diff --git a/src_pyeddl/train_full_model_onnx.py b/src_pyeddl/train_full_model_onnx.py
new file mode 100644
index 0000000..2e4eaa4
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+++ b/src_pyeddl/train_full_model_onnx.py
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+#Imports
+import numpy as np
+import pandas as pd
+
+# Utility file
+import ModelHandler as mh
+
+# sklearn
+from sklearn.utils import shuffle
+from sklearn.model_selection import train_test_split
+
+
+if __name__ == "__main__":
+    # Msc
+    temp_data_folder = "../temp_data/"
+    model_file = "../model/model.bin"
+    data_folder = "/shares/eslfiler1/scratch/teijeiro/chbmit_deephealth/"
+    signal_length = "1min"
+
+    # processing
+    validation_size=0.15
+
+    # Model
+    epochs = 50
+    batch_size = 32
+    learning_rate = 0.0075 # 0.00075
+    handler = mh.ModelHandler()
+
+    with open(data_folder+"signal_mit_"+signal_length+".csv", "rb") as file:
+        x_data_ = file.read().splitlines()
+
+    y_data = np.loadtxt(data_folder+"labels_mit_"+signal_length+".txt")
+    info_data = np.loadtxt(data_folder+"infos_mit_"+signal_length+".txt", dtype="str")
+
+    # Convert from string
+    x_data = []
+    for sig in x_data_:
+      x_data.append(np.fromstring(sig, sep=','))
+
+    # Reshape in 2D as data are 1D in csv file
+    x_data = [np.reshape(np.array(x_data_i), (handler.NB_CHNS, int(len(x_data_i)/handler.NB_CHNS))) for x_data_i in x_data]
+
+    # Create the pandas df
+    data = pd.concat([pd.Series(y_data), pd.Series([info[0] for info in info_data]),pd.Series([info[1] for info in info_data])], axis=1)
+    data.columns = ["label", "patient", "file"]
+    data["signal"] = ""
+    data["signal"].astype(object)
+    for i, sig in enumerate(x_data):
+        data.at[i, "signal"] = sig
+
+    patients = np.unique(data.patient)
+    data.sort_values(["patient", "file", "label"], inplace=True)
+    data = data.reset_index(drop=True)
+    
+    # Cutting the signals in 5-second segments for training
+    x_train, y_train, _, _ = handler.cut_signal_data(data, data[data.patient==None])
+    
+    # Train/validation splitting
+    x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=validation_size, stratify=y_train)
+    x_train = np.array(x_train)
+    x_val = np.array(x_val)
+    x_train = x_train.reshape((len(x_train), handler.NB_CHNS, 1280, 1))
+    x_val = x_val.reshape((len(x_val), handler.NB_CHNS, 1280, 1))
+    y_train = np.eye(2)[np.array(y_train).astype(int)]
+    y_val = np.eye(2)[np.array(y_val).astype(int)]
+    
+    #Model training
+    handler.train(model_file, epochs, batch_size, learning_rate, x_train, y_train, x_val, y_val)
+    
+    #Plain evaluation with the training set
+    handler.evaluate(model_file, x_train, y_train)
+    
+    #Model storage in ONNX
+    handler.save_to_onnx('uc13_epilepsy_net.onnx')