def train(self):
datah = DataHandler()
train_data = datah.getTrainSplit()
print(type(train_data))
print(np.shape(train_data[0]))
print(np.shape(train_data[1]))
print(train_data[1])
# test_data=datah.getTestSplit()
# validation_data=datah.getValidationSplit()
saved = ModelCheckpoint(
"Weights/weights.{epoch:02d}-{val_loss:.2f}.hdf5",
monitor='val_loss',
verbose=0,
save_best_only=False,
save_weights_only=False,
mode='auto',
period=1)
self.model.fit(np.array(train_data[0]),
train_data[1],
initial_epoch=self.start_epoch,
validation_split=0.8,
epochs=10000,
batch_size=500,
verbose=1,
callbacks=[saved])
