Hadiri Simposium Women in ML pada 7 Desember Daftar sekarang

Simulasi TFF dengan akselerator

Tetap teratur dengan koleksi Simpan dan kategorikan konten berdasarkan preferensi Anda.

Tutorial ini akan menjelaskan cara mengatur simulasi TFF dengan akselerator. Kami fokus pada GPU mesin tunggal (multi-) untuk saat ini dan akan memperbarui tutorial ini dengan pengaturan multi-mesin dan TPU.

Lihat di TensorFlow.org Jalankan di Google Colab Lihat sumber di GitHub Unduh buku catatan

Sebelum kita mulai

Pertama, mari kita pastikan notebook terhubung ke backend yang memiliki komponen yang relevan dikompilasi.

!pip install --quiet --upgrade tensorflow_federated_nightly
!pip install --quiet --upgrade nest_asyncio
!pip install -U tensorboard_plugin_profile

import nest_asyncio
nest_asyncio.apply()
%load_ext tensorboard
import collections
import time

import numpy as np
import tensorflow as tf
import tensorflow_federated as tff

Periksa apakah TF dapat mendeteksi GPU fisik dan membuat lingkungan multi-GPU virtual untuk simulasi GPU TFF. Kedua GPU virtual akan memiliki memori terbatas untuk mendemonstrasikan cara mengkonfigurasi runtime TFF.

gpu_devices = tf.config.list_physical_devices('GPU')
if not gpu_devices:
  raise ValueError('Cannot detect physical GPU device in TF')
tf.config.set_logical_device_configuration(
    gpu_devices[0], 
    [tf.config.LogicalDeviceConfiguration(memory_limit=1024),
     tf.config.LogicalDeviceConfiguration(memory_limit=1024)])
tf.config.list_logical_devices()
[LogicalDevice(name='/device:CPU:0', device_type='CPU'),
 LogicalDevice(name='/device:GPU:0', device_type='GPU'),
 LogicalDevice(name='/device:GPU:1', device_type='GPU')]

Jalankan contoh "Hello World" berikut untuk memastikan lingkungan TFF telah diatur dengan benar. Jika tidak bekerja, silakan merujuk ke Instalasi panduan untuk petunjuk.

@tff.federated_computation
def hello_world():
  return 'Hello, World!'

hello_world()
b'Hello, World!'

Pengaturan eksperimental EMNIST

Dalam tutorial ini, kami melatih pengklasifikasi gambar EMNIST dengan algoritma Federated Averaging. Mari kita mulai dengan memuat contoh MNIST dari situs web TFF.

emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data(only_digits=True)

Kita mendefinisikan fungsi preprocessing contoh EMNIST mengikuti simple_fedavg misalnya. Perhatikan bahwa argumen client_epochs_per_round mengontrol jumlah zaman lokal pada klien dalam belajar federasi.

def preprocess_emnist_dataset(client_epochs_per_round, batch_size, test_batch_size):

  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=client_epochs_per_round).batch(batch_size, drop_remainder=False)

  def preprocess_test_dataset(dataset):
    return dataset.map(element_fn).batch(test_batch_size, drop_remainder=False)

  train_set = emnist_train.preprocess(preprocess_train_dataset)
  test_set = preprocess_test_dataset(
      emnist_test.create_tf_dataset_from_all_clients())
  return train_set, test_set

Kami menggunakan model seperti VGG, yaitu, setiap blok memiliki dua konvolusi 3x3 dan jumlah filter menjadi dua kali lipat ketika peta fitur disubsampel.

def _conv_3x3(input_tensor, filters, strides):
  """2D Convolutional layer with kernel size 3x3."""

  x = tf.keras.layers.Conv2D(
      filters=filters,
      strides=strides,
      kernel_size=3,
      padding='same',
      kernel_initializer='he_normal',
      use_bias=False,
  )(input_tensor)
  return x


def _basic_block(input_tensor, filters, strides):
  """A block of two 3x3 conv layers."""

  x = input_tensor
  x = _conv_3x3(x, filters, strides)
  x = tf.keras.layers.Activation('relu')(x)

  x = _conv_3x3(x, filters, 1)
  x = tf.keras.layers.Activation('relu')(x)
  return x


def _vgg_block(input_tensor, size, filters, strides):
  """A stack of basic blocks."""
  x = _basic_block(input_tensor, filters, strides=strides)
  for _ in range(size - 1):
      x = _basic_block(x, filters, strides=1)
  return x


def create_cnn(num_blocks, conv_width_multiplier=1, num_classes=10):
  """Create a VGG-like CNN model. 

