From 82e1049d58bc050c99e995af303c3e4279b8ca59 Mon Sep 17 00:00:00 2001
From: Frank Sauerburger <f.sauerburger@cern.ch>
Date: Wed, 1 Jul 2020 10:06:37 +0200
Subject: [PATCH] Store fold models in separate h5 files
---
nnfwtbn/model.py | 24 ++++++++++++++----------
1 file changed, 14 insertions(+), 10 deletions(-)
diff --git a/nnfwtbn/model.py b/nnfwtbn/model.py
index c4f7309..9057edc 100644
--- a/nnfwtbn/model.py
+++ b/nnfwtbn/model.py
@@ -636,18 +636,21 @@ class HepNet:
Save the model and all associated components to a hdf5 file.
"""
+ # save model architecture and weights (only if already trained)
+ if len(self.models) == self.cv.k:
+ for fold_i in range(self.cv.k):
+ path_token = path.rsplit(".", 1)
+ path_token.insert(-1, f"fold_{fold_i}")
+
+ # this is the built-in save function from keras
+ self.models[fold_i].save(".".join(path_token))
+
with h5py.File(path, "w") as output_file:
# save default model class
# since this is a arbitrary piece of python code we need to use the python_to_str function
group = output_file.create_group("models/default")
group.attrs["model_cls"] = np.string_(python_to_str(self.model_cls))
- # save model architecture and weights (only if already trained)
- if len(self.models) == self.cv.k:
- for fold_i in range(self.cv.k):
- group = output_file.create_group("models/fold_{}".format(fold_i))
- # this is the built-in save function from keras
- self.models[fold_i].save(group)
# save class name of default normalizer as string
group = output_file.create_group("normalizers/default")
@@ -696,10 +699,11 @@ class HepNet:
# load trained models (if existing)
with h5py.File(path, "r") as input_file:
for fold_i in range(cv.k):
- key = "models/fold_{}".format(fold_i)
- if key in input_file:
- model = keras.models.load_model(input_file[key])
- instance.models.append(model)
+ path_token = path.rsplit(".", 1)
+ path_token.insert(-1, f"fold_{fold_i}")
+
+ model = keras.models.load_model(".".join(path_token))
+ instance.models.append(model)
# load normalizer
for fold_i in range(cv.k):
--
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