tf_dash.py

from tensordash.tensordash import Customdash

histories = Customdash(
    ModelName = '<YOUR_MODEL_NAME>',
    email = '<YOUR_EMAIL_ID>', 
    password = '<YOUR PASSWORD>')

try:

    for epoch in range(num_epochs):
        epoch_loss_avg = tf.keras.metrics.Mean()
        epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()

        for x, y in train_dataset:

            loss_value, grads = grad(model, x, y)
            optimizer.apply_gradients(zip(grads, model.trainable_variables))

            epoch_loss_avg(loss_value)
            epoch_accuracy(y, model(x, training=True))

        train_loss_results.append(epoch_loss_avg.result())
        train_accuracy_results.append(epoch_accuracy.result())

        histories.sendLoss(loss = epoch_loss_avg.result(), accuracy = epoch_accuracy.result(), epoch = epoch, total_epochs = epochs) // Add this line to your training loop

except:
    histories.sendCrash()