def test_model(data, classes):
model = metadata.create(classes=classes,
sites=23,
domains=15,
learning_rate=0.001)
prediction = model.predict(data)
assert prediction.shape == (1, classes)
def create(self, weights=None, submodel=None):
"""Load a model
Args:
weights: a saved model weights from previous run
name: a model name from DeepTreeAttention.models
"""
self.classes = pd.read_csv(self.classes_file).shape[0]
if self.config["train"]["gpus"] > 1:
self.strategy = tf.distribute.MirroredStrategy()
print("Running in parallel on {} GPUs".format(
self.strategy.num_replicas_in_sync))
self.config["train"]["batch_size"] = self.config["train"][
"batch_size"] * self.strategy.num_replicas_in_sync
with self.strategy.scope():
self.HSI_model, self.HSI_spatial, self.HSI_spectral = Hang.create_models(
self.HSI_size, self.HSI_size, self.HSI_channels,
self.classes, self.config["train"]["learning_rate"])
self.RGB_model, self.RGB_spatial, self.RGB_spectral = Hang.create_models(
self.RGB_size, self.RGB_size, self.RGB_channels,
self.classes, self.config["train"]["learning_rate"])
#create a metadata model
self.metadata_model = metadata.create(
classes=self.classes,
sites=self.sites,
domains=self.domains,
learning_rate=self.config["train"]["learning_rate"])
else:
self.HSI_model, self.HSI_spatial, self.HSI_spectral = Hang.create_models(
self.HSI_size, self.HSI_size, self.HSI_channels, self.classes,
self.config["train"]["learning_rate"])
self.RGB_model, self.RGB_spatial, self.RGB_spectral = Hang.create_models(
self.RGB_size, self.RGB_size, self.RGB_channels, self.classes,
self.config["train"]["learning_rate"])
#create a metadata model
self.metadata_model = metadata.create(
classes=self.classes,
sites=self.sites,
domains=self.domains,
learning_rate=self.config["train"]["learning_rate"])
def test_ensemble(HSI_image, metadata_data):
batch, height, width, channels = HSI_image.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)
metadata_model = metadata.create(classes=2, sites=10, domains =10, learning_rate=0.001)
ensemble = Hang.learned_ensemble(HSI_model=model1, metadata_model=metadata_model, classes=2)
prediction = ensemble.predict([HSI_image] + metadata_data)
assert prediction.shape == (1, 2)
def ensemble_model():
sensor_inputs, sensor_outputs, spatial, spectral = Hang2020_geographic.define_model(
classes=2, height=20, width=20, channels=369)
model1 = tf.keras.Model(inputs=sensor_inputs, outputs=sensor_outputs)
metadata_model = metadata.create(classes=2,
sites=10,
domains=10,
learning_rate=0.001)
ensemble_model = Hang2020_geographic.learned_ensemble(
HSI_model=model1, metadata_model=metadata_model, classes=2)
ensemble_model = tf.keras.Model(
ensemble_model.inputs,
ensemble_model.get_layer("submodel_concat").output)
return ensemble_model
def test_define(HSI_image, metadata_data):
batch, height, width, channels = HSI_image.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)
metadata_model = metadata.create(classes=2,
sites=10,
domains=10,
learning_rate=0.001)
ensemble = Hang.learned_ensemble(HSI_model=model1,
metadata_model=metadata_model,
classes=2)
extractor = tf.keras.Model(ensemble.inputs, ensemble.output)
neighbor_array = []
neighbor_distance = []
for x in np.arange(5):
prediction = extractor.predict([HSI_image] + metadata_data)
neighbor_array.append(prediction)
neighbor_distance.append(np.random.rand())
#stack and batch
neighbor_array = np.vstack(neighbor_array)
neighbor_array = np.expand_dims(neighbor_array, axis=0)
neighbor_distance = np.expand_dims(neighbor_distance, axis=0)
neighbor_model = neighbors_model.create(ensemble_model=ensemble,
freeze=False,
k_neighbors=5,
classes=2)
prediction = neighbor_model.predict([HSI_image] + metadata_data +
[neighbor_array] + [neighbor_distance])
assert prediction.shape == (1, 2)
def create(self, weights=None, submodel=None):
"""Load a model
Args:
weights: a saved model weights from previous run
name: a model name from DeepTreeAttention.models
"""
self.classes = pd.read_csv(self.classes_file).shape[0]
if self.config["train"]["gpus"] > 1:
self.strategy = tf.distribute.MirroredStrategy()
print("Running in parallel on {} GPUs".format(
self.strategy.num_replicas_in_sync))
self.config["train"]["batch_size"] = self.config["train"][
"batch_size"] * self.strategy.num_replicas_in_sync
with self.strategy.scope():
self.HSI_model, self.HSI_spatial, self.HSI_spectral = Hang.create_models(
self.HSI_size, self.HSI_size, self.HSI_channels,
self.classes, self.config["train"]["learning_rate"])
self.RGB_model, self.RGB_spatial, self.RGB_spectral = Hang.create_models(
self.RGB_size, self.RGB_size, self.RGB_channels,
self.classes, self.config["train"]["learning_rate"])
#create a metadata model
self.metadata_model = metadata.create(
classes=self.classes,
sites=self.sites,
domains=self.domains,
learning_rate=self.config["train"]["learning_rate"])
self.ensemble_model = Hang.learned_ensemble(
HSI_model=self.HSI_model,
metadata_model=self.metadata_model,
freeze=self.config["ensemble"]["freeze"],
classes=self.classes)
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')])
else:
self.HSI_model, self.HSI_spatial, self.HSI_spectral = Hang.create_models(
self.HSI_size, self.HSI_size, self.HSI_channels, self.classes,
self.config["train"]["learning_rate"])
self.RGB_model, self.RGB_spatial, self.RGB_spectral = Hang.create_models(
self.RGB_size, self.RGB_size, self.RGB_channels, self.classes,
self.config["train"]["learning_rate"])
#create a metadata model
self.metadata_model = metadata.create(
classes=self.classes,
sites=self.sites,
domains=self.domains,
learning_rate=self.config["train"]["learning_rate"])
#create an ensemble model
self.ensemble_model = Hang.learned_ensemble(
HSI_model=self.HSI_model,
metadata_model=self.metadata_model,
freeze=self.config["train"]["ensemble"]["freeze"],
classes=self.classes)
#Compile ensemble
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')])