Migrasi penyimpanan pos pemeriksaan

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

Menyimpan model atau bobot/parameter model "terbaik" secara terus-menerus memiliki banyak manfaat. Ini termasuk kemampuan untuk melacak kemajuan pelatihan dan memuat model yang disimpan dari berbagai status tersimpan.

Di TensorFlow 1, untuk mengonfigurasi penyimpanan pos pemeriksaan selama pelatihan/validasi dengan tf.estimator.Estimator API, Anda menentukan jadwal di tf.estimator.RunConfig atau menggunakan tf.estimator.CheckpointSaverHook . Panduan ini menunjukkan cara bermigrasi dari alur kerja ini ke TensorFlow 2 Keras API.

Di TensorFlow 2, Anda dapat mengonfigurasi tf.keras.callbacks.ModelCheckpoint dengan beberapa cara:

  • Simpan versi "terbaik" menurut metrik yang dipantau menggunakan parameter save_best_only=True , di mana monitor dapat berupa, misalnya, 'loss' , 'val_loss' , 'accuracy', or 'val_accuracy'`.
  • Simpan terus menerus pada frekuensi tertentu (menggunakan argumen save_freq ).
  • Simpan bobot/parameter saja alih-alih seluruh model dengan menyetel save_weights_only ke True .

Untuk detail selengkapnya, lihat dokumen tf.keras.callbacks.ModelCheckpoint API dan bagian Simpan pos pemeriksaan selama pelatihan di tutorial Simpan dan muat model . Pelajari lebih lanjut tentang format Checkpoint di bagian format TF Checkpoint di panduan Simpan dan muat model Keras . Selain itu, untuk menambah toleransi kesalahan, Anda dapat menggunakan tf.keras.callbacks.BackupAndRestore atau tf.train.Checkpoint untuk pemeriksaan manual. Pelajari lebih lanjut di Panduan migrasi toleransi kesalahan .

Callback Keras adalah objek yang dipanggil pada titik yang berbeda selama pelatihan/evaluasi/prediksi dalam Keras Model.fit / Model.evaluate / Model.predict API bawaan. Pelajari lebih lanjut di bagian Langkah berikutnya di akhir panduan.

Mempersiapkan

Mulailah dengan impor dan kumpulan data sederhana untuk tujuan demonstrasi:

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

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: Simpan pos pemeriksaan dengan tf.estimator API

Contoh TensorFlow 1 ini menunjukkan cara mengonfigurasi tf.estimator.RunConfig untuk menyimpan pos pemeriksaan di setiap langkah selama pelatihan/evaluasi dengan API tf.estimator.Estimator :

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: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 [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]
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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]
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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]
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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]
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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]
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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]
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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 : 0.38483s
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},
 [])

%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: Simpan pos pemeriksaan dengan panggilan balik Keras untuk Model.fit

Di TensorFlow 2, saat Anda menggunakan Keras Model.fit (atau Model.evaluate ) bawaan untuk pelatihan/evaluasi, Anda dapat mengonfigurasi tf.keras.callbacks.ModelCheckpoint lalu meneruskannya ke parameter callbacks Model.fit (atau Model. Model.evaluate ). (Pelajari lebih lanjut di dokumen API dan bagian Menggunakan callback di Pelatihan dan evaluasi dengan panduan metode bawaan.)

Pada contoh di bawah, Anda akan menggunakan panggilan balik tf.keras.callbacks.ModelCheckpoint untuk menyimpan pos pemeriksaan di direktori sementara:

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>

%ls {model_checkpoint_callback.filepath}

assets/  keras_metadata.pb  saved_model.pb  variables/

Langkah selanjutnya

Pelajari lebih lanjut tentang pos pemeriksaan di:

Pelajari lebih lanjut tentang panggilan balik di:

Anda mungkin juga menemukan sumber daya terkait migrasi berikut ini berguna: