def training_all_data(self, model, x, y, splits, patience_value=25):
for i in splits:
# shuffle, sort = 0
x_train = x[i[0]]
y_train = y[i[0]]
x_val = x[i[1]]
y_val = y[i[1]]
self.sp_obj = sp(patience=patience_value,
verbose=True,
save_the_best=True)
self.sp_obj.set_validation_data((x_val, y_val))
# print("FOLD: ", i)
hist = self.training(model, x_train, y_train, x_val, y_val)
histories_fold = {}
histories_fold['val_loss'] = hist.history['val_loss'][-1]
histories_fold['val_acc'] = hist.history['val_acc'][-1]
histories_fold['loss'] = hist.history['loss'][-1]
histories_fold['acc'] = hist.history['acc'][-1]
histories_fold['max_sp_val'] = hist.history['max_sp_val'][-1]
histories_fold['max_sp_fa_val'] = hist.history['max_sp_fa_val'][-1]
histories_fold['max_sp_pd_val'] = hist.history['max_sp_pd_val'][-1]
histories_fold['max_sp_best_epoch_val'] = hist.history[
'max_sp_best_epoch_val'][-1]
self.histories.append(histories_fold)
self.weights_model.append(model.get_weights())
best_sp = []
for i in self.histories:
best_sp.append(i['max_sp_val'])
model.set_weights(self.weights_model[np.argmax(best_sp)])
return model
def training(self, model, x, y, x_val, y_val, patience_value=25):
self.sp_obj = sp(patience=patience_value,
verbose=True,
save_the_best=True)
return model.fit(x,
y,
epochs=300,
batch_size=1024,
verbose=False,
validation_data=(x_val, y_val),
callbacks=[self.sp_obj],
class_weight=compute_class_weight(
'balanced', np.unique(y), y),
shuffle=True)
model = Sequential()
model.add(Conv1D(16, kernel_size=2, activation='relu', input_shape=(100, 1)))
model.add(Conv1D(32, kernel_size=2, activation='relu'))
model.add(Flatten())
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='linear'))
model.add(Activation('sigmoid'))
# compile the model
model.compile(
'adam',
loss='binary_crossentropy',
metrics=['acc'],
)
sp_obj = sp(patience=25, verbose=True, save_the_best=True)
# sp_obj.set_validation_data( (x_val, y_val) )
for i in splits:
# shuffle, sort = 0
x = data[i[0]]
y = target[i[0]]
x_val = data[i[1]]
y_val = target[i[1]]
sp_obj.set_validation_data((x_val, y_val))
# train the model
history = model.fit(x,
y,
epochs=10,
batch_size=1024,
def __init__(self):
self.model = Sequential()
self.sp_obj = sp(patience=25, verbose=True, save_the_best=True)
self.histories = []
self.weights_model = []