From 48ddb38d9551fea0eedc2685e1559c4f425abd5d Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Wed, 2 Mar 2022 15:22:38 +0100
Subject: [PATCH] added optimizer to CNNfromConfig
---
mlair/model_modules/convolutional_networks.py | 17 +++++++++++++++--
1 file changed, 15 insertions(+), 2 deletions(-)
diff --git a/mlair/model_modules/convolutional_networks.py b/mlair/model_modules/convolutional_networks.py
index 8efe71a9..486441db 100644
--- a/mlair/model_modules/convolutional_networks.py
+++ b/mlair/model_modules/convolutional_networks.py
@@ -56,7 +56,7 @@ class CNNfromConfig(AbstractModelClass):
"""
- def __init__(self, input_shape: list, output_shape: list, layer_configuration: list, **kwargs):
+ def __init__(self, input_shape: list, output_shape: list, layer_configuration: list, optimizer="adam", **kwargs):
assert len(input_shape) == 1
assert len(output_shape) == 1
@@ -67,9 +67,9 @@ class CNNfromConfig(AbstractModelClass):
self.activation_output = self._activation.get(activation_output)
self.activation_output_name = activation_output
self.kwargs = kwargs
+ self.optimizer = self._set_optimizer(optimizer, **kwargs)
# apply to model
-
self.set_model()
self.set_compile_options()
self.set_custom_objects(loss=custom_loss([keras.losses.mean_squared_error, var_loss]), var_loss=var_loss)
@@ -90,6 +90,19 @@ class CNNfromConfig(AbstractModelClass):
self.model = keras.Model(inputs=x_input, outputs=[out])
print(self.model.summary())
+ def _set_optimizer(self, optimizer, **kwargs):
+ try:
+ opt_name = optimizer.lower()
+ opt = self._optimizer.get(opt_name)
+ opt_kwargs = {}
+ if opt_name == "adam":
+ opt_kwargs = select_from_dict(kwargs, ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad"])
+ elif opt_name == "sgd":
+ opt_kwargs = select_from_dict(kwargs, ["lr", "momentum", "decay", "nesterov"])
+ return opt(**opt_kwargs)
+ except KeyError:
+ raise AttributeError(f"Given optimizer {optimizer} is not supported in this model class.")
+
def _set_regularizer(self, regularizer, **kwargs):
if regularizer is None or (isinstance(regularizer, str) and regularizer.lower() == "none"):
return None
--
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