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emnist_fedavg_main_cookup.py
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import collections
import functools
from absl import app
from absl import flags
import numpy as np
import tensorflow as tf
import tensorflow_federated as tff
import pickle
import random
import os
from tensorflow.compat.v1.keras import backend as Kbackbend
import simple_fedavg_tf
import simple_fedavg_tff
from tensorflow_federated.python.simulation import hdf5_client_data
# Training hyperparameters
flags.DEFINE_integer('total_rounds', 151, 'Number of total training rounds.')
flags.DEFINE_integer('rounds_per_eval', 5, 'How often to evaluate')
flags.DEFINE_integer('train_clients_per_round', 32,
'How many clients to sample per round.')
flags.DEFINE_integer('expected_clients_per_round', 3,
'How many clients to communicate per round.')
flags.DEFINE_integer('j_max_iter_greedy_alg', 4,
'Maximum number of iteration of greedy algorithm.')
flags.DEFINE_integer('client_epochs_per_round', 1,
'Number of epochs in the client to take per round.')
flags.DEFINE_integer('batch_size', 20, 'Batch size used on the client.')
flags.DEFINE_integer('test_batch_size', 100, 'Minibatch size of test data.')
flags.DEFINE_bool('importance_sampling', True, 'Importance sampling is used.')
flags.DEFINE_string('name', 'emnist', 'Name of the experiment.')
flags.DEFINE_integer('random_seed', 123, 'Random seed that should be used for client sampling.')
# Optimizer configuration (this defines one or more flags per optimizer).
flags.DEFINE_float('server_learning_rate', 1.0, 'Server learning rate.')
flags.DEFINE_float('client_learning_rate', 0.1, 'Client learning rate.')
flags.DEFINE_string('dataset_filename', 'cookup_train_1', 'Name of the cooked dataset (without suffix).')
FLAGS = flags.FLAGS
def get_emnist_dataset(dataset_filename):
"""Loads and preprocesses the EMNIST dataset.
Returns:
A `(emnist_train, emnist_test)` tuple where `emnist_train` is a
`tff.simulation.ClientData` object representing the training data and
`emnist_test` is a single `tf.data.Dataset` representing the test data of
all clients.
"""
emnist_train = hdf5_client_data.HDF5ClientData(f'./dataset/{dataset_filename}.h5')
emnist_test = hdf5_client_data.HDF5ClientData('./dataset/test.h5')
def element_fn(element):
return collections.OrderedDict(
x=tf.expand_dims(element['pixels'], -1), y=element['label'])
def preprocess_train_dataset(dataset):
# Use buffer_size same as the maximum client dataset size,
# 418 for Federated EMNIST
return dataset.map(element_fn).shuffle(buffer_size=418).repeat(
count=FLAGS.client_epochs_per_round).batch(
FLAGS.batch_size, drop_remainder=False)
def preprocess_test_dataset(dataset):
return dataset.map(element_fn).batch(
FLAGS.test_batch_size, drop_remainder=False)
emnist_train = emnist_train.preprocess(preprocess_train_dataset)
emnist_test = preprocess_test_dataset(
emnist_test.create_tf_dataset_from_all_clients())
return emnist_train, emnist_test
def create_original_fedavg_cnn_model(only_digits=True):
"""The CNN model used in https://arxiv.org/abs/1602.05629.
This function is duplicated from research/optimization/emnist/models.py to
make this example completely stand-alone.
Args:
only_digits: If True, uses a final layer with 10 outputs, for use with the
digits only EMNIST dataset. If False, uses 62 outputs for the larger
dataset.
Returns:
An uncompiled `tf.keras.Model`.
