TensorFlow가 5월 14일 Google I/O로 돌아왔습니다! 지금 등록하세요

TensorBoard 이동: TensorFlow의 시각화 도구모음

TensorFlow.org에서 보기

Google Colab에서 실행

GitHub에서 소스 보기

노트북 다운로드

TensorBoard 는 TensorFlow에서 측정과 시각화를 제공하는 내장 도구이다. 정확도와 손실 같은, 머신러닝 실험의 공통의 기준들은 TensorBoard가 추적하고 보여줄 수 있다. TensorBoard 는 TensorFlow 1과 2의 코드와 호환된다.

TensorFlow 1에서는, tf.estimator.Estimator 기본적으로 TensorBoard에 대한 개요를 저장한다. 그에 비해, TensorFlow 2에서는 , 개요는 tf.keras.callbacks.TensorBoard callback 을 사용해서 저장한다.

이 가이드는 첫째로 TensorFlow 1에서 평가기준을 가지고 어떻게 TensorBoard를 이용하는지와 TensorFlow 2에서 어떻게 동등한 프로세스를 수행할 수 있는지에 대해서 보여준다.

설정

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

2022-12-14 20:35:34.703412: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-14 20:35:34.703523: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-14 20:35:34.703532: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

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

TensorFlow 1: tf.estimator와 TensorBoard

이 TensorFlow 1 예시에서, tf.estimator.DNNClassifier을 예시로 들어 설명하는데, MNIST dataset에서 훈련하고 평가하며, 기준들을 보여주기 위해서 TensorBoard를 이용한다:

%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': '/tmpfs/tmp/tmpcd6gyy_j', '_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 /tmpfs/tmp/ipykernel_43295/2752664473.py:20: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.

WARNING:tensorflow:From /tmpfs/tmp/ipykernel_43295/2752664473.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/python3.9/site-packages/tensorflow/python/training/training_util.py:396: 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/python3.9/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:60: 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/python3.9/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/python3.9/site-packages/tensorflow/python/training/monitored_session.py:910: 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.
2022-12-14 20:35:40.429332: W tensorflow/core/common_runtime/type_inference.cc:339] Type inference failed. This indicates an invalid graph that escaped type checking. Error message: INVALID_ARGUMENT: expected compatible input types, but input 1:
type_id: TFT_OPTIONAL
args {
  type_id: TFT_PRODUCT
  args {
    type_id: TFT_TENSOR
    args {
      type_id: TFT_INT64
    }
  }
}
 is neither a subtype nor a supertype of the combined inputs preceding it:
type_id: TFT_OPTIONAL
args {
  type_id: TFT_PRODUCT
  args {
    type_id: TFT_TENSOR
    args {
      type_id: TFT_INT32
    }
  }
}