  The CNN has (6*num_blocks + 2) layers.
  """
  input_shape = (28, 28, 1)  # channels_last
  img_input = tf.keras.layers.Input(shape=input_shape)
  x = img_input
  x = tf.image.per_image_standardization(x)

  x = _conv_3x3(x, 16 * conv_width_multiplier, 1)
  x = _vgg_block(x, size=num_blocks, filters=16 * conv_width_multiplier, strides=1)
  x = _vgg_block(x, size=num_blocks, filters=32 * conv_width_multiplier, strides=2)
  x = _vgg_block(x, size=num_blocks, filters=64 * conv_width_multiplier, strides=2)

  x = tf.keras.layers.GlobalAveragePooling2D()(x)
  x = tf.keras.layers.Dense(num_classes)(x)

  model = tf.keras.models.Model(
      img_input,
      x,
      name='cnn-{}-{}'.format(6 * num_blocks + 2, conv_width_multiplier))
  return model

Sekarang mari kita definisikan loop pelatihan untuk EMNIST. Perhatikan bahwa use_experimental_simulation_loop=True di tff.learning.build_federated_averaging_process disarankan untuk simulasi TFF performant, dan diperlukan untuk mengambil keuntungan dari multi-GPU pada mesin tunggal. Lihat simple_fedavg contoh bagi bagaimana mendefinisikan algoritma pembelajaran Federasi disesuaikan yang memiliki performa tinggi pada GPU, salah satu kunci fitur adalah dengan menggunakan eksplisit for ... iter(dataset) untuk loop pelatihan.

def keras_evaluate(model, test_data, metric):
  metric.reset_states()
  for batch in test_data:
    preds = model(batch['x'], training=False)
    metric.update_state(y_true=batch['y'], y_pred=preds)
  return metric.result()


def run_federated_training(client_epochs_per_round, 
                           train_batch_size, 
                           test_batch_size, 
                           cnn_num_blocks, 
                           conv_width_multiplier,
                           server_learning_rate, 
                           client_learning_rate, 
                           total_rounds, 
                           clients_per_round, 
                           rounds_per_eval,
                           logdir='logdir'):

  train_data, test_data = preprocess_emnist_dataset(
      client_epochs_per_round, train_batch_size, test_batch_size)
  data_spec = test_data.element_spec

  def _model_fn():
    keras_model = create_cnn(cnn_num_blocks, conv_width_multiplier)
    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    return tff.learning.from_keras_model(
        keras_model, input_spec=data_spec, loss=loss)

  def _server_optimizer_fn():
    return tf.keras.optimizers.SGD(learning_rate=server_learning_rate)

  def _client_optimizer_fn():
    return tf.keras.optimizers.SGD(learning_rate=client_learning_rate)

  iterative_process = tff.learning.build_federated_averaging_process(
      model_fn=_model_fn, 
      server_optimizer_fn=_server_optimizer_fn, 
      client_optimizer_fn=_client_optimizer_fn, 
      use_experimental_simulation_loop=True)

  metric = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
  eval_model = create_cnn(cnn_num_blocks, conv_width_multiplier)
  logging.info(eval_model.summary())

  server_state = iterative_process.initialize()
  start_time = time.time()
  for round_num in range(total_rounds):
    sampled_clients = np.random.choice(
        train_data.client_ids,
        size=clients_per_round,
        replace=False)
    sampled_train_data = [
        train_data.create_tf_dataset_for_client(client)
        for client in sampled_clients
    ]
    if round_num == total_rounds-1:
      with tf.profiler.experimental.Profile(logdir):
        server_state, train_metrics = iterative_process.next(
            server_state, sampled_train_data)
    else:
      server_state, train_metrics = iterative_process.next(
            server_state, sampled_train_data)
    print(f'Round {round_num} training loss: {train_metrics["train"]["loss"]}, '
     f'time: {(time.time()-start_time)/(round_num+1.)} secs')
    if round_num % rounds_per_eval == 0 or round_num == total_rounds-1:
      server_state.model.assign_weights_to(eval_model)
      accuracy = keras_evaluate(eval_model, test_data, metric)
      print(f'Round {round_num} validation accuracy: {accuracy * 100.0}')

