Migrer TensorBoard : la boîte à outils de visualisation de TensorFlow

Voir sur TensorFlow.org Exécuter dans Google Colab Voir la source sur GitHub Télécharger le cahier

TensorBoard est un outil intégré permettant de fournir des mesures et des visualisations dans TensorFlow. Les métriques d'expérience d'apprentissage automatique courantes, telles que la précision et la perte, peuvent être suivies et affichées dans TensorBoard. TensorBoard est compatible avec le code TensorFlow 1 et 2.

Dans TensorFlow 1, tf.estimator.Estimator enregistre par défaut les résumés pour TensorBoard. En comparaison, dans TensorFlow 2, les résumés peuvent être enregistrés à l'aide d'un rappel tf.keras.callbacks.TensorBoard .

Ce guide explique comment utiliser TensorBoard, d'abord dans TensorFlow 1 avec des estimateurs, puis comment effectuer le processus équivalent dans TensorFlow 2.

Installer

import tensorflow.compat.v1 as tf1
import tensorflow as tf
import tempfile
import numpy as np
import datetime
%load_ext tensorboard

mnist = tf.keras.datasets.mnist # The MNIST dataset.

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
11501568/11490434 [==============================] - 0s 0us/step

TensorFlow 1 : TensorBoard avec tf.estimator

Dans cet exemple TensorFlow 1, vous instanciez un tf.estimator.DNNClassifier , l'entraînez et l'évaluez sur l'ensemble de données MNIST, puis utilisez TensorBoard pour afficher les métriques :

%reload_ext tensorboard

feature_columns = [tf1.feature_column.numeric_column("x", shape=[28, 28])]

config = tf1.estimator.RunConfig(save_summary_steps=1,
                                 save_checkpoints_steps=1)

path = tempfile.mkdtemp()

classifier = tf1.estimator.DNNClassifier(
    feature_columns=feature_columns,
    hidden_units=[256, 32],
    optimizer=tf1.train.AdamOptimizer(0.001),
    n_classes=10,
    dropout=0.1,
    model_dir=path,
    config = config
)

train_input_fn = tf1.estimator.inputs.numpy_input_fn(
    x={"x": x_train},
    y=y_train.astype(np.int32),
    num_epochs=10,
    batch_size=50,
    shuffle=True,
)

test_input_fn = tf1.estimator.inputs.numpy_input_fn(
    x={"x": x_test},
    y=y_test.astype(np.int32),
    num_epochs=10,
    shuffle=False
)

train_spec = tf1.estimator.TrainSpec(input_fn=train_input_fn, max_steps=10)
eval_spec = tf1.estimator.EvalSpec(input_fn=test_input_fn,
                                   steps=10,
                                   throttle_secs=0)

tf1.estimator.train_and_evaluate(estimator=classifier,
                                train_spec=train_spec,
                                eval_spec=eval_spec)

INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp9w0s5sgg', '_tf_random_seed': None, '_save_summary_steps': 1, '_save_checkpoints_steps': 1, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
WARNING:tensorflow:From /tmp/ipykernel_13367/2752664473.py:20: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.

WARNING:tensorflow:From /tmp/ipykernel_13367/2752664473.py:20: The name tf.estimator.inputs.numpy_input_fn is deprecated. Please use tf.compat.v1.estimator.inputs.numpy_input_fn instead.

