From 78d8baef71dd84b1fab01648f232fffeb364a370 Mon Sep 17 00:00:00 2001
From: lucas_miranda <lucasmiranda42@gmail.com>
Date: Fri, 12 Mar 2021 01:24:49 +0100
Subject: [PATCH] Prototyped KNN_purity callback
---
deepof/model_utils.py | 85 ++++++++++++++++++++++---------------------
1 file changed, 44 insertions(+), 41 deletions(-)
diff --git a/deepof/model_utils.py b/deepof/model_utils.py
index 8a2fe23a..9c895bfb 100644
--- a/deepof/model_utils.py
+++ b/deepof/model_utils.py
@@ -186,10 +186,7 @@ class one_cycle_scheduler(tf.keras.callbacks.Callback):
self.iteration += 1
K.set_value(self.model.optimizer.lr, rate)
- def on_batch_end(self, epoch, logs=None):
- """Add current learning rate as a metric, to check whether scheduling is working properly"""
-
- return self.last_rate
+ logs["learning_rate"] = self.last_rate
class knn_cluster_purity(tf.keras.callbacks.Callback):
@@ -208,51 +205,57 @@ class knn_cluster_purity(tf.keras.callbacks.Callback):
def on_epoch_end(self, batch: int, logs):
""" Passes samples through the encoder and computes cluster purity on the latent embedding """
- # Get encoer and grouper from full model
- cluster_means = [
- layer for layer in self.model.layers if layer.name == "cluster_means"
- ][0]
- cluster_assignment = [
- layer for layer in self.model.layers if layer.name == "cluster_assignment"
- ][0]
+ if self.validation_data is not None:
- encoder = tf.keras.models.Model(
- self.model.layers[0].input, cluster_means.output
- )
- grouper = tf.keras.models.Model(
- self.model.layers[0].input, cluster_assignment.output
- )
+ # Get encoer and grouper from full model
+ cluster_means = [
+ layer for layer in self.model.layers if layer.name == "cluster_means"
+ ][0]
+ cluster_assignment = [
+ layer
+ for layer in self.model.layers
+ if layer.name == "cluster_assignment"
+ ][0]
- # Use encoder and grouper to predict on validation data
- encoding = encoder.predict(self.validation_data)
- groups = grouper.predict(self.validation_data)
+ encoder = tf.keras.models.Model(
+ self.model.layers[0].input, cluster_means.output
+ )
+ grouper = tf.keras.models.Model(
+ self.model.layers[0].input, cluster_assignment.output
+ )
- # Multiply encodings by groups, to get a weighted version of the matrix
- encoding = (
- encoding
- * tf.tile(groups, [1, encoding.shape[1] // groups.shape[1]]).numpy()
- )
- hard_groups = groups.argmax(axis=1)
+ print(self.validation_data)
- # Fit KNN model
- knn = NearestNeighbors().fit(encoding)
+ # Use encoder and grouper to predict on validation data
+ encoding = encoder.predict(self.validation_data)
+ groups = grouper.predict(self.validation_data)
- # Iterate over samples and compute purity over k neighbours
- random_idxs = np.random.choice(
- range(encoding.shape[0]), self.samples, replace=False
- )
- purity_vector = np.zeros(self.samples)
- for i, sample in enumerate(random_idxs):
- indexes = knn.kneighbors(
- encoding[sample][np.newaxis, :], self.k, return_distance=False
+ # Multiply encodings by groups, to get a weighted version of the matrix
+ encoding = (
+ encoding
+ * tf.tile(groups, [1, encoding.shape[1] // groups.shape[1]]).numpy()
)
- purity_vector[i] = (
- np.sum(hard_groups[indexes] == hard_groups[sample])
- / self.k
- * np.max(groups[sample])
+ hard_groups = groups.argmax(axis=1)
+
+ # Fit KNN model
+ knn = NearestNeighbors().fit(encoding)
+
+ # Iterate over samples and compute purity over k neighbours
+ random_idxs = np.random.choice(
+ range(encoding.shape[0]), self.samples, replace=False
)
+ purity_vector = np.zeros(self.samples)
+ for i, sample in enumerate(random_idxs):
+ indexes = knn.kneighbors(
+ encoding[sample][np.newaxis, :], self.k, return_distance=False
+ )
+ purity_vector[i] = (
+ np.sum(hard_groups[indexes] == hard_groups[sample])
+ / self.k
+ * np.max(groups[sample])
+ )
- return purity_vector.mean()
+ logs["knn_cluster_purity"] = purity_vector.mean()
class uncorrelated_features_constraint(Constraint):
--
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