Eksekusi GPU tunggal

Runtime default TFF sama dengan TF: ketika GPU disediakan, GPU pertama akan dipilih untuk dieksekusi. Kami menjalankan pelatihan federasi yang ditentukan sebelumnya untuk beberapa putaran dengan model yang relatif kecil. Putaran terakhir dari eksekusi diprofilkan dengan tf.profiler dan divisualisasikan oleh tensorboard . Pembuatan profil memverifikasi bahwa GPU pertama digunakan.

run_federated_training(
    client_epochs_per_round=1, 
    train_batch_size=16, 
    test_batch_size=128, 
    cnn_num_blocks=2, 
    conv_width_multiplier=4,
    server_learning_rate=1.0, 
    client_learning_rate=0.01,
    total_rounds=10,
    clients_per_round=16, 
    rounds_per_eval=2,
    )
Model: "cnn-14-4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 28, 28, 1)]       0         
_________________________________________________________________
tf.image.per_image_standardi (None, 28, 28, 1)         0         
_________________________________________________________________
conv2d (Conv2D)              (None, 28, 28, 64)        576       
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation (Activation)      (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_1 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_2 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_3 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 14, 14, 128)       73728     
_________________________________________________________________
activation_4 (Activation)    (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_5 (Activation)    (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_6 (Activation)    (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_7 (Activation)    (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 7, 7, 256)         294912    
_________________________________________________________________
activation_8 (Activation)    (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_9 (Activation)    (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_10 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_11 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
global_average_pooling2d (Gl (None, 256)               0         
_________________________________________________________________
dense (Dense)                (None, 10)                2570      
=================================================================
Total params: 2,731,082
Trainable params: 2,731,082
Non-trainable params: 0
_________________________________________________________________
Round 0 training loss: 2.4688243865966797, time: 13.382015466690063 secs
Round 0 validation accuracy: 15.240497589111328
Round 1 training loss: 2.3217368125915527, time: 9.311999917030334 secs
Round 2 training loss: 2.3100595474243164, time: 6.972411632537842 secs
Round 2 validation accuracy: 11.226489067077637
Round 3 training loss: 2.303222417831421, time: 6.467299699783325 secs
Round 4 training loss: 2.2976326942443848, time: 5.526083135604859 secs
Round 4 validation accuracy: 11.224040031433105
Round 5 training loss: 2.2919719219207764, time: 5.468692660331726 secs
Round 6 training loss: 2.2911534309387207, time: 4.935825347900391 secs
Round 6 validation accuracy: 11.833855628967285
Round 7 training loss: 2.2871201038360596, time: 4.918408691883087 secs
Round 8 training loss: 2.2818832397460938, time: 4.602836343977186 secs
Round 8 validation accuracy: 11.385677337646484
Round 9 training loss: 2.2790346145629883, time: 4.99558527469635 secs
Round 9 validation accuracy: 11.226489067077637
%tensorboard --logdir=logdir --port=0

Eksekusi CPU

Sebagai perbandingan, mari kita konfigurasikan runtime TFF untuk eksekusi CPU. Eksekusi CPU hanya sedikit lebih lambat untuk model yang relatif kecil ini.