"""
data_format = 'channels_last'
input_shape = [28, 28, 1]
max_pool = functools.partial(
tf.keras.layers.MaxPooling2D,
pool_size=(2, 2),
padding='same',
data_format=data_format)
conv2d = functools.partial(
tf.keras.layers.Conv2D,
kernel_size=5,
padding='same',
data_format=data_format,
activation=tf.nn.relu,
kernel_initializer=tf.keras.initializers.GlorotUniform(seed=FLAGS.random_seed))
model = tf.keras.models.Sequential([
conv2d(filters=32, input_shape=input_shape),
max_pool(),
conv2d(filters=64),
max_pool(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu,
kernel_initializer=tf.keras.initializers.GlorotUniform(seed=FLAGS.random_seed)),
tf.keras.layers.Dense(10 if only_digits else 62,
kernel_initializer=tf.keras.initializers.GlorotUniform(seed=FLAGS.random_seed)),
tf.keras.layers.Activation(tf.nn.softmax),
])
return model
def server_optimizer_fn():
return tf.keras.optimizers.SGD(learning_rate=FLAGS.server_learning_rate)
def client_optimizer_fn():
return tf.keras.optimizers.SGD(learning_rate=FLAGS.client_learning_rate)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
os.environ['PYTHONHASHSEED'] = str(FLAGS.random_seed)
np.random.seed(FLAGS.random_seed)
random.seed(FLAGS.random_seed)
tf.random.set_seed(FLAGS.random_seed)
session_conf = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
sess = tf.compat.v1.Session(graph=tf.compat.v1.get_default_graph(), config=session_conf)
Kbackbend.set_session(sess)
train_data, test_data = get_emnist_dataset(FLAGS.dataset_filename)
def tff_model_fn():
"""Constructs a fully initialized model for use in federated averaging."""
keras_model = create_original_fedavg_cnn_model(only_digits=True)
loss = tf.keras.losses.SparseCategoricalCrossentropy()
return simple_fedavg_tf.KerasModelWrapper(keras_model,
test_data.element_spec, loss)
# iterative_process = simple_fedavg_tff.build_federated_averaging_process(
# tff_model_fn, FLAGS.expected_clients_per_round, FLAGS.train_clients_per_round,
# FLAGS.j_max_iter_greedy_alg, FLAGS.importance_sampling, server_optimizer_fn, client_optimizer_fn)
# server_state = iterative_process.initialize()
federated_averaging = simple_fedavg_tff.FedAvg(
tff_model_fn, FLAGS.expected_clients_per_round, FLAGS.train_clients_per_round,
FLAGS.j_max_iter_greedy_alg, FLAGS.importance_sampling, server_optimizer_fn, client_optimizer_fn)
server_state = federated_averaging.initialize()
metric_acc = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
model = tff_model_fn()
train_loss = []
val_acc = []
probs = []
clients = train_data.client_ids
np.random.seed(FLAGS.random_seed)
sampled_clients_list = [np.random.choice(
len(clients),
size=FLAGS.train_clients_per_round,
replace=False) for _ in range(FLAGS.total_rounds)]
for round_num in range(FLAGS.total_rounds):
sampled_clients = [clients[i] for i in sampled_clients_list[round_num]]
sampled_train_data = [
train_data.create_tf_dataset_for_client(client)
for client in sampled_clients
]
# server_state, train_metrics = iterative_process.next(
# server_state, sampled_train_data)
server_state, train_metrics, prob = federated_averaging.next(
server_state, sampled_train_data)
print(f'Round {round_num} training loss: {train_metrics}')
train_loss.append(train_metrics)
probs.append(prob)
if round_num % FLAGS.rounds_per_eval == 0:
model.from_weights(server_state.model_weights)
accuracy = simple_fedavg_tf.keras_evaluate(model.keras_model, test_data,
metric_acc)
print(f'Round {round_num} validation accuracy: {accuracy * 100.0}')
val_acc.append(accuracy)
with open(f'./tff_save/{FLAGS.name}_valacc.pk', 'wb') as f:
pickle.dump(val_acc, f)
with open(f'./tff_save/{FLAGS.name}_trainloss.pk', 'wb') as f:
pickle.dump(train_loss, f)
with open(f'./tff_save/{FLAGS.name}_probs.pk', 'wb') as f:
pickle.dump(probs, f)
if __name__ == '__main__':
app.run(main)