Geçiş kontrol noktası kaydetme

TensorFlow.org'da görüntüleyin Google Colab'da çalıştırın Kaynağı GitHub'da görüntüleyin Not defterini indir

"En iyi" modeli veya model ağırlıklarını/parametrelerini sürekli olarak kaydetmenin birçok faydası vardır. Bunlar, eğitim ilerlemesini izleyebilmeyi ve kaydedilmiş modelleri farklı kaydedilmiş durumlardan yükleyebilmeyi içerir.

TensorFlow 1'de, tf.estimator.Estimator API'leri ile eğitim/doğrulama sırasında kontrol noktası kaydetmeyi yapılandırmak için tf.estimator.RunConfig içinde bir zamanlama belirtir veya tf.estimator.RunConfig tf.estimator.CheckpointSaverHook kullanırsınız. Bu kılavuz, bu iş akışından TensorFlow 2 Keras API'lerine nasıl geçiş yapılacağını gösterir.

TensorFlow 2'de tf.keras.callbacks.ModelCheckpoint çeşitli şekillerde yapılandırabilirsiniz:

  • "en iyi" sürümü, save_best_only=True parametresini kullanarak izlenen bir metriğe göre kaydedin; burada monitor , örneğin, 'loss' , 'val_loss' , 'accuracy', or 'val_accuracy'`.
  • Sürekli olarak belirli bir frekansta kaydedin ( save_freq argümanını kullanarak).
  • save_weights_only ayarını True olarak ayarlayarak ağırlıkları/parametreleri yalnızca tüm model yerine kaydedin.

Daha fazla ayrıntı için, Modelleri kaydet ve yükle öğreticisindeki tf.keras.callbacks.ModelCheckpoint API belgelerine ve Eğitim sırasında denetim noktalarını kaydet bölümüne bakın. Keras modellerini kaydet ve yükle kılavuzundaki TF Kontrol Noktası biçimi bölümünde Kontrol Noktası biçimi hakkında daha fazla bilgi edinin. Ek olarak, hata toleransı eklemek için manuel kontrol noktası için tf.keras.callbacks.BackupAndRestore veya tf.train.Checkpoint kullanabilirsiniz. Hata toleransı geçiş kılavuzunda daha fazla bilgi edinin.

Keras geri aramaları , yerleşik Model.fit / Model.evaluate / Model.predict API'lerinde eğitim/değerlendirme/tahmin sırasında farklı noktalarda çağrılan nesnelerdir. Kılavuzun sonundaki Sonraki adımlar bölümünde daha fazla bilgi edinin.

Kurmak

Gösteri amacıyla içe aktarma ve basit bir veri kümesiyle başlayın:

import tensorflow.compat.v1 as tf1
import tensorflow as tf
import numpy as np
import tempfile

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
-yer tutucu2 l10n-yer
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: tf.estimator API'leri ile kontrol noktalarını kaydedin

Bu TensorFlow 1 örneği, tf.estimator.Estimator API'leri ile eğitim/değerlendirme sırasında her adımda kontrol noktalarını kaydetmek için tf.estimator.RunConfig nasıl yapılandırılacağını gösterir:

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=0.2,
    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/tmplrkjo9in', '_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_20296/3980459272.py:18: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.

