def test_ensemble(RGB_image, HSI_image):
batch, height, width, channels = HSI_image[0].shape
sensor_inputs, sensor_outputs, spatial, spectral = Hang.define_model(
classes=2, height=height, width=width, channels=channels)
model1 = tf.keras.Model(inputs=sensor_inputs, outputs=sensor_outputs)
batch, height, width, channels = RGB_image[0].shape
sensor_inputs, sensor_outputs, spatial, spectral = Hang.define_model(
classes=2, height=height, width=width, channels=channels)
model2 = tf.keras.Model(inputs=sensor_inputs, outputs=sensor_outputs)
ensemble = Hang.ensemble(models=[model1, model2], classes=2)
prediction = ensemble.predict([HSI_image[0], RGB_image[0]])
assert prediction.shape == (1, 2)
def ensemble(self, experiment, class_weight=None, freeze = True, train=True):
#Manually override batch size
self.config["train"]["batch_size"] = self.config["train"]["ensemble"]["batch_size"]
self.read_data(mode="ensemble")
self.ensemble_model = Hang.ensemble([self.HSI_model, self.RGB_model], freeze=freeze, classes=self.classes)
if train:
self.ensemble_model.compile(
loss="categorical_crossentropy",
optimizer=tf.keras.optimizers.Adam(
lr=float(self.config["train"]["learning_rate"])),
metrics=[tf.keras.metrics.CategoricalAccuracy(
name='acc')])
if self.val_split is None:
print("Cannot run callbacks without validation data, skipping...")
callback_list = None
label_names = None
elif experiment is None:
print("Cannot run callbacks without comet experiment, skipping...")
callback_list = None
label_names = None
else:
if self.classes_file is not None:
labeldf = pd.read_csv(self.classes_file)
label_names = list(labeldf.taxonID.values)
else:
label_names = None
callback_list = callbacks.create(log_dir=self.log_dir,
experiment=experiment,
validation_data=self.val_split,
train_data=self.train_split,
label_names=label_names,
submodel="ensemble")
#Train ensemble layer
self.ensemble_model.fit(
self.train_split,
epochs=self.config["train"]["epochs"],
validation_data=self.val_split,
callbacks=callback_list,
class_weight=class_weight)