# Migrate TensorBoard: TensorFlow's visualization toolkit

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|---|---|---|---|
| [![](https://www.tensorflow.org/images/tf_logo_32px.png) View on TensorFlow.org](https://www.tensorflow.org/guide/migrate/tensorboard) | [![](https://www.tensorflow.org/images/colab_logo_32px.png) Run in Google Colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/migrate/tensorboard.ipynb) | [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/docs/blob/master/site/en/guide/migrate/tensorboard.ipynb) | [![](https://www.tensorflow.org/images/download_logo_32px.png)Download notebook](https://storage.googleapis.com/tensorflow_docs/docs/site/en/guide/migrate/tensorboard.ipynb) |

[TensorBoard](https://www.tensorflow.org/tensorboard) is a built-in tool for providing measurements and visualizations in TensorFlow. Common machine learning experiment metrics, such as accuracy and loss, can be tracked and displayed in TensorBoard. TensorBoard is compatible with TensorFlow 1 and 2 code.

In TensorFlow 1, `tf.estimator.Estimator` saves summaries for TensorBoard by default. In comparison, in TensorFlow 2, summaries can be saved using a [`tf.keras.callbacks.TensorBoard`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/TensorBoard) [callback](https://keras.io/api/callbacks/).

This guide demonstrates how to use TensorBoard, first, in TensorFlow 1 with Estimators, and then, how to carry out the equivalent process in TensorFlow 2.

### Setup

    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

### TensorFlow 1: TensorBoard with tf.estimator

In this TensorFlow 1 example, you instantiate a `tf.estimator.DNNClassifier`, train and evaluate it on the MNIST dataset, and use TensorBoard to display the metrics:

    %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)

    %tensorboard --logdir {classifier.model_dir}

![](https://www.tensorflow.org/static/guide/migrate/images/tensorboard_TF1.png)

### TensorFlow 2: TensorBoard with a Keras callback and Model.fit

In this TensorFlow 2 example, you create and store logs with the [`tf.keras.callbacks.TensorBoard`](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/TensorBoard) callback, and train the model. The callback tracks the accuracy and loss per epoch. It is passed to [`Model.fit`](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit) in the `callbacks` list.

    %reload_ext tensorboard

    def create_model():
      return tf.keras.models.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28), name='layers_flatten'),
        tf.keras.layers.Dense(512, activation='relu', name='layers_dense'),
        tf.keras.layers.Dropout(0.2, name='layers_dropout'),
        tf.keras.layers.Dense(10, activation='softmax', name='layers_dense_2')
      ])

    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])

    %tensorboard --logdir {tensorboard_callback.log_dir}

![](https://www.tensorflow.org/static/guide/migrate/images/tensorboard_TF2.png)

# Next steps

- Learn more about TensorBoard in the [Get started](https://www.tensorflow.org/tensorboard/get_started) guide.
- For lower level APIs, refer to the [tf.summary migration to TensorFlow 2](https://www.tensorflow.org/tensorboard/migrate) guide.