diff --git a/decodertest.py b/decodertest.py
deleted file mode 100644
index e150970..0000000
--- a/decodertest.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# --*-- coding: utf-8 --*--
-"""
-train.py
-
-Launches the training process for the model.
-"""
-
-import os
-from pathlib import Path
-
-os.environ["KERAS_BACKEND"] = "jax"
-os.environ["XLA_PYTHON_CLIENT_PREALLOCATE"] = "false"
-
-import jax
-import json
-import numpy as np
-#from pyspark.ml.feature import OneHotEncoder, StringIndexer
-from pyspark.sql import SparkSession, functions, types, Row
-import pyspark as spark
-import tensorflow as tf
-import keras
-import keras.metrics as metrics
-import matplotlib.pyplot as plt
-from sklearn.metrics import auc, confusion_matrix, roc_curve
-from sklearn.preprocessing import OneHotEncoder, LabelBinarizer
-
-from model.model import CompoundModel, DecoderModel
-from pipe.etl import etl, read
-import train.decoder_train as dt
-from visualize.plot import spectra_plot
-
-def multi_gpu():
-    devices = jax.devices("gpu")
-    mesh = keras.distribution.DeviceMesh(
-        shape=(len(devices),), axis_names=["model"], devices=devices
-    )
-    layout_map = keras.distribution.LayoutMap(mesh)
-    layout_map["dense.*kernel"] = (None, "model")
-    layout_map["dense.*bias"] = ("model",)
-    layout_map["dense.*kernel_regularizer"] = (None, "model")
-    layout_map["dense.*bias_regularizer"] = ("model",)
-    layout_map["dense.*activity_regularizer"] = (None,)
-    layout_map["dense.*kernel_constraint"] = (None, "model")
-    layout_map["dense.*bias_constraint"] = ("model",)
-    layout_map["conv1d.*kernel"] = (None, None, None, "model")
-    layout_map["conv1d.*kernel_regularizer"] = (None, None, None, "model")
-    layout_map["conv1d.*bias_regularizer"] = ("model",)
-
-    model_parallel = keras.distribution.ModelParallel(
-            layout_map=layout_map
-    )
-    keras.distribution.set_distribution(model_parallel)
-
-def main():
-
-    with open("parameters.json", "r") as file:
-        params = json.load(file)
-
-    spark = SparkSession.builder.appName("train").getOrCreate()
-
-    keys = ["treatment", "target"]
-    
-    if params["load_from_scratch"]:
-        (train_set, validation_set,
-         test_set, categories) = etl(spark, split=params["split"])
-    else:
-        (train_set, validation_set,
-         test_set, categories) = read(spark, split=params["split"])
-        
-
-    n_classes = [dset.shape[1] for dset in train_set[1]]
-    decoder = dt.load_decoder(params, train_set[0].shape[1:], n_classes)
-
-    decoder = dt.train_decoder(decoder, params, train_set, validation_set)
-    # Test model performance
-
-    metrics = {key: None for key in keys}
-    metrics, test_predict = dt.test_decoder(decoder, test_set, metrics)
-
-    spectra_plot(test_predict[0], name=None)
-
-
-if __name__ == "__main__":
-    main()
-
-# EOF
diff --git a/model/model.py b/model/model.py
index be7b97b..d2b5251 100644
--- a/model/model.py
+++ b/model/model.py
@@ -11,6 +11,7 @@
         GlobalAveragePooling1D, LayerNormalization, Masking, Conv2D, \
         MultiHeadAttention, concatenate
 
+from model.activation import sublinear
 from model.transformer import TimeseriesTransformerBuilder as TSTFBuilder
 
 class CompoundModel(Model):
@@ -205,7 +206,7 @@ def _modelstack(self, input_shape, head_size, num_heads, ff_dim,
         x = Conv1D(filters=input_shape[1],
                    kernel_size=1,
                    padding="valid",
-                   activation="linear")(x)
+                   activation=sublinear)(x)
 
         return Model(inputs, x)
 
diff --git a/train_encoder.py b/train_model.py
similarity index 100%
rename from train_encoder.py
rename to train_model.py