From 70724ab835ba40b04ed60942c7fdc523f3cac433 Mon Sep 17 00:00:00 2001
From: lucas_miranda <lucasmiranda42@gmail.com>
Date: Mon, 13 Jul 2020 11:33:26 +0200
Subject: [PATCH] Modified LSTMs to work with cuDNN implementation
---
source/model_utils.py | 17 +++++++++++++++++
source/models.py | 14 +++++++-------
2 files changed, 24 insertions(+), 7 deletions(-)
diff --git a/source/model_utils.py b/source/model_utils.py
index ecbdf8fb..fd718679 100644
--- a/source/model_utils.py
+++ b/source/model_utils.py
@@ -5,6 +5,7 @@ from keras import backend as K
from sklearn.metrics import silhouette_score
from tensorflow.keras.constraints import Constraint
from tensorflow.keras.layers import Layer
+import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
@@ -12,6 +13,22 @@ tfd = tfp.distributions
tfpl = tfp.layers
# Helper functions
+def far_away_uniform_initialiser(shape, minval=0, maxval=15, iters=1000000):
+ """
+ Returns a uniformly initialised matrix in which the columns are as far as possible
+ """
+ init_dist = 0
+ for i in range(iters):
+ temp = np.random.uniform(minval, maxval, shape)
+ dist = np.abs(np.linalg.norm(np.diff(temp)))
+
+ if dist > init_dist:
+ init_dist = dist
+ init = temp
+
+ return init.astype(np.float32)
+
+
def compute_kernel(x, y):
x_size = K.shape(x)[0]
y_size = K.shape(y)[0]
diff --git a/source/models.py b/source/models.py
index 95f5a403..ce692b69 100644
--- a/source/models.py
+++ b/source/models.py
@@ -193,21 +193,21 @@ class SEQ_2_SEQ_GMVAE:
self.overlap_loss = overlap_loss
if self.prior == "standard_normal":
+
+ init_means = far_away_uniform_initialiser(
+ [self.number_of_components, self.ENCODING], minval=0, maxval=15
+ )
+
self.prior = tfd.mixture.Mixture(
cat=tfd.categorical.Categorical(
probs=tf.ones(self.number_of_components) / self.number_of_components
),
components=[
tfd.Independent(
- tfd.Normal(
- loc=tf.random.uniform(
- shape=[self.ENCODING], minval=0, maxval=15
- ),
- scale=1,
- ),
+ tfd.Normal(loc=init_means[k], scale=1,),
reinterpreted_batch_ndims=1,
)
- for _ in range(self.number_of_components)
+ for k in range(self.number_of_components)
],
)
--
GitLab