def train_net_intra(model, file):
appX = []
appy = []
init = time.time()
X, flash = prefilter.prepare_data(file, cutoff=[0.5, 30], fs=250.0)
X_clean, y_clean = prefilter.clean_data(X, flash)
appX.append(X_clean)
appy.append(y_clean)
X = [subject for subject in appX]
y = [subject for subject in appy]
X_train, X_valid, y_train, y_valid = train_test_split(np.vstack(X),
np.vstack(y),
test_size=0.1)
history = model.fit(X_train,
y_train,
validation_data=(X_valid, y_valid),
batch_size=10,
epochs=50,
verbose=1)
end = time.time()
print("time elapsed training is:", (end - init) / 60, " minutes")
return history.history['accuracy'], history.history[
'val_accuracy'], history.history['loss'], history.history['val_loss']
modeling.model_compile(model)
acc, val_acc, loss, val_loss = modeling.train_net(model, files)
plt.rcParams["figure.figsize"] = (10, 7)
plt.plot(acc)
plt.plot(val_acc)
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Valid'], loc='upper left')
plt.show()
from sklearn.model_selection import train_test_split
X, Flash = prefilter.prepare_data(test_subject, cutoff=[0.5, 30], fs=250.0)
X_clean, y_clean = prefilter.clean_data(X, Flash)
X_train, X_test, y_train, y_test = train_test_split(X_clean,
y_clean,
test_size=0.1)
history = model.fit(X_train, y_train, batch_size=1, epochs=30)
plt.plot(history.history['accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Test'], loc='upper left')
plt.show()
# Plot test loss values
plt.plot(history.history['loss'])