experiment.log_parameters(model.config["evaluation"])
experiment.log_parameters(model.config["predict"])
experiment.add_tag("HSI")
##Train
#Train see config.yml for tfrecords path with weighted classes in cross entropy
model.read_data()
class_weight = model.calc_class_weight()
##Train subnetwork
experiment.log_parameter("Train subnetworks", True)
with experiment.context_manager("HSI_spatial_subnetwork"):
print("Train HSI spatial subnetwork")
model.read_data(mode="HSI_submodel")
model.train(submodel="spatial", sensor="hyperspectral",class_weight=[class_weight, class_weight, class_weight], experiment=experiment)
with experiment.context_manager("HSI_spectral_subnetwork"):
print("Train HSI spectral subnetwork")
model.read_data(mode="HSI_submodel")
model.train(submodel="spectral", sensor="hyperspectral", class_weight=[class_weight, class_weight, class_weight], experiment=experiment)
#Train full model
with experiment.context_manager("HSI_model"):
experiment.log_parameter("Class Weighted", True)
model.read_data(mode="HSI_train")
model.train(class_weight=class_weight, sensor="hyperspectral", experiment=experiment)
model.HSI_model.save("{}/HSI_model.h5".format(save_dir))
#Get Alpha score for the weighted spectral/spatial average. Higher alpha favors spatial network.
if model.config["train"]["HSI"]["weighted_sum"]:
model.HSI_model = load_model("{}/HSI_model.h5".format(dirname), custom_objects={"WeightedSum": WeightedSum}, compile=False)
model.metadata_model = load_model("{}/metadata_model.h5".format(dirname), compile=False)
else:
#model.RGB_model = load_model("{}/RGB_model.h5".format(dirname), custom_objects={"WeightedSum": WeightedSum})
model.HSI_model = load_model("{}/HSI_model.h5".format(dirname), custom_objects={"WeightedSum": WeightedSum})
model.metadata_model = load_model("{}/metadata_model.h5".format(dirname), compile=False)
else:
if model.config["train"]["pretrain"]:
#metadata network
with experiment.context_manager("metadata"):
print("Train metadata")
model.read_data(mode="metadata")
print(model.metadata_model.summary())
model.train(submodel="metadata", experiment=experiment)
model.metadata_model.save("{}/metadata_model.h5".format(save_dir))
##Train subnetwork
experiment.log_parameter("Train subnetworks", True)
with experiment.context_manager("HSI_spatial_subnetwork"):
print("Train HSI spatial subnetwork")
model.read_data(mode="HSI_submodel")
model.train(submodel="spatial", sensor="hyperspectral", experiment=experiment)
with experiment.context_manager("HSI_spectral_subnetwork"):
print("Train HSI spectral subnetwork")
model.train(submodel="spectral", sensor="hyperspectral", experiment=experiment)
#Train full model
with experiment.context_manager("HSI_model"):
#Linear metadata model for testing purposes
from comet_ml import Experiment
import tensorflow as tf
from DeepTreeAttention.trees import AttentionModel
from DeepTreeAttention.models import metadata
from DeepTreeAttention.callbacks import callbacks
import pandas as pd
model = AttentionModel(
config="/home/b.weinstein/DeepTreeAttention/conf/tree_config.yml")
model.create()
#Log config
experiment = Experiment(project_name="neontrees", workspace="bw4sz")
experiment.log_parameters(model.config["train"])
experiment.log_parameters(model.config["evaluation"])
experiment.log_parameters(model.config["predict"])
experiment.add_tag("metadata")
##Train
#Train see config.yml for tfrecords path with weighted classes in cross entropy
with experiment.context_manager("metadata"):
model.read_data(mode="metadata")
class_weight = model.calc_class_weight()
model.train(submodel="metadata",
experiment=experiment,
class_weight=class_weight)
#Log config
experiment.log_parameters(model.config["train"])
experiment.log_parameters(model.config["predict"])
##Train
#Train see config.yml for tfrecords path with weighted classes in cross entropy
model.read_data(validation_split=True)
class_weight = model.calc_class_weight()
## Train subnetwork
experiment.log_parameter("Train subnetworks", True)
with experiment.context_manager("spatial_subnetwork"):
print("Train spatial subnetwork")
model.read_data(mode="submodel",validation_split=True)
model.train(submodel="spatial", class_weight=[class_weight, class_weight, class_weight])
with experiment.context_manager("spectral_subnetwork"):
print("Train spectral subnetwork")
model.read_data(mode="submodel",validation_split=True)
model.train(submodel="spectral", class_weight=[class_weight, class_weight, class_weight])
#Train full model
experiment.log_parameter("Class Weighted", True)
model.read_data(validation_split=True)
model.train(class_weight=class_weight, experiment=experiment)
#Get Alpha score for the weighted spectral/spatial average. Higher alpha favors spatial network.
if model.config["train"]["weighted_sum"]:
estimate_a = model.model.layers[-1].get_weights()
experiment.log_metric(name="spatial-spectral weight", value=estimate_a[0][0])
experiment = Experiment(project_name="neontrees", workspace="bw4sz")
experiment.log_parameters(model.config["train"])
experiment.log_parameters(model.config["evaluation"])
experiment.log_parameters(model.config["predict"])
experiment.add_tag("RGB")
experiment.log_parameter("timestamp", timestamp)
##Train
#Train see config.yml for tfrecords path with weighted classes in cross entropy
##Train subnetwork
experiment.log_parameter("Train subnetworks", True)
with experiment.context_manager("RGB_spatial_subnetwork"):
print("Train RGB spatial subnetwork")
model.read_data(HSI=False, RGB=True, metadata=False, submodel=False)
model.train(submodel="spatial", sensor="RGB", experiment=experiment)
with experiment.context_manager("RGB_spectral_subnetwork"):
print("Train RGB spectral subnetwork")
model.train(submodel="spectral", sensor="RGB", experiment=experiment)
#Train full model
with experiment.context_manager("RGB_model"):
model.read_data(HSI=False, RGB=True, metadata=False)
model.train(sensor="RGB", experiment=experiment)
#Get Alpha score for the weighted spectral/spatial average. Higher alpha favors spatial network.
if model.config["train"]["RGB"]["weighted_sum"]:
estimate_a = model.RGB_model.get_layer("weighted_sum").get_weights()
experiment.log_metric(name="spatial-spectral weight",
value=estimate_a[0][0])