def setup_training(
snapshots: np.ndarray,
hparams: tf.contrib.training.HParams) -> Tuple[tf.Tensor, tf.Tensor]:
"""Create Tensors for training.
Args:
snapshots: np.ndarray with shape [examples, x] with high-resolution
training data.
hparams: hyperparameters for training.
Returns:
Tensors for the current loss, and for taking a training step.
"""
dataset = model.make_dataset(snapshots,
hparams,
dataset_type=model.Dataset.TRAINING)
tensors = dataset.make_one_shot_iterator().get_next()
predictions = model.predict_result(tensors['inputs'], hparams)
loss_per_head = model.loss_per_head(predictions,
labels=tensors['labels'],
baseline=tensors['baseline'],
hparams=hparams)
loss = model.weighted_loss(loss_per_head, hparams)
train_step = create_training_step(loss, hparams)
return loss, train_step
def __init__(self,
snapshots: np.ndarray,
hparams: tf.contrib.training.HParams,
training: bool = False):
"""Initialize an object for running inference.
Args:
snapshots: np.ndarray with shape [examples, x] with high-resolution
training data.
hparams: hyperparameters for training.
training: whether to evaluate on training or validation datasets.
"""
if training:
dataset_type = model.Dataset.TRAINING
else:
dataset_type = model.Dataset.VALIDATION
dataset = model.make_dataset(snapshots,
hparams,
dataset_type=dataset_type,
repeat=False,
evaluation=True)
iterator = dataset.make_initializable_iterator()
data = iterator.get_next()
_, coarse_equation = equations.from_hparams(hparams)
predictions = model.predict_result(data['inputs'], hparams)
loss_per_head = model.loss_per_head(predictions,
labels=data['labels'],
baseline=data['baseline'],
hparams=hparams)
loss = model.weighted_loss(loss_per_head, hparams)
results = dict(data, predictions=predictions)
metrics = {
k: tf.contrib.metrics.streaming_concat(v)
for k, v in results.items()
}
metrics['loss'] = tf.metrics.mean(loss)
space_loss, time_loss, integrated_loss = model.result_unstack(
loss_per_head, coarse_equation)
metrics['loss/space_derivatives'] = tf.metrics.mean(space_loss)
metrics['loss/time_derivative'] = tf.metrics.mean(time_loss)
if integrated_loss is not None:
metrics['loss/integrated_solution'] = tf.metrics.mean(
integrated_loss)
initializer = tf.group(iterator.initializer,
tf.local_variables_initializer())
self._initializer = initializer
self._metrics = metrics
def setup_training(dataset, hparams, scale=1.0):
# predict u, u_x, u_t as in training
tensors = dataset.make_one_shot_iterator().get_next()
predictions = model.predict_result(tensors['inputs'], hparams)
loss_per_head = model.loss_per_head(predictions,
labels=tensors['labels'],
baseline=tensors['baseline'],
hparams=hparams)
loss = model.weighted_loss(loss_per_head, hparams)
train_step = pde.training.create_training_step(loss, hparams)
return loss, train_step
if (step + 1) % hparams.eval_interval == 0:
print(step, sess.run(loss))
# save
save_path = 'tmp_model.ckpt'
saver = tf.train.Saver()
saver.save(sess, save_path)
# prediction
demo_dataset = pde.model.make_dataset(snapshots,
hparams,
Dataset.VALIDATION,
repeat=False,
evaluation=True)
tensors = demo_dataset.make_one_shot_iterator().get_next()
tensors['predictions'] = model.predict_result(tensors['inputs'], hparams)
array_list = []
while True:
try:
array_list.append(sess.run(tensors))
except tf.errors.OutOfRangeError:
break
arrays = {k: np.concatenate([d[k] for d in array_list]) for k in array_list[0]}
# do some simple plots
# inputs = arrays['inputs']
# predictions = arrays['predictions'][:, :, 0]
# labels = arrays['labels'][:, :, 0]
# print("inputs shape: ", inputs.shape)
