c442e87d0b6a88e9787e8e82eb493e0dcf669c4d,doc/source/notebooks/intro_to_gpflow2.pct.py,,,#,266

Before Change

// %%
initial_value = 1.2
a = tf.Variable(initial_value)

// %% [markdown]
// Create `Checkpoint` object:

// %%
ckpt = tf.train.Checkpoint(a=a)
manager = tf.train.CheckpointManager(ckpt, output_logdir, max_to_keep=3)

// %% [markdown]
// Save the variable `a` and change its value right after:

After Change

print_task = ExecuteCallback(callback=print_cb)

// We group these tasks and specify a period of `100` steps for them
fast_tasks = MonitorTaskGroup([model_task, elbo_task, print_task], period=100)

// We also want to see the model"s fit during the optimisation
image_task = ImageToTensorBoard(output_logdir, plot_model, "samples_image")

// We typically don"t want to plot too frequently during optimisation,
// which is why we specify a larger period for this task.
slow_taks = MonitorTaskGroup(image_task, period=500)
monitor = Monitor(fast_tasks, slow_taks)


def monitored_training_loop(epochs: int):
    tf_optimization_step = tf.function(optimization_step)

Instances