From b3941ccfc9cbe6bc688c21c405cebdd35fc4d639 Mon Sep 17 00:00:00 2001
From: lucas_miranda <lucasmiranda42@gmail.com>
Date: Wed, 10 Jun 2020 10:52:50 +0200
Subject: [PATCH] Changed full model for diagonal model in all variational
implementations in models.py
---
main.ipynb | 2 +-
source/model_utils.py | 36 ++++++++++++++++++++++++++++++------
source/models.py | 11 +++++------
3 files changed, 36 insertions(+), 13 deletions(-)
diff --git a/main.ipynb b/main.ipynb
index 9197697d..687ad9ae 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -371,7 +371,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# vaep.summary()"
+ "vaep.summary()"
]
},
{
diff --git a/source/model_utils.py b/source/model_utils.py
index 93a23534..5dfd7058 100644
--- a/source/model_utils.py
+++ b/source/model_utils.py
@@ -109,13 +109,37 @@ class UncorrelatedFeaturesConstraint(Constraint):
return self.weightage * self.uncorrelated_feature(x)
-class GaussianMixtureLayer(Layer):
- def __init(self, *args, **kwargs):
- self.is_placeholder = True
- super(GaussianMixtureLayer, self).__init__(*args, **kwargs)
+class MultivariateNormalDiag(tfpl.DistributionLambda):
+ def __init__(
+ self,
+ event_size,
+ convert_to_tensor_fn=tfd.Distribution.sample,
+ validate_args=False,
+ **kwargs
+ ):
+
+ super(MultivariateNormalDiag, self).__init__(
+ lambda t: MultivariateNormalDiag.new(t, event_size, validate_args),
+ convert_to_tensor_fn,
+ **kwargs
+ )
- def call(self, inputs, **kwargs):
- pass
+ @staticmethod
+ def new(params, event_size, validate_args=False, name=None):
+ """Create the distribution instance from a `params` vector."""
+ with tf.name_scope(name or "MultivariateNormalDiag"):
+ params = tf.convert_to_tensor(params, name="params")
+ return tfd.mvn_diag.MultivariateNormalDiag(
+ loc=params[..., :event_size],
+ scale_diag=params[..., event_size:],
+ validate_args=validate_args,
+ )
+
+ @staticmethod
+ def params_size(event_size, name=None):
+ """The number of `params` needed to create a single distribution."""
+ with tf.name_scope(name or "MultivariateNormalDiag_params_size"):
+ return 2 * event_size
class KLDivergenceLayer(tfpl.KLDivergenceAddLoss):
diff --git a/source/models.py b/source/models.py
index a2bda982..691098cb 100644
--- a/source/models.py
+++ b/source/models.py
@@ -282,7 +282,7 @@ class SEQ_2_SEQ_VAE:
encoder = Model_E5(encoder)
encoder = Dense(
- tfpl.MultivariateNormalTriL.params_size(self.ENCODING), activation=None
+ MultivariateNormalDiag.params_size(self.ENCODING), activation=None
)(encoder)
# Define and control custom loss functions
@@ -299,7 +299,7 @@ class SEQ_2_SEQ_VAE:
)
)
- z = tfpl.MultivariateNormalTriL(self.ENCODING)(encoder)
+ z = MultivariateNormalDiag(self.ENCODING)(encoder)
if "ELBO" in self.loss:
z = KLDivergenceLayer(self.prior, weight=kl_beta)(z)
@@ -495,7 +495,7 @@ class SEQ_2_SEQ_VAEP:
encoder = Model_E5(encoder)
encoder = Dense(
- tfpl.MultivariateNormalTriL.params_size(self.ENCODING), activation=None
+ MultivariateNormalDiag.params_size(self.ENCODING), activation=None
)(encoder)
# Define and control custom loss functions
@@ -511,7 +511,7 @@ class SEQ_2_SEQ_VAEP:
)
)
- z = tfpl.MultivariateNormalTriL(self.ENCODING)(encoder)
+ z = MultivariateNormalDiag(self.ENCODING)(encoder)
if "ELBO" in self.loss:
z = KLDivergenceLayer(self.prior, weight=kl_beta)(z)
@@ -585,7 +585,7 @@ class SEQ_2_SEQ_VAEP:
# end-to-end autoencoder
encoder = Model(x, z, name="SEQ_2_SEQ_VEncoder")
vaep = Model(
- inputs=x, outputs=[x_decoded_mean, x_predicted_mean], name="SEQ_2_SEQ_VAE"
+ inputs=x, outputs=[x_decoded_mean, x_predicted_mean], name="SEQ_2_SEQ_VAEP"
)
# Build generator as a separate entity
@@ -883,7 +883,6 @@ class SEQ_2_SEQ_MMVAEP:
# TODO:
-# - Try sample, mean and mode for MMDiscrepancyLayer
# - Gaussian Mixture + Categorical priors -> Deep Clustering
# - prior of equal gaussians
# - prior of equal gaussians + gaussian noise on the means (not exactly the same init)
--
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