Test_summary_writer = tf.summary.create_file_writer(test_log_dir) Train_summary_writer = tf.summary.create_file_writer(train_log_dir) Test_log_dir = 'logs/gradient_tape/' + current_time + '/test' Train_log_dir = 'logs/gradient_tape/' + current_time + '/train' Set up summary writers to write the summaries to disk in a different logs directory: current_time = ().strftime("%Y%m%d-%H%M%S") Optimizer.apply_gradients(zip(grads, ainable_variables)) Predictions = model(x_train, training=True) Test_accuracy = tf.('test_accuracy')ĭefine the training and test functions: def train_step(model, optimizer, x_train, y_train): Test_loss = tf.('test_loss', dtype=tf.float32) Train_loss = tf.('train_loss', dtype=tf.float32) Choose loss and optimizer: loss_object = tf.()Ĭreate stateful metrics that can be used to accumulate values during training and logged at any point: # Define our metrics
The training code follows the advanced quickstart tutorial, but shows how to log metrics to TensorBoard. Train_dataset = train_dataset.shuffle(60000).batch(64) Test_dataset = tf._tensor_slices((x_test, y_test)) Use the same dataset as above, but convert it to tf.data.Dataset to take advantage of batching capabilities: train_dataset = tf._tensor_slices((x_train, y_train))
When training with methods such as tf.GradientTape(), use tf.summary to log the required information. You can see what other plugins are available in TensorBoard by clicking on the "inactive" dropdown towards the top right.
For example, the Keras TensorBoard callback lets you log images and embeddings as well. This can be useful to visualize weights and biases and verify that they are changing in an expected way.Īdditional TensorBoard plugins are automatically enabled when you log other types of data. The Distributions and Histograms dashboards show the distribution of a Tensor over time.In this case, the Keras graph of layers is shown which can help you ensure it is built correctly. The Graphs dashboard helps you visualize your model.You can use it to also track training speed, learning rate, and other scalar values. The Scalars dashboard shows how the loss and metrics change with every epoch.%tensorboard -logdir logs/fitĪ brief overview of the dashboards shown (tabs in top navigation bar): On the command line, run the same command without "%". In notebooks, use the %tensorboard line magic. The two interfaces are generally the same. Start TensorBoard through the command line or within a notebook experience. Tensorboard_callback = tf.(log_dir=log_dir, histogram_freq=1) Place the logs in a timestamped subdirectory to allow easy selection of different training runs. Additionally, enable histogram computation every epoch with histogram_freq=1 (this is off by default) When training with Keras's Model.fit(), adding the tf. callback ensures that logs are created and stored. (x_train, y_train),(x_test, y_test) = mnist.load_data() Using the MNIST dataset as the example, normalize the data and write a function that creates a simple Keras model for classifying the images into 10 classes. # Clear any logs from previous runs rm -rf. # Load the TensorBoard notebook extension The remaining guides in this website provide more details on specific capabilities, many of which are not included here. This quickstart will show how to quickly get started with TensorBoard. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more. TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. In machine learning, to improve something you often need to be able to measure it.