layer as a regularization loss — similar to how we’d specify something like an L2 regularization. This loss is added to the first term for us by Keras.

The sampling used to computed the first term explains how we were able to generate multiple models by calling the model with the same inputs multiple times: each time we did that, we sampled a new set of weights according to the

How do we specify the prior and the variational posterior? They’re trainable distributions just like we’ve seen in the case 2. For example, the trainable prior we used in case 3 is defined as follows:

```
def prior_trainable(kernel_size, bias_size=0, dtype=None):
n = kernel_size + bias_size
return tf.keras.Sequential([
tfp.layers.VariableLayer(n, dtype=dtype),
tfp.layers.DistributionLambda(lambda t: tfd.Independent(
tfd.Normal(loc=t, scale=1),
reinterpreted_batch_ndims=1)),
])
```

It’s just a callable that returns a regular Keras model with a `DistributionLambda`

layer! The only new component here is the `VariableLayer`

which simply returns the value of a trainable variable, ignoring any inputs (because the prior is not conditioned on any inputs). Note that if we wanted to convert this to a non-trainable prior, we would pass `trainable=False`

to `VariableLayer`

constructor.
Next post

TensorFlow Probability

Regression with Probabilistic Layers in TensorFlow Probability

marzo 12, 2019
—
*Posted by Pavel Sountsov, Chris Suter, Jacob Burnim, Joshua V. Dillon, and the TensorFlow Probability team*

BackgroundAt the 2019 TensorFlow Dev Summit, we announced Probabilistic Layers in TensorFlow Probability (TFP). Here, we demonstrate in more detail how to use TFP layers to manage the uncertainty inherent in regression predictions.

Regression and ProbabilityRegression is one of the most basic …

Build, deploy, and experiment easily with TensorFlow