WARNING:tensorflow:From /tmp/ipykernel_20296/3980459272.py:18: 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:397: 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:914: 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/tmplrkjo9in/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/tmplrkjo9in/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 2022-01-14T02:28:47
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmplrkjo9in/model.ckpt-1
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
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INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.26374s
INFO:tensorflow:Finished evaluation at 2022-01-14-02:28:47
INFO:tensorflow:Saving dict for global step 1: accuracy = 0.1765625, average_loss = 2.2546134, global_step = 1, loss = 288.5905
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1: /tmp/tmplrkjo9in/model.ckpt-1
INFO:tensorflow:loss = 118.3231, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2...
INFO:tensorflow:Saving checkpoints for 2 into /tmp/tmplrkjo9in/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 2022-01-14T02:28:48
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmplrkjo9in/model.ckpt-2
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
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INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.36662s
INFO:tensorflow:Finished evaluation at 2022-01-14-02:28:48
INFO:tensorflow:Saving dict for global step 2: accuracy = 0.2859375, average_loss = 2.1868849, global_step = 2, loss = 279.92126
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2: /tmp/tmplrkjo9in/model.ckpt-2
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3...
INFO:tensorflow:Saving checkpoints for 3 into /tmp/tmplrkjo9in/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 2022-01-14T02:28:48
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmplrkjo9in/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]
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INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.22792s
INFO:tensorflow:Finished evaluation at 2022-01-14-02:28:48
INFO:tensorflow:Saving dict for global step 3: accuracy = 0.35078126, average_loss = 2.1220195, global_step = 3, loss = 271.6185
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3: /tmp/tmplrkjo9in/model.ckpt-3
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4...
INFO:tensorflow:Saving checkpoints for 4 into /tmp/tmplrkjo9in/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 2022-01-14T02:28:49
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmplrkjo9in/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 [10/10]
INFO:tensorflow:Inference Time : 0.22387s
INFO:tensorflow:Finished evaluation at 2022-01-14-02:28:49
INFO:tensorflow:Saving dict for global step 4: accuracy = 0.40234375, average_loss = 2.0655982, global_step = 4, loss = 264.39658
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4: /tmp/tmplrkjo9in/model.ckpt-4
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5...
INFO:tensorflow:Saving checkpoints for 5 into /tmp/tmplrkjo9in/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1054: 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 2022-01-14T02:28:49
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmplrkjo9in/model.ckpt-5
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
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INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.22548s
INFO:tensorflow:Finished evaluation at 2022-01-14-02:28:49
INFO:tensorflow:Saving dict for global step 5: accuracy = 0.42421874, average_loss = 2.0072064, global_step = 5, loss = 256.92242
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5: /tmp/tmplrkjo9in/model.ckpt-5
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6...
INFO:tensorflow:Saving checkpoints for 6 into /tmp/tmplrkjo9in/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 2022-01-14T02:28:50
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmplrkjo9in/model.ckpt-6
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
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INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.22806s
INFO:tensorflow:Finished evaluation at 2022-01-14-02:28:50
INFO:tensorflow:Saving dict for global step 6: accuracy = 0.43984374, average_loss = 1.9473753, global_step = 6, loss = 249.26404
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6: /tmp/tmplrkjo9in/model.ckpt-6
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7...
INFO:tensorflow:Saving checkpoints for 7 into /tmp/tmplrkjo9in/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 2022-01-14T02:28:50
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmplrkjo9in/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]
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INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.23091s
INFO:tensorflow:Finished evaluation at 2022-01-14-02:28:50
INFO:tensorflow:Saving dict for global step 7: accuracy = 0.44296876, average_loss = 1.8903366, global_step = 7, loss = 241.96309
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 7: /tmp/tmplrkjo9in/model.ckpt-7
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8...
INFO:tensorflow:Saving checkpoints for 8 into /tmp/tmplrkjo9in/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 2022-01-14T02:28:51
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmplrkjo9in/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]
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INFO:tensorflow:Evaluation [5/10]
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INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.22453s
INFO:tensorflow:Finished evaluation at 2022-01-14-02:28:51
INFO:tensorflow:Saving dict for global step 8: accuracy = 0.44453126, average_loss = 1.8294731, global_step = 8, loss = 234.17256
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 8: /tmp/tmplrkjo9in/model.ckpt-8
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9...
INFO:tensorflow:Saving checkpoints for 9 into /tmp/tmplrkjo9in/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 2022-01-14T02:28:51
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmplrkjo9in/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]
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INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.22271s
INFO:tensorflow:Finished evaluation at 2022-01-14-02:28:51
INFO:tensorflow:Saving dict for global step 9: accuracy = 0.47734374, average_loss = 1.7674354, global_step = 9, loss = 226.23174
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 9: /tmp/tmplrkjo9in/model.ckpt-9
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10...
INFO:tensorflow:Saving checkpoints for 10 into /tmp/tmplrkjo9in/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 2022-01-14T02:28:52
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmplrkjo9in/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 : 0.38483s
INFO:tensorflow:Finished evaluation at 2022-01-14-02:28:52
INFO:tensorflow:Saving dict for global step 10: accuracy = 0.5140625, average_loss = 1.7108486, global_step = 10, loss = 218.98862
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10: /tmp/tmplrkjo9in/model.ckpt-10
INFO:tensorflow:Loss for final step: 96.2236.
({'accuracy': 0.5140625,
  'average_loss': 1.7108486,
  'loss': 218.98862,
  'global_step': 10},
 [])
yer tutucu5 l10n-yer
%ls {classifier.model_dir}

checkpoint
eval/
events.out.tfevents.1642127326.kokoro-gcp-ubuntu-prod-837339153
graph.pbtxt
model.ckpt-10.data-00000-of-00001
model.ckpt-10.index
model.ckpt-10.meta
model.ckpt-6.data-00000-of-00001
model.ckpt-6.index
model.ckpt-6.meta
model.ckpt-7.data-00000-of-00001
model.ckpt-7.index
model.ckpt-7.meta
model.ckpt-8.data-00000-of-00001
model.ckpt-8.index
model.ckpt-8.meta
model.ckpt-9.data-00000-of-00001
model.ckpt-9.index
model.ckpt-9.meta

TensorFlow 2: Model.fit için bir Keras geri aramasıyla kontrol noktalarını kaydedin

TensorFlow 2'de, eğitim/değerlendirme için yerleşik Model.fit (veya Model.evaluate ) Model.fit , tf.keras.callbacks.ModelCheckpoint yapılandırabilir ve ardından callbacks (veya Model.evaluate ). (Yerleşik yöntemler kılavuzuyla Eğitim ve değerlendirme'deki API belgelerinde ve Geri aramaları kullanma bölümünde daha fazla bilgi edinin.)

