TensorBoard for Visualizations

TensorBoard is a great interactive visualization tool that you can use to view the learning curves during training, compare learning curves between multiple runs, visualize the computation graphs, analyze training statistics, view images generated by your model, visualize complex multidimensional data automatically when you install TensorFlow, so you already have it.

Getting Started with TensorBoard

To use TensorBoard, you must modify your program so that it outputs the data you want to visualize to special binary log files called event files. Each binary data record is called a summary. The TensorBoard server will monitor the log directory, and it will automatically pick up the changes and update the visualizations. This allows you to visualize live data such as learning curves during training.

In general, you want to point the TensorBoard server to a root log directory and configure your program so that it writes to a different subdirectory every time it runs. this way, the same TensorBoard server instance will allow you to visualize and compare data from multiple runs of your program, without getting mixed up.

Let’s get started by loading the TensorBoard notebook extension:

# Load the TensorBoard notebook extension %load_ext tensorboard import tensorflow as tf import datetime # Clear any logs from previous runs !rm -rf ./logs/

I will use the MNIST dataset in this article, I will normalize the data and then write a function that will create a Keras model to classify the images into 10 classes:

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 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') ])

Using TensorBoard with Keras

When we train with a Keral’s Model.fit(), by adding the tf.keras.callbacks. The TensorBoard callbacks will ensure that all the logs have been created and stored successfully. It also allows histogram computations with every epoch where histogram_freq=1.

Also Read: 10 Machine Learning Projects to Boost your Portfolio.

Now, lets place the logs in a timestamped subdirectory to enable easy selection of multiple training runs:

model = create_model() model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1) model.fit(x=x_train, y=y_train, epochs=5, validation_data=(x_test, y_test), callbacks=[tensorboard_callback])

Now, start the TensorBoard through the command line or within a notebook experience. The two interfaces are generally the same. In notebooks, use the %tensorboard line magic. On the command line, run the same command without “%”.

%tensorboard --logdir logs/fit
visualization

Using TensorBoard with Other Methods

When training with methods such as tf.GradientTape(), use tf.summary to log the required information. Use the same dataset as above, but convert it to tf.data.Dataset to take advantage of batching capabilities:

train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)) train_dataset = train_dataset.shuffle(60000).batch(64) test_dataset = test_dataset.batch(64) loss_object = tf.keras.losses.SparseCategoricalCrossentropy() optimizer = tf.keras.optimizers.Adam()

Create stateful metrics that can be used to accumulate values during training and logged at any point:

# Define our metrics train_loss = tf.keras.metrics.Mean('train_loss', dtype=tf.float32) train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy('train_accuracy') test_loss = tf.keras.metrics.Mean('test_loss', dtype=tf.float32) test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy('test_accuracy')

Define the training and test functions:

def train_step(model, optimizer, x_train, y_train): with tf.GradientTape() as tape: predictions = model(x_train, training=True) loss = loss_object(y_train, predictions) grads = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) train_loss(loss) train_accuracy(y_train, predictions) def test_step(model, x_test, y_test): predictions = model(x_test) loss = loss_object(y_test, predictions) test_loss(loss) test_accuracy(y_test, predictions)

To set up the summary writers to write the summaries to the disk in a different logs directory:

current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S") train_log_dir = 'logs/gradient_tape/' + current_time + '/train' test_log_dir = 'logs/gradient_tape/' + current_time + '/test' train_summary_writer = tf.summary.create_file_writer(train_log_dir) test_summary_writer = tf.summary.create_file_writer(test_log_dir)

Now let’s start training by using the tf.summary.scalar() to log the metrics during the tarining or testing with the scope of summary writers to write the summary in the disk. It’s up to you to select which metrics to log and how how many times to repeat it, you have full control over it.

model = create_model() # reset our model EPOCHS = 5 for epoch in range(EPOCHS): for (x_train, y_train) in train_dataset: train_step(model, optimizer, x_train, y_train) with train_summary_writer.as_default(): tf.summary.scalar('loss', train_loss.result(), step=epoch) tf.summary.scalar('accuracy', train_accuracy.result(), step=epoch) for (x_test, y_test) in test_dataset: test_step(model, x_test, y_test) with test_summary_writer.as_default(): tf.summary.scalar('loss', test_loss.result(), step=epoch) tf.summary.scalar('accuracy', test_accuracy.result(), step=epoch) template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}' print (template.format(epoch+1, train_loss.result(), train_accuracy.result()*100, test_loss.result(), test_accuracy.result()*100)) # Reset metrics every epoch train_loss.reset_states() test_loss.reset_states() train_accuracy.reset_states() test_accuracy.reset_states()
Epoch 1, Loss: 0.24321186542510986, Accuracy: 92.84333801269531, Test Loss: 0.13006582856178284, Test Accuracy: 95.9000015258789
Epoch 2, Loss: 0.10446818172931671, Accuracy: 96.84833526611328, Test Loss: 0.08867532759904861, Test Accuracy: 97.1199951171875
Epoch 3, Loss: 0.07096975296735764, Accuracy: 97.80166625976562, Test Loss: 0.07875105738639832, Test Accuracy: 97.48999786376953
Epoch 4, Loss: 0.05380449816584587, Accuracy: 98.34166717529297, Test Loss: 0.07712937891483307, Test Accuracy: 97.56999969482422
Epoch 5, Loss: 0.041443776339292526, Accuracy: 98.71833038330078, Test Loss: 0.07514958828687668, Test Accuracy: 97.5

Now, let’s open TensorBoard again, but this time point it to another directory:

%tensorboard --logdir logs/gradient_tape
tensorboard

Also Read: Multiclass Classification in Machine Learning.

So, I hope you liked this article on TensorBoard. Feel free to ask you valuable questions in the comment section below.

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