cpu_device = tf.config.list_logical_devices('CPU')[0]
tff.backends.native.set_local_python_execution_context(
    server_tf_device=cpu_device, client_tf_devices=[cpu_device])

run_federated_training(
    client_epochs_per_round=1, 
    train_batch_size=16, 
    test_batch_size=128, 
    cnn_num_blocks=2, 
    conv_width_multiplier=4,
    server_learning_rate=1.0, 
    client_learning_rate=0.01,
    total_rounds=10,
    clients_per_round=16, 
    rounds_per_eval=2,
    )
Model: "cnn-14-4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         [(None, 28, 28, 1)]       0         
_________________________________________________________________
tf.image.per_image_standardi (None, 28, 28, 1)         0         
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 28, 28, 64)        576       
_________________________________________________________________
conv2d_14 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_12 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_15 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_13 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_16 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_14 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_17 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_15 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_18 (Conv2D)           (None, 14, 14, 128)       73728     
_________________________________________________________________
activation_16 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_19 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_17 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_20 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_18 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_21 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_19 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_22 (Conv2D)           (None, 7, 7, 256)         294912    
_________________________________________________________________
activation_20 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_23 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_21 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_24 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_22 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_25 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_23 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
global_average_pooling2d_1 ( (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                2570      
=================================================================
Total params: 2,731,082
Trainable params: 2,731,082
Non-trainable params: 0
_________________________________________________________________
Round 0 training loss: 2.4787657260894775, time: 15.264191627502441 secs
Round 0 validation accuracy: 12.191418647766113
Round 1 training loss: 2.3097336292266846, time: 11.785032272338867 secs
Round 2 training loss: 2.3062121868133545, time: 9.677561124165853 secs
Round 2 validation accuracy: 11.415066719055176
Round 3 training loss: 2.2982261180877686, time: 9.301376760005951 secs
Round 4 training loss: 2.2953946590423584, time: 8.377780866622924 secs
Round 4 validation accuracy: 20.537813186645508
Round 5 training loss: 2.290337324142456, time: 8.385509928067526 secs
Round 6 training loss: 2.2842795848846436, time: 7.809031554630825 secs
Round 6 validation accuracy: 11.934267044067383
Round 7 training loss: 2.2752432823181152, time: 7.8433578312397 secs
Round 8 training loss: 2.2698657512664795, time: 7.478067080179851 secs
Round 8 validation accuracy: 26.16330337524414
Round 9 training loss: 2.2609798908233643, time: 7.632814192771912 secs
Round 9 validation accuracy: 23.079936981201172

Eksekusi multi-GPU

Sangat mudah untuk mengkonfigurasi TFF untuk eksekusi multi-GPU. Perhatikan bahwa pelatihan klien diparalelkan dalam TFF. Dalam pengaturan multi-GPU, klien akan ditetapkan ke multi-GPU secara round robin. Eksekusi dua GPU berikut tidak lebih cepat dari eksekusi GPU tunggal karena pelatihan klien diparalelkan dalam pengaturan GPU tunggal dan multi, dan pengaturan multiGPU memiliki dua GPU virtual yang dibuat dari satu GPU fisik.

gpu_devices = tf.config.list_logical_devices('GPU')
tff.backends.native.set_local_python_execution_context(client_tf_devices=gpu_devices)

run_federated_training(
    client_epochs_per_round=1, 
    train_batch_size=16, 
    test_batch_size=128, 
    cnn_num_blocks=2, 
    conv_width_multiplier=4,
    server_learning_rate=1.0, 
    client_learning_rate=0.01,
    total_rounds=10,
    clients_per_round=16, 
    rounds_per_eval=2,
    logdir='multigpu'
    )
Model: "cnn-14-4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         [(None, 28, 28, 1)]       0         
_________________________________________________________________
tf.image.per_image_standardi (None, 28, 28, 1)         0         
_________________________________________________________________
conv2d_26 (Conv2D)           (None, 28, 28, 64)        576       
_________________________________________________________________
conv2d_27 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_24 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_28 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_25 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_29 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_26 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_30 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_27 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_31 (Conv2D)           (None, 14, 14, 128)       73728     
_________________________________________________________________
activation_28 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_32 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_29 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_33 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_30 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_34 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_31 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_35 (Conv2D)           (None, 7, 7, 256)         294912    
_________________________________________________________________
activation_32 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_36 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_33 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_37 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_34 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_38 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_35 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
global_average_pooling2d_2 ( (None, 256)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 10)                2570      
=================================================================
Total params: 2,731,082
Trainable params: 2,731,082
Non-trainable params: 0
_________________________________________________________________
Round 0 training loss: 2.911365270614624, time: 12.759389877319336 secs
Round 0 validation accuracy: 9.541536331176758
Round 1 training loss: 2.3175694942474365, time: 9.202919721603394 secs
Round 2 training loss: 2.311001777648926, time: 6.802880525588989 secs
Round 2 validation accuracy: 9.911344528198242
Round 3 training loss: 2.3105244636535645, time: 6.611470937728882 secs
Round 4 training loss: 2.3082072734832764, time: 5.678833389282227 secs
Round 4 validation accuracy: 10.212578773498535
Round 5 training loss: 2.304673671722412, time: 5.5404335260391235 secs
Round 6 training loss: 2.3035168647766113, time: 5.008027451378958 secs
Round 6 validation accuracy: 9.935834884643555
Round 7 training loss: 2.3052737712860107, time: 5.1173741817474365 secs
Round 8 training loss: 2.3007171154022217, time: 4.745321141348945 secs
Round 8 validation accuracy: 10.768514633178711
Round 9 training loss: 2.302018404006958, time: 5.0809732437133786 secs
Round 9 validation accuracy: 12.311422348022461

