2023.11.23
阅读的deepxde version: 1.9.0
1. train_aux_vars ，即pde当中的第三个参数
这个变量的含义困惑很久。最后发现就是operator learning的PDEs方程parameters。
脉络：def pde aux_vars->
deepxde目前支持tf1最多，但是对其他框架也有支持，仓库的主要维护者Lu Lu跟百度应该有合作，目前对paddlepaddle的支持正在提升。因此，实际上对于很多函数，作者写了4遍，每个框架都写一遍，基本同样的功能，但里面的细节不同。
pdeoperator.py 中的PDEoperator坐标类的train_next_batch方法
（注意：我这里命名是按着记忆直接写的，不是源码中的真实名称）
python
def train_next_batch(self, batch_size=None):
if self.train_x is None:
func_feats = self.func_space.random(self.num_func)
func_vals = self.func_space.eval_batch(func_feats, self.eval_pts)
vx = self.func_space.eval_batch(
func_feats, self.pde.train_x[:, self.func_vars]
)
self.train_x = (func_vals, self.pde.train_x)
self.train_aux_vars = vx
if self.batch_size is None:
return self.train_x, self.train_y, self.train_aux_vars
indices = self.train_sampler.get_next(self.batch_size)
traix_x = (self.train_x[0][indices], self.train_x[1])
return traix_x, self.train_y, self.train_aux_vars[indices]model.py
python
def set_data_train(self, X_train, y_train, train_aux_vars=None):
self.X_train = X_train
self.y_train = y_train
self.train_aux_vars = train_aux_vars
python
self.train_state.set_data_train(
*self.data.train_next_batch(self.batch_size)
)根据这些代码段顺藤摸瓜确定了train_aux_vars的定位：Parameters of PDEs.
python
def outputs_losses(training, inputs, targets, auxiliary_vars, losses_fn):
self.net.auxiliary_vars = auxiliary_vars
# Don't call outputs() decorated by @tf.function above, otherwise the
# gradient of outputs wrt inputs will be lost here.
outputs_ = self.net(inputs, training=training)
# Data losses
losses = losses_fn(targets, outputs_, loss_fn, inputs, self)
if not isinstance(losses, list):
losses = [losses]
# Regularization loss
if self.net.regularizer is not None:
losses += [tf.math.reduce_sum(self.net.losses)]
losses = tf.convert_to_tensor(losses)
# Weighted losses
if loss_weights is not None:
losses *= loss_weights
return outputs_, losses值得一提的是作者从头到尾都在使用 TrainState 来储存训练当中的各种变量。
python
class TrainState:
def __init__(self):
self.epoch = 0
self.step = 0
# Current data
self.X_train = None
self.y_train = None
self.train_aux_vars = None
self.X_test = None
self.y_test = None
self.test_aux_vars = None
# Results of current step
# Train results
self.loss_train = None
self.y_pred_train = None
# Test results
self.loss_test = None
self.y_pred_test = None
self.y_std_test = None
self.metrics_test = None
# The best results correspond to the min train loss
self.best_step = 0
self.best_loss_train = np.inf
self.best_loss_test = np.inf
self.best_y = None
self.best_ystd = None
self.best_metrics = None
def set_data_train(self, X_train, y_train, train_aux_vars=None):
self.X_train = X_train
self.y_train = y_train
self.train_aux_vars = train_aux_vars
def set_data_test(self, X_test, y_test, test_aux_vars=None):
self.X_test = X_test
self.y_test = y_test
self.test_aux_vars = test_aux_vars
def update_best(self):
if self.best_loss_train > np.sum(self.loss_train):
self.best_step = self.step
self.best_loss_train = np.sum(self.loss_train)
self.best_loss_test = np.sum(self.loss_test)
self.best_y = self.y_pred_test
self.best_ystd = self.y_std_test
self.best_metrics = self.metrics_test
def disregard_best(self):
self.best_loss_train = np.inf由train state可见pde前两个变量的含义就是偏微分方程组的坐标输入和解输出
deepxde采用LGPL，是比较激进的一类开源许可证。激进的开源是一种商业策略，参见我写的另一篇文章。
2023-11-27
在物理启发时，compile不要加metrics. 因为物理启发不会用标签。
cpp
model.compile(
'adam',
lr=1e-4,
decay=("inverse time", 1, 1e-4),
# metrics=["mean l2 relative error"],
)到源码中会发现
Model.py
cpp
@utils.timing
def train(
self,
iterations=None,
batch_size=None,
display_every=1000,
disregard_previous_best=False,
callbacks=None,
model_restore_path=None,
model_save_path=None,
epochs=None,
):
"""Trains the model.