    while inferring type of node 'dnn/zero_fraction/cond/output/_18'
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpcd6gyy_j/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 /tmpfs/tmp/tmpcd6gyy_j/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 2022-12-14T20:35:41
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpcd6gyy_j/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]
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 : 0.29574s
INFO:tensorflow:Finished evaluation at 2022-12-14-20:35:41
INFO:tensorflow:Saving dict for global step 1: accuracy = 0.13203125, average_loss = 2.2470224, global_step = 1, loss = 287.61887
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1: /tmpfs/tmp/tmpcd6gyy_j/model.ckpt-1
INFO:tensorflow:loss = 116.3344, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2...
INFO:tensorflow:Saving checkpoints for 2 into /tmpfs/tmp/tmpcd6gyy_j/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 2022-12-14T20:35:42
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpcd6gyy_j/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]
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 : 0.29143s
INFO:tensorflow:Finished evaluation at 2022-12-14-20:35:42
INFO:tensorflow:Saving dict for global step 2: accuracy = 0.1921875, average_loss = 2.1825042, global_step = 2, loss = 279.36053
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2: /tmpfs/tmp/tmpcd6gyy_j/model.ckpt-2
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3...
INFO:tensorflow:Saving checkpoints for 3 into /tmpfs/tmp/tmpcd6gyy_j/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 2022-12-14T20:35:42
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpcd6gyy_j/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]
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 : 0.29103s
INFO:tensorflow:Finished evaluation at 2022-12-14-20:35:43
INFO:tensorflow:Saving dict for global step 3: accuracy = 0.25234374, average_loss = 2.1178257, global_step = 3, loss = 271.0817
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3: /tmpfs/tmp/tmpcd6gyy_j/model.ckpt-3
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4...
INFO:tensorflow:Saving checkpoints for 4 into /tmpfs/tmp/tmpcd6gyy_j/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 2022-12-14T20:35:43
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpcd6gyy_j/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]
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 : 0.29107s
INFO:tensorflow:Finished evaluation at 2022-12-14-20:35:43
INFO:tensorflow:Saving dict for global step 4: accuracy = 0.346875, average_loss = 2.047689, global_step = 4, loss = 262.1042
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4: /tmpfs/tmp/tmpcd6gyy_j/model.ckpt-4
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5...
INFO:tensorflow:Saving checkpoints for 5 into /tmpfs/tmp/tmpcd6gyy_j/model.ckpt.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/saver.py:1064: remove_checkpoint (from tensorflow.python.checkpoint.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 2022-12-14T20:35:43
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpcd6gyy_j/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]
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 : 0.28687s
INFO:tensorflow:Finished evaluation at 2022-12-14-20:35:44
INFO:tensorflow:Saving dict for global step 5: accuracy = 0.43046874, average_loss = 1.9782779, global_step = 5, loss = 253.21957
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 5: /tmpfs/tmp/tmpcd6gyy_j/model.ckpt-5
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 6...
INFO:tensorflow:Saving checkpoints for 6 into /tmpfs/tmp/tmpcd6gyy_j/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 2022-12-14T20:35:44
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpcd6gyy_j/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]
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 : 0.28808s
INFO:tensorflow:Finished evaluation at 2022-12-14-20:35:44
INFO:tensorflow:Saving dict for global step 6: accuracy = 0.46953124, average_loss = 1.9166076, global_step = 6, loss = 245.32578
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 6: /tmpfs/tmp/tmpcd6gyy_j/model.ckpt-6
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 7...
INFO:tensorflow:Saving checkpoints for 7 into /tmpfs/tmp/tmpcd6gyy_j/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 2022-12-14T20:35:45
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpcd6gyy_j/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 : 0.28395s
INFO:tensorflow:Finished evaluation at 2022-12-14-20:35:45
INFO:tensorflow:Saving dict for global step 7: accuracy = 0.5078125, average_loss = 1.8560688, global_step = 7, loss = 237.57681
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 7: /tmpfs/tmp/tmpcd6gyy_j/model.ckpt-7
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 8...
INFO:tensorflow:Saving checkpoints for 8 into /tmpfs/tmp/tmpcd6gyy_j/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 2022-12-14T20:35:45
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpcd6gyy_j/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 : 0.28429s
INFO:tensorflow:Finished evaluation at 2022-12-14-20:35:45
INFO:tensorflow:Saving dict for global step 8: accuracy = 0.52265626, average_loss = 1.7997448, global_step = 8, loss = 230.36734
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 8: /tmpfs/tmp/tmpcd6gyy_j/model.ckpt-8
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 9...
INFO:tensorflow:Saving checkpoints for 9 into /tmpfs/tmp/tmpcd6gyy_j/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 2022-12-14T20:35:46
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpcd6gyy_j/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 : 0.28570s
INFO:tensorflow:Finished evaluation at 2022-12-14-20:35:46
INFO:tensorflow:Saving dict for global step 9: accuracy = 0.534375, average_loss = 1.7498262, global_step = 9, loss = 223.97775
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 9: /tmpfs/tmp/tmpcd6gyy_j/model.ckpt-9
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10...
INFO:tensorflow:Saving checkpoints for 10 into /tmpfs/tmp/tmpcd6gyy_j/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 2022-12-14T20:35:46
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpcd6gyy_j/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 : 0.28561s
INFO:tensorflow:Finished evaluation at 2022-12-14-20:35:47
INFO:tensorflow:Saving dict for global step 10: accuracy = 0.53984374, average_loss = 1.7024753, global_step = 10, loss = 217.91684
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10: /tmpfs/tmp/tmpcd6gyy_j/model.ckpt-10
INFO:tensorflow:Loss for final step: 86.60825.
({'accuracy': 0.53984374,
  'average_loss': 1.7024753,
  'loss': 217.91684,
  'global_step': 10},
 [])

%tensorboard --logdir {classifier.model_dir}

TensorFlow 2: Keras callback and Model.fit 과 TensorBoard

이 TensorFlow 2 예시에서,tf.keras.callbacks.TensorBoard callback와 함께 로그를 생성하고 저장하고, 모델을 훈련시킨다. callback 정확도와 loss per epoch를 추적한다. 그리고 in the callbacks 리스트 안에 있는 Model.fit 에 전달된다.

%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: 0.2193 - accuracy: 0.9351 - val_loss: 0.1083 - val_accuracy: 0.9665
Epoch 2/10
1875/1875 [==============================] - 2s 1ms/step - loss: 0.0963 - accuracy: 0.9707 - val_loss: 0.0878 - val_accuracy: 0.9734
Epoch 3/10
1875/1875 [==============================] - 2s 1ms/step - loss: 0.0695 - accuracy: 0.9784 - val_loss: 0.0683 - val_accuracy: 0.9786
Epoch 4/10
1875/1875 [==============================] - 2s 1ms/step - loss: 0.0523 - accuracy: 0.9830 - val_loss: 0.0696 - val_accuracy: 0.9787
Epoch 5/10
1875/1875 [==============================] - 2s 1ms/step - loss: 0.0430 - accuracy: 0.9862 - val_loss: 0.0648 - val_accuracy: 0.9819
Epoch 6/10
1875/1875 [==============================] - 2s 1ms/step - loss: 0.0358 - accuracy: 0.9881 - val_loss: 0.0618 - val_accuracy: 0.9809
Epoch 7/10
1875/1875 [==============================] - 2s 1ms/step - loss: 0.0317 - accuracy: 0.9894 - val_loss: 0.0756 - val_accuracy: 0.9785
Epoch 8/10
1875/1875 [==============================] - 2s 1ms/step - loss: 0.0263 - accuracy: 0.9911 - val_loss: 0.0710 - val_accuracy: 0.9822
Epoch 9/10
1875/1875 [==============================] - 2s 1ms/step - loss: 0.0235 - accuracy: 0.9920 - val_loss: 0.0705 - val_accuracy: 0.9826
Epoch 10/10
1875/1875 [==============================] - 2s 1ms/step - loss: 0.0231 - accuracy: 0.9923 - val_loss: 0.0666 - val_accuracy: 0.9825
<keras.callbacks.History at 0x7fa9b0134fa0>

%tensorboard --logdir {tensorboard_callback.log_dir}

다음 단계

Get started 가이드에서 TensorBoard에 대한 더 많은 것을 알아보기.
lower level APIs에 대해서는, tf.summary migration to TensorFlow 2 가이드를 참조.