diff --git a/aimodel/slurm-encoderonly-rainfall.job b/aimodel/slurm-encoderonly-rainfall.job
index b389f90..2c632ba 100755
--- a/aimodel/slurm-encoderonly-rainfall.job
+++ b/aimodel/slurm-encoderonly-rainfall.job
@@ -35,9 +35,9 @@ show_help() {
 	echo -e "    WINDOW_SIZE=33     The window size to use when convolving the input dataset for single pixel prediction." >&2;
 	echo -e "    STEPS_PER_EPOCH	The number of steps to consider an epoch. Defaults to None, which means use the entire dataset." >&2;
 	echo -e "    EPOCHS=25			The number of epochs to train for." >&2;
+	echo -e "    LEARNING_RATE      The learning rate to use. Default: 0.001." >&2;
 # 	echo -e "    NO_REMOVE_ISOLATED_PIXELS	Set to any value to avoid the engine from removing isolated pixels - that is, water pixels with no other surrounding pixels, either side to side to diagonally." >&2;
 # 	echo -e "    PATH_CHECKPOINT 	The path to a checkcpoint to load. If specified, a model will be loaded instead of being trained." >&2;
-# 	echo -e "    LEARNING_RATE      The learning rate to use. Default: 0.001." >&2;
 # 	echo -e "    PREDICT_COUNT		The number of items from the (SCRAMBLED) dataset to make a prediction for." >&2;
 	echo -e "    POSTFIX			Postfix to append to the output dir (auto calculated)." >&2;
 	echo -e "    ARGS				Optional. Any additional arguments to pass to the python program." >&2;
@@ -66,8 +66,8 @@ echo -e ">>> DIR_OUTPUT: ${DIR_OUTPUT}";
 echo -e ">>> Additional args: ${ARGS}";
 
 export PATH=$HOME/software/bin:$PATH;
-export BATCH_SIZE DIRPATH_RAINFALLWATER PATH_HEIGHTMAP STEPS_PER_EPOCH DIRPATH_OUTPUT PATH_CHECKPOINT CHANNELS WINDOW_SIZE EPOCHS;
-#LOSS LEARNING_RATE;
+export BATCH_SIZE DIRPATH_RAINFALLWATER PATH_HEIGHTMAP STEPS_PER_EPOCH DIRPATH_OUTPUT PATH_CHECKPOINT CHANNELS WINDOW_SIZE EPOCHS LEARNING_RATE;
+#LOSS ;
 
 echo ">>> Installing requirements";
 conda run -n py38 pip install -q -r requirements.txt;
diff --git a/aimodel/src/encoderonly_test_rainfall.py b/aimodel/src/encoderonly_test_rainfall.py
index 636111b..9de1e9d 100755
--- a/aimodel/src/encoderonly_test_rainfall.py
+++ b/aimodel/src/encoderonly_test_rainfall.py
@@ -22,10 +22,11 @@ DIRPATH_OUTPUT	= os.environ["DIRPATH_OUTPUT"]
 PATH_HEIGHTMAP	= os.environ["PATH_HEIGHTMAP"]			if "PATH_HEIGHTMAP"		in os.environ else None
 CHANNELS		= os.environ["CHANNELS"]				if "CHANNELS"			in os.environ else 8
 
-EPOCHS			= int(os.environ["EPOCHS"])			if "EPOCHS"			in os.environ else 25
+EPOCHS			= int(os.environ["EPOCHS"])				if "EPOCHS"				in os.environ else 25
 BATCH_SIZE		= int(os.environ["BATCH_SIZE"])			if "BATCH_SIZE"			in os.environ else 64
 WINDOW_SIZE		= int(os.environ["WINDOW_SIZE"])		if "WINDOW_SIZE"		in os.environ else 33
 STEPS_PER_EPOCH = int(os.environ["STEPS_PER_EPOCH"])	if "STEPS_PER_EPOCH"	in os.environ else None
+LEARNING_RATE	= float(os.environ["LEARNING_RATE"])	if "LEARNING_RATE"		in os.environ else 0.001
 
 logger.info("Encoder-only rainfall radar TEST")
 logger.info(f"> DIRPATH_RAINFALLWATER {DIRPATH_RAINFALLWATER}")
@@ -35,6 +36,7 @@ logger.info(f"> CHANNELS {CHANNELS}")
 logger.info(f"> BATCH_SIZE {BATCH_SIZE}")
 logger.info(f"> WINDOW_SIZE {WINDOW_SIZE}")
 logger.info(f"> STEPS_PER_EPOCH {STEPS_PER_EPOCH}")
+logger.info(f"> LEARNING_RATE {LEARNING_RATE}")
 
 
 if not os.path.exists(DIRPATH_OUTPUT):
@@ -89,7 +91,7 @@ def make_encoderonly(windowsize, channels, encoder="convnext", water_bins=2, **k
 		raise Exception(f"Error: Unknown encoder '{encoder}' (known encoders: convnext, resnet).")
 	
 	model.compile(
-		optimizer="Adam",
+		optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE),
 		loss = tf.keras.losses.SparseCategoricalCrossentropy(),
 		metrics = [
 			tf.keras.metrics.SparseCategoricalAccuracy()