def generate_seed_model(model_dimension='VGG11'):
model = VGG(dimension=model_dimension)
outfile_name = 'seed/' + model_dimension + '_keras.npz'
weights = model.get_weights()
helper = KerasHelper()
if not os.path.exists('seed'):
os.makedirs('seed')
helper.save_model(weights, outfile_name)
def train(model, filename, settings):
print("-- RUNNING TRAINING --", flush=True)
train_x, _, _ = read_data(filename)
model.fit(train_x,
train_x,
batch_size=settings['batch_size'],
epochs=settings['epochs'])
return model
if __name__ == '__main__':
with open('settings.yaml', 'r') as fh:
try:
settings = dict(yaml.safe_load(fh))
except yaml.YAMLError as e:
raise (e)
from fedn.utils.kerashelper import KerasHelper
from models.autocoder import create_seed_model
helper = KerasHelper()
weights = helper.load_model(sys.argv[1])
model = create_seed_model()
model.set_weights(weights)
model = train(model, '../data/train.csv', settings)
helper.save_model(model.get_weights(), sys.argv[2])
from fedn.utils.kerashelper import KerasHelper
from models.AMLmodel import construct_model
if __name__ == '__main__':
# Create a seed model and push to Minio
model = construct_model()
outfile_name = "initial_model.npz"
weights = model.get_weights()
helper = KerasHelper()
helper.save_model(weights, outfile_name)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import logging
import sys
from kerasmodel import create_seed_model
import os
if __name__ == '__main__':
logger = logging.getLogger('__name__')
logger.info("Calling the train function")
from fedn.utils.kerashelper import KerasHelper
helper = KerasHelper()
weights = helper.load_model(sys.argv[1])
model = create_seed_model('.')
model.local_model.set_weights(weights)
model.train('/data/train.txt', '')
helper.save_model(model.local_model.get_weights(), path=sys.argv[2])
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# File : init_model.py
# Author : Sheetal Reddy <*****@*****.**>
# Date : 08.03.2021
# Last Modified Date: 08.03.2021
# Last Modified By : Sheetal Reddy <*****@*****.**>
import sys
import os
from client1_new import TrainingProcess, Model, TrainDataReader
from fedn.utils.kerashelper import KerasHelper
from kerasmodel import create_seed_model
if __name__ == '__main__':
outfile_name = '/seed/' + sys.argv[1]
helper = KerasHelper()
start_process = create_seed_model('/client/', pretrained=True)
helper.save_model(start_process.local_model.get_weights(),
path=outfile_name)
print("seed model saved as: ", outfile_name)