Aşağıdaki örnekte, kontrol noktalarını geçici bir dizinde depolamak için bir tf.keras.callbacks.ModelCheckpoint geri çağrısı kullanacaksınız:

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()

model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath=log_dir)

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

Epoch 1/10
1840/1875 [============================>.] - ETA: 0s - loss: 0.2224 - accuracy: 0.9348
2022-01-14 02:28:56.714889: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: /tmp/tmpb85suru4/assets
1875/1875 [==============================] - 4s 2ms/step - loss: 0.2208 - accuracy: 0.9354 - val_loss: 0.1132 - val_accuracy: 0.9669
Epoch 2/10
1870/1875 [============================>.] - ETA: 0s - loss: 0.0961 - accuracy: 0.9706INFO:tensorflow:Assets written to: /tmp/tmpb85suru4/assets
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0962 - accuracy: 0.9706 - val_loss: 0.0784 - val_accuracy: 0.9753
Epoch 3/10
1860/1875 [============================>.] - ETA: 0s - loss: 0.0696 - accuracy: 0.9781INFO:tensorflow:Assets written to: /tmp/tmpb85suru4/assets
1875/1875 [==============================] - 3s 2ms/step - loss: 0.0695 - accuracy: 0.9782 - val_loss: 0.0684 - val_accuracy: 0.9788
Epoch 4/10
1860/1875 [============================>.] - ETA: 0s - loss: 0.0529 - accuracy: 0.9826INFO:tensorflow:Assets written to: /tmp/tmpb85suru4/assets
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0531 - accuracy: 0.9826 - val_loss: 0.0671 - val_accuracy: 0.9791
Epoch 5/10
1860/1875 [============================>.] - ETA: 0s - loss: 0.0423 - accuracy: 0.9860INFO:tensorflow:Assets written to: /tmp/tmpb85suru4/assets
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0424 - accuracy: 0.9860 - val_loss: 0.0772 - val_accuracy: 0.9757
Epoch 6/10
1860/1875 [============================>.] - ETA: 0s - loss: 0.0345 - accuracy: 0.9888INFO:tensorflow:Assets written to: /tmp/tmpb85suru4/assets
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0345 - accuracy: 0.9888 - val_loss: 0.0669 - val_accuracy: 0.9811
Epoch 7/10
1860/1875 [============================>.] - ETA: 0s - loss: 0.0314 - accuracy: 0.9895INFO:tensorflow:Assets written to: /tmp/tmpb85suru4/assets
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0313 - accuracy: 0.9895 - val_loss: 0.0718 - val_accuracy: 0.9800
Epoch 8/10
1870/1875 [============================>.] - ETA: 0s - loss: 0.0298 - accuracy: 0.9899INFO:tensorflow:Assets written to: /tmp/tmpb85suru4/assets
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0298 - accuracy: 0.9899 - val_loss: 0.0632 - val_accuracy: 0.9825
Epoch 9/10
1860/1875 [============================>.] - ETA: 0s - loss: 0.0230 - accuracy: 0.9925INFO:tensorflow:Assets written to: /tmp/tmpb85suru4/assets
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0231 - accuracy: 0.9924 - val_loss: 0.0748 - val_accuracy: 0.9800
Epoch 10/10
1860/1875 [============================>.] - ETA: 0s - loss: 0.0220 - accuracy: 0.9920INFO:tensorflow:Assets written to: /tmp/tmpb85suru4/assets
1875/1875 [==============================] - 3s 1ms/step - loss: 0.0222 - accuracy: 0.9920 - val_loss: 0.0703 - val_accuracy: 0.9825
<keras.callbacks.History at 0x7f638c204410>
yer tutucu9 l10n-yer
%ls {model_checkpoint_callback.filepath}

assets/  keras_metadata.pb  saved_model.pb  variables/

Sonraki adımlar

Şurada kontrol noktası oluşturma hakkında daha fazla bilgi edinin:

Geri aramalar hakkında daha fazla bilgi edinin:

Ayrıca, taşımayla ilgili aşağıdaki kaynakları da faydalı bulabilirsiniz: