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TensorBoard | TensorFlow
WEBtensorboard provides the visualization and tooling needed for machine learning experimentation: Tracking and visualizing metrics such as loss and accuracy. Visualizing the model graph (ops and layers) Viewing histograms of weights, biases, or other tensors as they change over time.
Tensorflow.orgGet started with TensorBoard | TensorFlow
WEBOct 25, 2023 — tensorboard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more.
Tensorflow.orgExamining the TensorFlow Graph | TensorBoard
WEBOct 25, 2023 — tensorboard’s Graphs dashboard is a powerful tool for examining your TensorFlow model. You can quickly view a conceptual graph of your model’s structure and ensure it matches your intended design. You can also view a op-level graph to understand how TensorFlow understands your program.
Tensorflow.orgTensorBoard Scalars: Logging training metrics in Keras
WEBApr 28, 2024 — tensorboard reads log data from the log directory hierarchy. In this notebook, the root log directory is logs/scalars, suffixed by a timestamped subdirectory. The timestamped subdirectory enables you to easily identify and select training runs as you use tensorboard and iterate on your model.
Tensorflow.orgUsing TensorBoard in Notebooks | TensorFlow
WEBFeb 11, 2023 — tensorboard can be used directly within notebook experiences such as Colab and Jupyter. This can be helpful for sharing results, integrating tensorboard into existing workflows, and using tensorboard without installing anything locally.
Tensorflow.orgIntroducing TensorBoard.dev: a new way to share your ML …
WEBDec 2, 2019 — That is why we launched tensorboard.dev: a managed service (currently in preview) that enables you to easily host, track, and share your ML experiments for free. Simply upload your tensorboard logs and receive a link that can be viewed by everyone, with no installation or setup.
Blog.tensorflow.orgTensorFlow Profiler: Profile model performance | TensorBoard
WEBJan 6, 2022 — Train an image classification model with tensorboard callbacks. In this tutorial, you explore the capabilities of the TensorFlow Profiler by capturing the performance profile obtained by training a model to classify images in the MNIST dataset. Use TensorFlow datasets to import the training data and split it into training and test sets.
Tensorflow.orgVisualizing Data using the Embedding Projector in TensorBoard
WEBJul 6, 2023 — Using the tensorboard Embedding Projector, you can graphically represent high dimensional embeddings. This can be helpful in visualizing, examining, and understanding your embedding layers. In this tutorial, you will learn how visualize this type of trained layer.
Tensorflow.orgDisplaying image data in TensorBoard | TensorFlow
WEBFeb 11, 2023 — Using the TensorFlow Image Summary API, you can easily log tensors and arbitrary images and view them in tensorboard. This can be extremely helpful to sample and examine your input data, or to visualize layer weights and generated tensors .
Tensorflow.orgInteractive supervision with TensorBoard — The TensorFlow Blog
WEBNov 25, 2017 — The tensorboard projector features t-distributed Stochastic Neighborhood Embedding (t-SNE) for visualizing high-dimensional datasets, since it is a well-balanced dimensionality reduction algorithm that requires no labels yet reveals latent structure in many types of data.
Blog.tensorflow.org