INFO:tensorflow:Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 1 or save_checkpoints_secs None.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:65: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:491: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/monitored_session.py:907: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp9w0s5sgg/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1...
INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmp9w0s5sgg/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-09-22T20:09:45
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp9w0s5sgg/model.ckpt-1
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
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INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.25105s
INFO:tensorflow:Finished evaluation at 2021-09-22-20:09:45
INFO:tensorflow:Saving dict for global step 1: accuracy = 0.16328125, average_loss = 2.2459474, global_step = 1, loss = 287.48126
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1: /tmp/tmp9w0s5sgg/model.ckpt-1
INFO:tensorflow:loss = 115.65201, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2...
INFO:tensorflow:Saving checkpoints for 2 into /tmp/tmp9w0s5sgg/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-09-22T20:09:46
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp9w0s5sgg/model.ckpt-2
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
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INFO:tensorflow:Evaluation [5/10]
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INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.24542s
INFO:tensorflow:Finished evaluation at 2021-09-22-20:09:46
INFO:tensorflow:Saving dict for global step 2: accuracy = 0.25625, average_loss = 2.1821105, global_step = 2, loss = 279.31015
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2: /tmp/tmp9w0s5sgg/model.ckpt-2
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3...
INFO:tensorflow:Saving checkpoints for 3 into /tmp/tmp9w0s5sgg/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-09-22T20:09:46
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp9w0s5sgg/model.ckpt-3
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
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INFO:tensorflow:Evaluation [5/10]
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INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.24425s
INFO:tensorflow:Finished evaluation at 2021-09-22-20:09:46
INFO:tensorflow:Saving dict for global step 3: accuracy = 0.3, average_loss = 2.1269565, global_step = 3, loss = 272.25043
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3: /tmp/tmp9w0s5sgg/model.ckpt-3
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4...
INFO:tensorflow:Saving checkpoints for 4 into /tmp/tmp9w0s5sgg/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-09-22T20:09:47
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp9w0s5sgg/model.ckpt-4
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
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INFO:tensorflow:Evaluation [5/10]
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INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.25865s
INFO:tensorflow:Finished evaluation at 2021-09-22-20:09:47
INFO:tensorflow:Saving dict for global step 4: accuracy = 0.3484375, average_loss = 2.0719538, global_step = 4, loss = 265.21008
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4: /tmp/tmp9w0s5sgg/model.ckpt-4
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5...
INFO:tensorflow:Saving checkpoints for 5 into /tmp/tmp9w0s5sgg/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/saver.py:971: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to delete files with this prefix.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-09-22T20:09:47
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp9w0s5sgg/model.ckpt-5
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
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INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.23830s
INFO:tensorflow:Finished evaluation at 2021-09-22-20:09:47
INFO:tensorflow:Saving dict for global step 5: accuracy = 0.40078124, average_loss = 2.013233, global_step = 5, loss = 257.69382
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5: /tmp/tmp9w0s5sgg/model.ckpt-5
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6...
INFO:tensorflow:Saving checkpoints for 6 into /tmp/tmp9w0s5sgg/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 6...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-09-22T20:09:48
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp9w0s5sgg/model.ckpt-6
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
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INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.24491s
INFO:tensorflow:Finished evaluation at 2021-09-22-20:09:48
INFO:tensorflow:Saving dict for global step 6: accuracy = 0.45546874, average_loss = 1.9495137, global_step = 6, loss = 249.53775
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6: /tmp/tmp9w0s5sgg/model.ckpt-6
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7...
INFO:tensorflow:Saving checkpoints for 7 into /tmp/tmp9w0s5sgg/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 7...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-09-22T20:09:48
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp9w0s5sgg/model.ckpt-7
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.24294s
INFO:tensorflow:Finished evaluation at 2021-09-22-20:09:49
INFO:tensorflow:Saving dict for global step 7: accuracy = 0.4984375, average_loss = 1.8863195, global_step = 7, loss = 241.4489
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 7: /tmp/tmp9w0s5sgg/model.ckpt-7
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8...
INFO:tensorflow:Saving checkpoints for 8 into /tmp/tmp9w0s5sgg/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 8...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-09-22T20:09:49
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp9w0s5sgg/model.ckpt-8
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.24205s
INFO:tensorflow:Finished evaluation at 2021-09-22-20:09:49
INFO:tensorflow:Saving dict for global step 8: accuracy = 0.50390625, average_loss = 1.8260235, global_step = 8, loss = 233.731
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 8: /tmp/tmp9w0s5sgg/model.ckpt-8
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9...
INFO:tensorflow:Saving checkpoints for 9 into /tmp/tmp9w0s5sgg/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 9...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-09-22T20:09:49
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp9w0s5sgg/model.ckpt-9
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.24289s
INFO:tensorflow:Finished evaluation at 2021-09-22-20:09:50
INFO:tensorflow:Saving dict for global step 9: accuracy = 0.5046875, average_loss = 1.770147, global_step = 9, loss = 226.57881
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 9: /tmp/tmp9w0s5sgg/model.ckpt-9
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10...
INFO:tensorflow:Saving checkpoints for 10 into /tmp/tmp9w0s5sgg/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10...
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-09-22T20:09:50
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp9w0s5sgg/model.ckpt-10
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.24503s
INFO:tensorflow:Finished evaluation at 2021-09-22-20:09:50
INFO:tensorflow:Saving dict for global step 10: accuracy = 0.50859374, average_loss = 1.7165858, global_step = 10, loss = 219.72298
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10: /tmp/tmp9w0s5sgg/model.ckpt-10
INFO:tensorflow:Loss for final step: 82.50392.
({'accuracy': 0.50859374,
  'average_loss': 1.7165858,
  'loss': 219.72298,
  'global_step': 10},
 [])

%tensorboard --logdir {classifier.model_dir}

TensorFlow 2 : TensorBoard avec un rappel Keras et Model.fit

Dans cet exemple TensorFlow 2, vous créez et stockez des journaux avec le rappel tf.keras.callbacks.TensorBoard , et vous entraînez le modèle. Le rappel suit la précision et la perte par époque. Il est passé à Model.fit dans la liste des callbacks .

%reload_ext tensorboard

def create_model():
  return tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
  ])

model = create_model()
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'],
              steps_per_execution=10)

log_dir = tempfile.mkdtemp()
tensorboard_callback = tf.keras.callbacks.TensorBoard(
  log_dir=log_dir,
  histogram_freq=1) # Enable histogram computation with each epoch.

model.fit(x=x_train,
          y=y_train,
          epochs=10,
          validation_data=(x_test, y_test),
          callbacks=[tensorboard_callback])

Epoch 1/10
1875/1875 [==============================] - 4s 2ms/step - loss: 0.2205 - accuracy: 0.9351 - val_loss: 0.1078 - val_accuracy: 0.9675
Epoch 2/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0958 - accuracy: 0.9711 - val_loss: 0.0852 - val_accuracy: 0.9716
Epoch 3/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0685 - accuracy: 0.9786 - val_loss: 0.0728 - val_accuracy: 0.9772
Epoch 4/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0525 - accuracy: 0.9834 - val_loss: 0.0694 - val_accuracy: 0.9786
Epoch 5/10
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0429 - accuracy: 0.9860 - val_loss: 0.0705 - val_accuracy: 0.9784
Epoch 6/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0369 - accuracy: 0.9876 - val_loss: 0.0616 - val_accuracy: 0.9822
Epoch 7/10
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0316 - accuracy: 0.9897 - val_loss: 0.0777 - val_accuracy: 0.9787
Epoch 8/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0267 - accuracy: 0.9910 - val_loss: 0.0709 - val_accuracy: 0.9806
Epoch 9/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0239 - accuracy: 0.9918 - val_loss: 0.0651 - val_accuracy: 0.9835
Epoch 10/10
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0225 - accuracy: 0.9920 - val_loss: 0.0713 - val_accuracy: 0.9819
<keras.callbacks.History at 0x7f3b2c590310>

%tensorboard --logdir {tensorboard_callback.log_dir}

Prochaines étapes