keras JointDistribution minimal example not working

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技术栈
jupyter-notebook, python
领域
machine-learning

调研方向

使用 JointDistributionNamed 示例复现链接的 Colab，并检查 tensorflow_probability/python/layers/distribution_layer.py 中 traceback 所示的 DistributionLambda 调用。比较预期的 distribution 返回值与观察到的 dict 错误，并确认是否需要一条可正常工作的 model.fit 路径，或者需要记录一项已文档化的限制。

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描述

Hi,
I am trying to train a keras model using a mix of JointDistribution and other layers. My goal is to be able to build the model, compile and use model.fit on data.
Somehow, I keep getting errors and can't get it working.
I could not find a minimal example with it running and I think I am missing something.
Here is the minimal example I tried:

import tensorflow as tf
import tensorflow_probability as tfp
import tf_keras as tfk  
import numpy as np

tfd = tfp.distributions
tfpl = tfp.layers

# Generate synthetic data
np.random.seed(42)
x = np.random.randn(2000, 1).astype(np.float32)
true_w, true_b = 2.0, -1.0
y = true_w * x + true_b + np.random.randn(2000, 1).astype(np.float32) * 0.5

# Define the JointDistributionNamed model
def make_joint(x):
    return tfd.JointDistributionNamed({
        "weight": tfd.Normal(loc=0., scale=1.),
        "bias": tfd.Normal(loc=0., scale=1.),
        "obs": lambda weight, bias: tfd.Independent(
            tfd.Normal(loc=weight * x + bias, scale=0.5),
            reinterpreted_batch_ndims=1
        )
    })

# Keras model using DistributionLambda
inputs = tfk.Input(shape=(1,))
dense = tfk.layers.Dense(2)(inputs)  # Not used for parameterization here, just as a placeholder

def posterior_fn(params):
    # For demonstration, use fixed x from outer scope
    jd = make_joint(x)
    return jd

outputs = tfpl.DistributionLambda(posterior_fn)(dense)
model = tfk.Model(inputs=inputs, outputs=outputs)

# Custom loss: negative log-likelihood of observed y under the joint
def nll(y_true, y_pred):
    # y_pred is a JointDistributionNamed, so evaluate log_prob of obs
    return -y_pred.log_prob({"obs": y_true})

model.compile(optimizer=tf.optimizers.Adam(0.01), loss=nll)
model.fit(x, y, epochs=5, batch_size=32)

And I got this error:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
[<ipython-input-1-e7f1317b9e9b>](https://localhost:8080/#) in <cell line: 0>()
     33     return jd
     34 
---> 35 outputs = tfpl.DistributionLambda(posterior_fn)(dense)
     36 model = tfk.Model(inputs=inputs, outputs=outputs)
     37 

3 frames
[/usr/local/lib/python3.11/dist-packages/tensorflow_probability/python/layers/distribution_layer.py](https://localhost:8080/#) in _fn(*fargs, **fkwargs)
    183       # TODO(b/126056144): Remove silent handle once we identify how/why Keras
    184       # is losing the distribution handle for activity_regularizer.
--> 185       value._tfp_distribution = distribution  # pylint: disable=protected-access
    186       # TODO(b/120153609): Keras is incorrectly presuming everything is a
    187       # `tf.Tensor`. Closing this bug entails ensuring Keras only accesses

AttributeError: Exception encountered when calling layer "distribution_lambda" (type DistributionLambda).

'dict' object has no attribute '_tfp_distribution'

Call arguments received by layer "distribution_lambda" (type DistributionLambda):
  • inputs=tf.Tensor(shape=(None, 2), dtype=float32)
  • args=<class 'inspect._empty'>
  • kwargs={'training': 'None'}

I am posting it here because I feel this might be a bug.

You can reproduce it via this colab link

Please note I also tried with tfd.JointDistributionSequential and no luck either.

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