CODEWITHSUNDEEP
tensorflowkerastensorflow-probability
I. Schubert
I. Schubert

Reputation: 306

How to calculate the gradient of the Kullback-Leibler divergence of two tensorflow-probability distributions with respect to the distribution's mean?

In tensorflow-2.0, I am trying to create a keras.layers.Layer which outputs the Kullback-Leibler (KL) divergence between two tensorflow_probability.distributions. I would like to calculate the gradient of the output (i.e. the KL divergence) with respect to the mean value of one of the tensorflow_probability.distributions.

In all my attempts so far, the resulting gradients are 0, unfortunately.

I tried implementing a minimal example shown below. I was wondering if the problems might have to do with the eager execution mode of tf 2, as I know of a similar approach that worked in tf 1, where eager execution is disabled by default.

This is the minimal example I tried:

import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Layer,Input

# 1 Define Layer

class test_layer(Layer):

    def __init__(self, **kwargs):
        super(test_layer, self).__init__(**kwargs)

    def build(self, input_shape):
        self.mean_W = self.add_weight('mean_W',trainable=True)

        self.kernel_dist = tfp.distributions.MultivariateNormalDiag(
            loc=self.mean_W,
            scale_diag=(1.,)
        )
        super(test_layer, self).build(input_shape)

    def call(self,x):
        return tfp.distributions.kl_divergence(
            self.kernel_dist,
            tfp.distributions.MultivariateNormalDiag(
                loc=self.mean_W*0.,
                scale_diag=(1.,)
            )
        )

# 2 Create model

x = Input(shape=(3,))
fx = test_layer()(x)
test_model = Model(name='test_random', inputs=[x], outputs=[fx])


# 3 Calculate gradient

print('\n\n\nCalculating gradients: ')

# example data, only used as a dummy
x_data = np.random.rand(99,3).astype(np.float32)

for x_now in np.split(x_data,3):
#     print(x_now.shape)
    with tf.GradientTape() as tape:
        fx_now = test_model(x_now)
        grads = tape.gradient(
            fx_now,
            test_model.trainable_variables,
        )
        print('\nKL-Divergence: ', fx_now, '\nGradient: ',grads,'\n')

print(test_model.summary())

The output of the code above is

Calculating gradients: 

KL-Divergence:  tf.Tensor(0.0029436834, shape=(), dtype=float32) 
Gradient:  [<tf.Tensor: id=237, shape=(), dtype=float32, numpy=0.0>] 


KL-Divergence:  tf.Tensor(0.0029436834, shape=(), dtype=float32) 
Gradient:  [<tf.Tensor: id=358, shape=(), dtype=float32, numpy=0.0>] 


KL-Divergence:  tf.Tensor(0.0029436834, shape=(), dtype=float32) 
Gradient:  [<tf.Tensor: id=479, shape=(), dtype=float32, numpy=0.0>] 

Model: "test_random"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 3)]               0         
_________________________________________________________________
test_layer_3 (test_layer)    ()                        1         
=================================================================
Total params: 1
Trainable params: 1
Non-trainable params: 0
_________________________________________________________________
None

The KL divergence is calculated correcly, but the resulting gradient is 0. What would be a correct way to obtain the gradients?

Upvotes: 2

Views: 1641

Answers (2)

Brian Patton
Brian Patton

Reputation: 1076

We are working our way through distributions & bijectors, making them friendly to closing over variables in the constructor. (Have not yet done the MVNs.) In the meantime, you could use tfd.Independent(tfd.Normal(loc=self.mean_W, scale=1), reinterpreted_batch_ndims=1) which I think will work inside your build method because we've adapted Normal.

Also: have you seen the tfp.layers package? In particular https://www.tensorflow.org/probability/api_docs/python/tfp/layers/KLDivergenceAddLoss might be interesting to you.

Upvotes: 2

I. Schubert
I. Schubert

Reputation: 306

If anybody should be interested, I found out how to solve this:

The line

self.kernel_dist = tfp.distributions.MultivariateNormalDiag(
            loc=self.mean_W,
            scale_diag=(1.,)
        )

should not be inside the build()- method of the layer class definition, but rather inside the call() method. Here is the modified example:

import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Layer,Input

# 1 Define Layer

class test_layer(Layer):

    def __init__(self, **kwargs):
        super(test_layer, self).__init__(**kwargs)

    def build(self, input_shape):
        self.mean_W = self.add_weight('mean_W',trainable=True)
        super(test_layer, self).build(input_shape)

    def call(self,x):
        self.kernel_dist = tfp.distributions.MultivariateNormalDiag(
            loc=self.mean_W,
            scale_diag=(1.,)
        )
        return tfp.distributions.kl_divergence(
            self.kernel_dist,
            tfp.distributions.MultivariateNormalDiag(
                loc=self.mean_W*0.,
                scale_diag=(1.,)
            )
        )

# 2 Create model

x = Input(shape=(3,))
fx = test_layer()(x)
test_model = Model(name='test_random', inputs=[x], outputs=[fx])


# 3 Calculate gradient

print('\n\n\nCalculating gradients: ')

# example data, only used as a dummy
x_data = np.random.rand(99,3).astype(np.float32)

for x_now in np.split(x_data,3):
#     print(x_now.shape)
    with tf.GradientTape() as tape:
        fx_now = test_model(x_now)
        grads = tape.gradient(
            fx_now,
            test_model.trainable_variables,
        )
        print('\nKL-Divergence: ', fx_now, '\nGradient: ',grads,'\n')

print(test_model.summary())

The output now is



Calculating gradients: 

KL-Divergence:  tf.Tensor(0.024875917, shape=(), dtype=float32) 
Gradient:  [<tf.Tensor: id=742, shape=(), dtype=float32, numpy=0.22305119>] 


KL-Divergence:  tf.Tensor(0.024875917, shape=(), dtype=float32) 
Gradient:  [<tf.Tensor: id=901, shape=(), dtype=float32, numpy=0.22305119>] 


KL-Divergence:  tf.Tensor(0.024875917, shape=(), dtype=float32) 
Gradient:  [<tf.Tensor: id=1060, shape=(), dtype=float32, numpy=0.22305119>] 

Model: "test_random"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         [(None, 3)]               0         
_________________________________________________________________
test_layer_1 (test_layer)    ()                        1         
=================================================================
Total params: 1
Trainable params: 1
Non-trainable params: 0
_________________________________________________________________
None

as expected.

Is this something that was changed from tensorflow 1 to tensorflow 2?

Upvotes: 2

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