From 6146894c57423229f02baf3a7f981a64e53bf836 Mon Sep 17 00:00:00 2001
From: lucas_miranda <lucasmiranda42@gmail.com>
Date: Wed, 19 May 2021 01:27:59 +0200
Subject: [PATCH] Replaced for loop with vectorised mapping on ClusterOverlap
regularization layer
---
deepof/model_utils.py | 16 ++++++++--------
deepof/train_utils.py | 6 +++---
2 files changed, 11 insertions(+), 11 deletions(-)
diff --git a/deepof/model_utils.py b/deepof/model_utils.py
index f6b8acba..064d4cb2 100644
--- a/deepof/model_utils.py
+++ b/deepof/model_utils.py
@@ -560,9 +560,9 @@ class ClusterOverlap(Layer):
self,
batch_size: int,
encoding_dim: int,
- k: int = 100,
+ k: int = 25,
loss_weight: float = 0.0,
- samples: int = 50,
+ samples: int = None,
*args,
**kwargs
):
@@ -572,6 +572,8 @@ class ClusterOverlap(Layer):
self.loss_weight = loss_weight
self.min_confidence = 0.25
self.samples = samples
+ if self.samples is None:
+ self.samples = self.batch_size
super(ClusterOverlap, self).__init__(*args, **kwargs)
def get_config(self): # pragma: no cover
@@ -595,8 +597,8 @@ class ClusterOverlap(Layer):
max_groups = tf.reduce_max(categorical, axis=1)
# Iterate over samples and compute purity across neighbourhood
- self.samples = np.min([self.samples, self.batch_size]) # convert to numpy
- random_idxs = range(self.batch_size) # convert to batch size
+ self.samples = np.min([self.samples, self.batch_size])
+ random_idxs = range(self.batch_size)
random_idxs = np.random.choice(random_idxs, self.samples)
get_local_neighbourhood_entropy = partial(
@@ -628,9 +630,7 @@ class ClusterOverlap(Layer):
neighbourhood_entropy, aggregation="mean", name="neighbourhood_entropy"
)
- # if self.loss_weight:
- # self.add_loss(
- # self.loss_weight * neighbourhood_entropy, inputs=inputs
- # )
+ if self.loss_weight:
+ self.add_loss(self.loss_weight * tf.reduce_mean(neighbourhood_entropy))
return encodings
diff --git a/deepof/train_utils.py b/deepof/train_utils.py
index 9ac9f1dc..85042360 100644
--- a/deepof/train_utils.py
+++ b/deepof/train_utils.py
@@ -324,7 +324,7 @@ def autoencoder_fitting(
X_val_dataset = (
tf.data.Dataset.from_tensor_slices(X_val)
.with_options(options)
- .batch(batch_size * strategy.num_replicas_in_sync)
+ .batch(batch_size * strategy.num_replicas_in_sync, drop_remainder=True)
)
# Defines what to log on tensorboard (useful for trying out different models)
@@ -441,13 +441,13 @@ def autoencoder_fitting(
# Convert data to tf.data.Dataset objects
train_dataset = (
tf.data.Dataset.from_tensor_slices((Xs, tuple(ys)))
- .batch(batch_size * strategy.num_replicas_in_sync)
+ .batch(batch_size * strategy.num_replicas_in_sync, drop_remainder=True)
.shuffle(buffer_size=X_train.shape[0])
.with_options(options)
)
val_dataset = (
tf.data.Dataset.from_tensor_slices((Xvals, tuple(yvals)))
- .batch(batch_size * strategy.num_replicas_in_sync)
+ .batch(batch_size * strategy.num_replicas_in_sync, drop_remainder=True)
.with_options(options)
)
--
GitLab