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client.py
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client.py
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import argparse
import os
# Make TensorFlow log less verbose
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
# Do not consume all GPU at once
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "True"
import flwr as fl
import tensorflow as tf
from tensorflow import keras
from keras.preprocessing.image import ImageDataGenerator as data_augment
# Parse arguments
parser = argparse.ArgumentParser(description="DDD client")
parser.add_argument(
"--client",
required=True,
help="Partition of the dataset (from A to ZC). "
"The dataset is divided into 28 partitions.",
)
args = parser.parse_args()
#data augmetation
data_generate_training = data_augment(rescale=1./255,
shear_range = 0.2,
zoom_range = 0.2,
fill_mode = "nearest",
horizontal_flip = True,
width_shift_range = 0.2,
height_shift_range = 0.2,
validation_split = 0.15)
# the path for client A should be something like "/home/user/ddd/A"
datadir= "REPLACE_WITH_THE_PATH_WHERE_YOU_EXTRACTED_THE_DDD_DATASET" + "/" + args.client
#data split and loading
traind = data_generate_training.flow_from_directory(datadir,
target_size = (227, 227),
#seed = 123,
shuffle=True,
batch_size = 32,
subset = "training")
testd = data_generate_training.flow_from_directory(datadir,
target_size = (227, 227),
#seed = 123,
shuffle=True,
batch_size = 32,
subset = "validation")
#Building Model
CNNmodel = keras.Sequential([
keras.layers.Conv2D(32, (3, 3), input_shape=(227, 227, 3), activation='relu'),
keras.layers.BatchNormalization(),
keras.layers.MaxPooling2D((2,2)),
keras.layers.Dropout(0.5),
keras.layers.Conv2D(64, (3, 3), activation='relu'),
keras.layers.BatchNormalization(),
keras.layers.MaxPooling2D((2,2)),
keras.layers.Dropout(0.5),
keras.layers.Conv2D(128, (3, 3), activation='relu'),
keras.layers.BatchNormalization(),
keras.layers.MaxPooling2D((2,2)),
keras.layers.Dropout(0.5),
keras.layers.Flatten(),
keras.layers.Dense(256, activation='relu'),
keras.layers.Dropout(0.5),
keras.layers.Dense(128, activation = 'relu', kernel_regularizer='l1'),
keras.layers.Dense(2, activation = 'sigmoid')
])
# compile model
CNNmodel.compile(optimizer='adam',
loss="binary_crossentropy",
metrics=['accuracy', tf.keras.metrics.Precision(), tf.keras.metrics.Recall()])
# Define Flower client
class CifarClient(fl.client.NumPyClient):
def __init__(self, model, traind, testd):
self.model = model
self.traind = traind
self.testd = testd
def get_parameters(self, config):
return self.model.get_weights()
def fit(self, parameters, config):
self.model.set_weights(parameters)
self.model.fit(x=self.traind, epochs=10)
return self.model.get_weights(), len(self.traind), {}
def evaluate(self, parameters, config):
# Update local model with global parameters
self.model.set_weights(parameters)
loss, accuracy, precision, recall = self.model.evaluate(self.testd)
return loss, len(self.testd), {"accuracy": accuracy, "precision": precision, "recall": recall}
# Create Flower client
client = CifarClient(CNNmodel, traind, testd)
# Start Flower client
fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=client)