Model yang lebih besar dan OOM

Mari kita jalankan model yang lebih besar pada CPU dengan putaran yang lebih sedikit.

cpu_device = tf.config.list_logical_devices('CPU')[0]
tff.backends.native.set_local_python_execution_context(
    server_tf_device=cpu_device, client_tf_devices=[cpu_device])

run_federated_training(
    client_epochs_per_round=1, 
    train_batch_size=16, 
    test_batch_size=128, 
    cnn_num_blocks=4, 
    conv_width_multiplier=4,
    server_learning_rate=1.0, 
    client_learning_rate=0.01,
    total_rounds=5,
    clients_per_round=16, 
    rounds_per_eval=2,
    )
Model: "cnn-26-4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_4 (InputLayer)         [(None, 28, 28, 1)]       0         
_________________________________________________________________
tf.image.per_image_standardi (None, 28, 28, 1)         0         
_________________________________________________________________
conv2d_39 (Conv2D)           (None, 28, 28, 64)        576       
_________________________________________________________________
conv2d_40 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_36 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_41 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_37 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_42 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_38 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_43 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_39 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_44 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_40 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_45 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_41 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_46 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_42 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_47 (Conv2D)           (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_43 (Activation)   (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_48 (Conv2D)           (None, 14, 14, 128)       73728     
_________________________________________________________________
activation_44 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_49 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_45 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_50 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_46 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_51 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_47 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_52 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_48 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_53 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_49 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_54 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_50 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_55 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_51 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_56 (Conv2D)           (None, 7, 7, 256)         294912    
_________________________________________________________________
activation_52 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_57 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_53 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_58 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_54 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_59 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_55 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_60 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_56 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_61 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_57 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_62 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_58 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_63 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_59 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
global_average_pooling2d_3 ( (None, 256)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 10)                2570      
=================================================================
Total params: 5,827,658
Trainable params: 5,827,658
Non-trainable params: 0
_________________________________________________________________
Round 0 training loss: 2.437223434448242, time: 24.121686458587646 secs
Round 0 validation accuracy: 9.024785041809082
Round 1 training loss: 2.3081459999084473, time: 19.48685622215271 secs
Round 2 training loss: 2.305305242538452, time: 15.73950457572937 secs
Round 2 validation accuracy: 9.791339874267578
Round 3 training loss: 2.303149700164795, time: 15.194068729877472 secs
Round 4 training loss: 2.3026506900787354, time: 14.036769819259643 secs
Round 4 validation accuracy: 12.193867683410645

Model ini mungkin mengalami masalah kehabisan memori pada satu GPU. Migrasi dari eksperimen CPU skala besar ke simulasi GPU dapat dibatasi oleh penggunaan memori karena GPU sering kali memiliki memori yang terbatas. Ada beberapa parameter yang dapat disetel dalam runtime TFF untuk mengurangi masalah OOM

