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Commit 0e0631e9 authored by Ard Kastrati's avatar Ard Kastrati
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Merge branch 'master' of https://gitlab.ethz.ch/kard/dl-project

parents 32e4dda0 087efece
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ConvNet.py
View file @ 0e0631e9
......@@ -115,7 +115,7 @@ class ConvNet(ABC):
# early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=20)
ckpt_dir = config['model_dir'] + '/' + config['model'] + '_' + 'best_model.h5'
ckpt = tf.keras.callbacks.ModelCheckpoint(ckpt_dir, verbose=1, monitor='val_accuracy', save_best_only=True, mode='auto')
X_train, X_val, y_train, y_val = train_test_split(x, y, test_size=0.2, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(x, y, test_size=0.199182, shuffle=False)
prediction_ensemble = prediction_history((X_val,y_val))
hist = self.model.fit(X_train, y_train, verbose=2, batch_size=self.batch_size, validation_data=(X_val,y_val),
epochs=self.epochs, callbacks=[csv_logger, ckpt, prediction_ensemble])
......
EEGNet/eegNet.py
View file @ 0e0631e9
......@@ -149,7 +149,7 @@ class Classifier_EEGNet:
early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=20)
ckpt_dir = config['model_dir'] + '/' + config['model'] + '_' + 'best_model.h5'
ckpt = tf.keras.callbacks.ModelCheckpoint(ckpt_dir, verbose=1, monitor='val_accuracy', save_best_only=True, mode='auto')
X_train, X_val, y_train, y_val = train_test_split(eegnet_x, y, test_size=0.2, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(eegnet_x, y, test_size=0.199182, shuffle=False)
pred_ensemble = prediction_history((X_val,y_val))
hist = self.model.fit(X_train, y_train, verbose=1, validation_data=(X_val,y_val),
epochs=self.epochs, callbacks=[csv_logger, ckpt, early_stop,pred_ensemble])
......
config.py
View file @ 0e0631e9
......@@ -42,8 +42,7 @@ Cluster can be set to clustering(), clustering2() or clustering3(), where differ
"""
# Choosing model
config['model'] = 'eegnet'
config['model'] = 'cnn'
config['model'] = 'xception'
config['downsampled'] = False
config['split'] = False
config['cluster'] = clustering()
......
main.py
View file @ 0e0631e9
......@@ -18,7 +18,7 @@ def main():
logging.info('Started the Logging')
start_time = time.time()
# try:
# trainX, trainY = IOHelper.get_mat_data(config['data_dir'], verbose=True)
trainX, trainY = IOHelper.get_mat_data(config['data_dir'], verbose=True)
# if config['model'] == 'eegnet' or config['model'] == 'eegnet_cluster':
# trainX = np.transpose(trainX, (0, 2, 1))
......@@ -26,9 +26,9 @@ def main():
# tune(trainX,trainY)
# run(trainX,trainY)
run(trainX,trainY)
# select_best_model()
comparison_plot_loss()
# comparison_plot_loss()
logging.info("--- Runtime: %s seconds ---" % (time.time() - start_time))
logging.info('Finished Logging')
......
results/ETRA/cnn_ensemble/batches.log 0 → 100644
View file @ 0e0631e9
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results/ETRA/cnn_ensemble/cnn_ensemble_df_best_model.csv 0 → 100644
View file @ 0e0631e9
best_model_train_loss,best_model_val_loss,best_model_train_acc,best_model_val_acc
0.056600186973810196,0.2665606141090393,0.9790058135986328,0.9002079002079002
results/ETRA/cnn_ensemble/cnn_ensemble_history.csv 0 → 100644
View file @ 0e0631e9
loss,accuracy,val_loss,val_accuracy
0.65091872215271,0.605901837348938,0.4958110749721527,0.7586971586971587
0.5022376775741577,0.7451393008232117,0.4418316185474396,0.7916839916839917
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0.28742745518684387,0.8722766041755676,0.36084964871406555,0.8476784476784477
0.26396888494491577,0.8845146298408508,0.362216979265213,0.8442134442134442
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0.03078307956457138,0.989382266998291,0.26229792833328247,0.8972972972972973
results/ETRA/cnn_ensemble/info.log 0 → 100644
View file @ 0e0631e9
INFO:root:Started the Logging
INFO:root:X training loaded.
INFO:root:(129, 500, 36223)
INFO:root:y training loaded.
INFO:root:(1, 36223)
INFO:root:Setting the shapes
INFO:root:(36223, 500, 129)
INFO:root:(36223, 1)
INFO:root:Started running cnn. If you want to run other methods please choose another model in the config.py file.
INFO:root:Parameters:
INFO:root:--------------- use residual : True
INFO:root:--------------- depth : 12
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 64
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : True
INFO:root:--------------- depth : 12
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 64
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : True
INFO:root:--------------- depth : 12
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 64
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : True
INFO:root:--------------- depth : 12
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 64
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:Parameters:
INFO:root:--------------- use residual : True
INFO:root:--------------- depth : 12
INFO:root:--------------- batch size : 64
INFO:root:--------------- kernel size : 64
INFO:root:--------------- nb filters : 16
INFO:root:--------------- preprocessing: False
INFO:root:**********
INFO:root:--- Runtime: 4655.170459032059 seconds ---
INFO:root:Finished Logging
results/ETRA/cnn_ensemble/lsf.o15306043 0 → 100644
View file @ 0e0631e9
This diff is collapsed.
results/ETRA/eegnet_ensemble/batches.log 0 → 100644
View file @ 0e0631e9
epoch;accuracy;loss;val_accuracy;val_loss
0;0.7336596846580505;0.5454949736595154;0.7602217793464661;0.5246629118919373
1;0.7818877696990967;0.4733167886734009;0.800970196723938;0.46129629015922546
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