<reponame>SasoPavlic/AutoDaedalus
# AutoDaedalus
# <NAME>
# Git: https://github.com/SasoPavlic/AutoDaedalus
# Forked from: https://github.com/Pattio/DeepSwarm

import os
from deepswarm.backends import Dataset, TFKerasBackend
from deepswarm.deepswarm import DeepSwarm
from deepswarm.dataset import prepare_dataset
from deepswarm import data_config

# Change logging level for Tensorflow
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # or any {'0', '1', '2'}

# Contaminate dataset with anomalies (e.g. dataset with 99% of 1 digits and 1% of 3 digits)
validLabel = data_config["valid_label"]
anomalyLabel = data_config["anomaly_label"]
contamination = data_config["contamination"]
test_size = data_config["test_size"]
random_state = data_config["random_state"]

# Prepare dataset based on configuration file
(x_train, x_test, y_train, y_test) = prepare_dataset(validLabel,
                                    anomalyLabel,
                                    contamination,
                                    test_size,
                                    True,
                                    random_state)

# Create dataset object, which controls all the data
normalized_dataset = Dataset(
    training_examples=x_train,
    training_labels=y_train,
    testing_examples=x_test,
    testing_labels=y_test
)
# Create backend responsible for training & validating
backend = TFKerasBackend(dataset=normalized_dataset)
# Create DeepSwarm object responsible for optimization
deepswarm = DeepSwarm(backend=backend)
# Find the topology for a given dataset
topology = deepswarm.find_topology()
# Evaluate discovered topology
deepswarm.evaluate_topology(topology)
# Train topology for additional 100 epochs
trained_topology = deepswarm.train_topology(topology, 100)
# Evaluate the final topology
deepswarm.evaluate_topology(trained_topology)

# AutoDaedalus
# <NAME>
# Git: https://github.com/SasoPavlic/AutoDaedalus
# Forked from: https://github.com/Pattio/DeepSwarm

import os
from deepswarm.backends import Dataset, TFKerasBackend
from deepswarm.deepswarm import DeepSwarm
from deepswarm.dataset import prepare_dataset
from deepswarm import data_config

# Change logging level for Tensorflow
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # or any {'0', '1', '2'}

# Contaminate dataset with anomalies (e.g. dataset with 99% of 1 digits and 1% of 3 digits)
validLabel = data_config["valid_label"]
anomalyLabel = data_config["anomaly_label"]
contamination = data_config["contamination"]
test_size = data_config["test_size"]
random_state = data_config["random_state"]

# Prepare dataset based on configuration file
(x_train, x_test, y_train, y_test) = prepare_dataset(validLabel,
                                    anomalyLabel,
                                    contamination,
                                    test_size,
                                    True,
                                    random_state)

# Create dataset object, which controls all the data
normalized_dataset = Dataset(
    training_examples=x_train,
    training_labels=y_train,
    testing_examples=x_test,
    testing_labels=y_test
)
# Create backend responsible for training & validating
backend = TFKerasBackend(dataset=normalized_dataset)
# Create DeepSwarm object responsible for optimization
deepswarm = DeepSwarm(backend=backend)
# Find the topology for a given dataset
topology = deepswarm.find_topology()
# Evaluate discovered topology
deepswarm.evaluate_topology(topology)
# Train topology for additional 100 epochs
trained_topology = deepswarm.train_topology(topology, 100)
# Evaluate the final topology
deepswarm.evaluate_topology(trained_topology)
# AutoDaedalus # <NAME> # Git: https://github.com/SasoPavlic/AutoDaedalus # Forked from: https://github.com/Pattio/DeepSwarm # Change logging level for Tensorflow # or any {'0', '1', '2'} # Contaminate dataset with anomalies (e.g. dataset with 99% of 1 digits and 1% of 3 digits) # Prepare dataset based on configuration file # Create dataset object, which controls all the data # Create backend responsible for training & validating # Create DeepSwarm object responsible for optimization # Find the topology for a given dataset # Evaluate discovered topology # Train topology for additional 100 epochs # Evaluate the final topology