Args:
iterations (Integer): Number of iterations to train the model, i.e., number
of times the network weights are updated.
batch_size: Integer, tuple, or ``None``.
- If you solve PDEs via ``dde.data.PDE`` or ``dde.data.TimePDE``, do not use `batch_size`, and instead use
`dde.callbacks.PDEPointResampler
<https://deepxde.readthedocs.io/en/latest/modules/deepxde.html#deepxde.callbacks.PDEPointResampler>`_,
see an `example <https://github.com/lululxvi/deepxde/blob/master/examples/diffusion_1d_resample.py>`_.
- For DeepONet in the format of Cartesian product, if `batch_size` is an Integer,
then it is the batch size for the branch input; if you want to also use mini-batch for the trunk net input,
set `batch_size` as a tuple, where the fist number is the batch size for the branch net input
and the second number is the batch size for the trunk net input.
display_every (Integer): Print the loss and metrics every this steps.
disregard_previous_best: If ``True``, disregard the previous saved best
model.
callbacks: List of ``dde.callbacks.Callback`` instances. List of callbacks
to apply during training.
model_restore_path (String): Path where parameters were previously saved.
model_save_path (String): Prefix of filenames created for the checkpoint.
epochs (Integer): Deprecated alias to `iterations`. This will be removed in
a future version.
"""
if iterations is None and epochs is not None:
print(
"Warning: epochs is deprecated and will be removed in a future version."
" Use iterations instead."
)
iterations = epochs
self.batch_size = batch_size
self.callbacks = CallbackList(callbacks=callbacks)
self.callbacks.set_model(self)
if disregard_previous_best:
self.train_state.disregard_best()
if backend_name == "tensorflow.compat.v1":
if self.train_state.step == 0:
self.sess.run(tf.global_variables_initializer())
if config.hvd is not None:
bcast = config.hvd.broadcast_global_variables(0)
self.sess.run(bcast)
else:
utils.guarantee_initialized_variables(self.sess)
if model_restore_path is not None:
self.restore(model_restore_path, verbose=1)
if config.rank == 0:
print("Training model...\n")
self.stop_training = False
self.train_state.set_data_train(*self.data.train_next_batch(self.batch_size))
self.train_state.set_data_test(*self.data.test())
self._test()
self.callbacks.on_train_begin()
if optimizers.is_external_optimizer(self.opt_name):
if backend_name == "tensorflow.compat.v1":
self._train_tensorflow_compat_v1_scipy(display_every)
elif backend_name == "tensorflow":
self._train_tensorflow_tfp()
elif backend_name == "pytorch":
self._train_pytorch_lbfgs()
elif backend_name == "paddle":
self._train_paddle_lbfgs()
else:
if iterations is None:
raise ValueError("No iterations for {}.".format(self.opt_name))
self._train_sgd(iterations, display_every)
self.callbacks.on_train_end()
if config.rank == 0:
print("")
display.training_display.summary(self.train_state)
if model_save_path is not None:
self.save(model_save_path, verbose=1)
return self.losshistory, self.train_state
def _train_sgd(self, iterations, display_every):
for i in range(iterations):
self.callbacks.on_epoch_begin()
self.callbacks.on_batch_begin()
self.train_state.set_data_train(
*self.data.train_next_batch(self.batch_size)
)
self._train_step(
self.train_state.X_train,
self.train_state.y_train,
self.train_state.train_aux_vars,
)
self.train_state.epoch += 1
self.train_state.step += 1
if self.train_state.step % display_every == 0 or i + 1 == iterations:
self._test()
self.callbacks.on_batch_end()
self.callbacks.on_epoch_end()
if self.stop_training:
break里面经常出现测试代码：
self._test()