# Single GPU execution might hit OOM. 
gpu_devices = tf.config.list_logical_devices('GPU')
tff.backends.native.set_local_python_execution_context(client_tf_devices=[gpu_devices[0]])

try:
  run_federated_training(
      client_epochs_per_round=1, 
      train_batch_size=16, 
      test_batch_size=128, 
      cnn_num_blocks=4, 
      conv_width_multiplier=4,
      server_learning_rate=1.0, 
      client_learning_rate=0.01,
      total_rounds=5,
      clients_per_round=16, 
      rounds_per_eval=2,
      )
except ResourceExhaustedError as e:
  print(e)
# Control concurrency by `clients_per_thread`.
gpu_devices = tf.config.list_logical_devices('GPU')
tff.backends.native.set_local_python_execution_context(
    client_tf_devices=[gpu_devices[0]], clients_per_thread=2)

run_federated_training(
    client_epochs_per_round=1, 
    train_batch_size=16, 
    test_batch_size=128, 
    cnn_num_blocks=4, 
    conv_width_multiplier=4,
    server_learning_rate=1.0, 
    client_learning_rate=0.01,
    total_rounds=5,
    clients_per_round=16, 
    rounds_per_eval=2,
    )
Model: "cnn-26-4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 28, 28, 1)]       0         
_________________________________________________________________
tf.image.per_image_standardi (None, 28, 28, 1)         0         
_________________________________________________________________
conv2d (Conv2D)              (None, 28, 28, 64)        576       
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation (Activation)      (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_1 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_2 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_3 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_4 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_5 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_6 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_7 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 14, 14, 128)       73728     
_________________________________________________________________
activation_8 (Activation)    (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_9 (Activation)    (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_10 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_11 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_12 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_14 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_13 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_15 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_14 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_16 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_15 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_17 (Conv2D)           (None, 7, 7, 256)         294912    
_________________________________________________________________
activation_16 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_18 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_17 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_19 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_18 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_20 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_19 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_21 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_20 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_22 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_21 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_23 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_22 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_24 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_23 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
global_average_pooling2d (Gl (None, 256)               0         
_________________________________________________________________
dense (Dense)                (None, 10)                2570      
=================================================================
Total params: 5,827,658
Trainable params: 5,827,658
Non-trainable params: 0
_________________________________________________________________
Round 0 training loss: 2.4990053176879883, time: 11.922378778457642 secs
Round 0 validation accuracy: 11.224040031433105
Round 1 training loss: 2.307560920715332, time: 9.916815996170044 secs
Round 2 training loss: 2.3032877445220947, time: 7.68927804629008 secs
Round 2 validation accuracy: 11.224040031433105
Round 3 training loss: 2.302366256713867, time: 7.681552231311798 secs
Round 4 training loss: 2.301671028137207, time: 7.613566827774048 secs
Round 4 validation accuracy: 11.224040031433105
# Multi-GPU execution with configuration to mitigate OOM. 
cpu_device = tf.config.list_logical_devices('CPU')[0]
gpu_devices = tf.config.list_logical_devices('GPU')
tff.backends.native.set_local_python_execution_context(
    server_tf_device=cpu_device,
    client_tf_devices=gpu_devices, 
    clients_per_thread=1, 
    max_fanout=32)

run_federated_training(
    client_epochs_per_round=1, 
    train_batch_size=16, 
    test_batch_size=128, 
    cnn_num_blocks=4, 
    conv_width_multiplier=4,
    server_learning_rate=1.0, 
    client_learning_rate=0.01,
    total_rounds=5,
    clients_per_round=16, 
    rounds_per_eval=2,
    )
Model: "cnn-26-4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 28, 28, 1)]       0         
_________________________________________________________________
tf.image.per_image_standardi (None, 28, 28, 1)         0         
_________________________________________________________________
conv2d (Conv2D)              (None, 28, 28, 64)        576       
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation (Activation)      (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_1 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_2 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_3 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_4 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_5 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_6 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_7 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 14, 14, 128)       73728     
_________________________________________________________________
activation_8 (Activation)    (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_9 (Activation)    (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_10 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_11 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_12 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_14 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_13 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_15 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_14 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_16 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_15 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_17 (Conv2D)           (None, 7, 7, 256)         294912    
_________________________________________________________________
activation_16 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_18 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_17 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_19 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_18 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_20 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_19 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_21 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_20 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_22 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_21 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_23 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_22 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_24 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_23 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
global_average_pooling2d (Gl (None, 256)               0         
_________________________________________________________________
dense (Dense)                (None, 10)                2570      
=================================================================
Total params: 5,827,658
Trainable params: 5,827,658
Non-trainable params: 0
_________________________________________________________________
Round 0 training loss: 2.4691953659057617, time: 17.81941556930542 secs
Round 0 validation accuracy: 10.817495346069336
Round 1 training loss: 2.3081436157226562, time: 12.986191034317017 secs
Round 2 training loss: 2.3028159141540527, time: 9.518500963846842 secs
Round 2 validation accuracy: 11.500783920288086
Round 3 training loss: 2.303886651992798, time: 8.989932537078857 secs
Round 4 training loss: 2.3030669689178467, time: 8.733866214752197 secs
Round 4 validation accuracy: 12.992260932922363

Optimalkan kinerja

Teknik di TF yang bisa mencapai kinerja yang lebih baik secara umum dapat digunakan dalam TFF, misalnya, pelatihan presisi campuran dan XLA . Speedup (pada GPU seperti V100) dan penghematan memori presisi campuran sering bisa signifikan, yang dapat diperiksa oleh tf.profiler .

# Mixed precision training. 
cpu_device = tf.config.list_logical_devices('CPU')[0]
gpu_devices = tf.config.list_logical_devices('GPU')
tff.backends.native.set_local_python_execution_context(
    server_tf_device=cpu_device,
    client_tf_devices=gpu_devices, 
    clients_per_thread=1, 
    max_fanout=32)
policy = tf.keras.mixed_precision.experimental.Policy('mixed_float16')
tf.keras.mixed_precision.experimental.set_policy(policy)


run_federated_training(
    client_epochs_per_round=1, 
    train_batch_size=16, 
    test_batch_size=128, 
    cnn_num_blocks=4, 
    conv_width_multiplier=4,
    server_learning_rate=1.0, 
    client_learning_rate=0.01,
    total_rounds=5,
    clients_per_round=16, 
    rounds_per_eval=2,
    logdir='mixed'
    )
Model: "cnn-26-4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 28, 28, 1)]       0         
_________________________________________________________________
tf.image.per_image_standardi (None, 28, 28, 1)         0         
_________________________________________________________________
conv2d (Conv2D)              (None, 28, 28, 64)        576       
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation (Activation)      (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_1 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_2 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_3 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_4 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_5 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_6 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 28, 28, 64)        36864     
_________________________________________________________________
activation_7 (Activation)    (None, 28, 28, 64)        0         
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 14, 14, 128)       73728     
_________________________________________________________________
activation_8 (Activation)    (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_10 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_9 (Activation)    (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_11 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_10 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_11 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_13 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_12 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_14 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_13 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_15 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_14 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_16 (Conv2D)           (None, 14, 14, 128)       147456    
_________________________________________________________________
activation_15 (Activation)   (None, 14, 14, 128)       0         
_________________________________________________________________
conv2d_17 (Conv2D)           (None, 7, 7, 256)         294912    
_________________________________________________________________
activation_16 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_18 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_17 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_19 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_18 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_20 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_19 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_21 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_20 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_22 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_21 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_23 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_22 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
conv2d_24 (Conv2D)           (None, 7, 7, 256)         589824    
_________________________________________________________________
activation_23 (Activation)   (None, 7, 7, 256)         0         
_________________________________________________________________
global_average_pooling2d (Gl (None, 256)               0         
_________________________________________________________________
dense (Dense)                (None, 10)                2570      
=================================================================
Total params: 5,827,658
Trainable params: 5,827,658
Non-trainable params: 0
_________________________________________________________________
Round 0 training loss: 2.4187185764312744, time: 18.763780117034912 secs
Round 0 validation accuracy: 9.977468490600586
Round 1 training loss: 2.305102825164795, time: 13.712820529937744 secs
Round 2 training loss: 2.304737091064453, time: 9.993690172831217 secs
Round 2 validation accuracy: 11.779976844787598
Round 3 training loss: 2.2996833324432373, time: 9.29404467344284 secs
Round 4 training loss: 2.299349308013916, time: 9.195427560806275 secs
Round 4 validation accuracy: 11.224040031433105
%tensorboard --logdir=mixed --port=0