Rename TrainState.epoch as TrainState.iteration to better represent its actual meaning.

deepxde/callbacks.py

@@ -151,7 +151,7 @@ def on_epoch_end(self):
                 if self.verbose > 0:
                     print(
                         "Epoch {}: {} improved from {:.2e} to {:.2e}, saving model to {} ...\n".format(
-                            self.model.train_state.epoch,
+                            self.model.train_state.iteration,
                             self.monitor,
                             self.best,
                             current,
@@ -224,7 +224,7 @@ def on_train_begin(self):
             self.best = np.inf if self.monitor_op == np.less else -np.inf
 
     def on_epoch_end(self):
-        if self.model.train_state.epoch < self.start_from_epoch:
+        if self.model.train_state.iteration < self.start_from_epoch:
             return
         current = self.get_monitor_value()
         if self.monitor_op(current - self.min_delta, self.best):
@@ -233,7 +233,7 @@ def on_epoch_end(self):
         else:
             self.wait += 1
             if self.wait >= self.patience:
-                self.stopped_epoch = self.model.train_state.epoch
+                self.stopped_epoch = self.model.train_state.iteration
                 self.model.stop_training = True
 
     def on_train_end(self):
@@ -274,7 +274,7 @@ def on_epoch_end(self):
             self.model.stop_training = True
             print(
                 "\nStop training as time used up. time used: {:.1f} mins, epoch trained: {}".format(
-                    (time.time() - self.t_start) / 60, self.model.train_state.epoch
+                    (time.time() - self.t_start) / 60, self.model.train_state.iteration
                 )
             )
 
@@ -347,7 +347,7 @@ def on_train_begin(self):
             self.value = [var.value for var in self.var_list]
 
         print(
-            self.model.train_state.epoch,
+            self.model.train_state.iteration,
             utils.list_to_str(self.value, precision=self.precision),
             file=self.file,
         )
@@ -420,7 +420,7 @@ def op(inputs, params):
     def on_train_begin(self):
         self.on_predict_end()
         print(
-            self.model.train_state.epoch,
+            self.model.train_state.iteration,
             utils.list_to_str(self.value.flatten().tolist(), precision=self.precision),
             file=self.file,
         )

deepxde/model.py

@@ -1,6 +1,7 @@
 __all__ = ["LossHistory", "Model", "TrainState"]
 
 import pickle
+import warnings
 from collections import OrderedDict
 
 import numpy as np
@@ -715,7 +716,7 @@ def _train_sgd(self, iterations, display_every, verbose=1):
                 self.train_state.train_aux_vars,
             )
 
-            self.train_state.epoch += 1
+            self.train_state.iteration += 1
             self.train_state.step += 1
             if self.train_state.step % display_every == 0 or i + 1 == iterations:
                 self._test(verbose=verbose)
@@ -728,7 +729,7 @@ def _train_sgd(self, iterations, display_every, verbose=1):
 
     def _train_tensorflow_compat_v1_scipy(self, display_every, verbose=1):
         def loss_callback(loss_train, loss_test, *args):
-            self.train_state.epoch += 1
+            self.train_state.iteration += 1
             self.train_state.step += 1
             if self.train_state.step % display_every == 0:
                 self.train_state.loss_train = loss_train
@@ -749,7 +750,7 @@ def loss_callback(loss_train, loss_test, *args):
                         cb.epochs_since_last = 0
 
                         print(
-                            cb.model.train_state.epoch,
+                            cb.model.train_state.iteration,
                             list_to_str(
                                 [float(arg) for arg in args],
                                 precision=cb.precision,
@@ -792,7 +793,7 @@ def _train_tensorflow_tfp(self, verbose=1):
                 self.train_state.train_aux_vars,
             )
             n_iter += results.num_iterations.numpy()
-            self.train_state.epoch += results.num_iterations.numpy()
+            self.train_state.iteration += results.num_iterations.numpy()
             self.train_state.step += results.num_iterations.numpy()
             self._test(verbose=verbose)
 
@@ -819,7 +820,7 @@ def _train_pytorch_lbfgs(self, verbose=1):
                 # Converged
                 break
 
-            self.train_state.epoch += n_iter - prev_n_iter
+            self.train_state.iteration += n_iter - prev_n_iter
             self.train_state.step += n_iter - prev_n_iter
             prev_n_iter = n_iter
             self._test(verbose=verbose)
@@ -851,7 +852,7 @@ def _train_paddle_lbfgs(self, verbose=1):
                 # Converged
                 break
 
-            self.train_state.epoch += n_iter - prev_n_iter
+            self.train_state.iteration += n_iter - prev_n_iter
             self.train_state.step += n_iter - prev_n_iter
             prev_n_iter = n_iter
             self._test(verbose=verbose)
@@ -1071,7 +1072,7 @@ def save(self, save_path, protocol="backend", verbose=0):
         Returns:
             string: Path where model is saved.
         """
-        save_path = f"{save_path}-{self.train_state.epoch}"
+        save_path = f"{save_path}-{self.train_state.iteration}"
         if protocol == "pickle":
             save_path += ".pkl"
             with open(save_path, "wb") as f:
@@ -1104,7 +1105,7 @@ def save(self, save_path, protocol="backend", verbose=0):
         if verbose > 0:
             print(
                 "Epoch {}: saving model to {} ...\n".format(
-                    self.train_state.epoch, save_path
+                    self.train_state.iteration, save_path
                 )
             )
         return save_path
@@ -1159,7 +1160,7 @@ def print_model(self):
 
 class TrainState:
     def __init__(self):
-        self.epoch = 0
+        self.iteration = 0
         self.step = 0
 
         # Current data
@@ -1188,6 +1189,15 @@ def __init__(self):
         self.best_ystd = None
         self.best_metrics = None
 
+    @property
+    def epoch(self):
+        warnings.warn(
+            "TrainState.epoch is deprecated and will be removed in a future version. Use TrainState.iteration instead.",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        return self.iteration
+
     def set_data_train(self, X_train, y_train, train_aux_vars=None):
         self.X_train = X_train
         self.y_train = y_train

docs/demos/pinn_forward/elasticity.plate.rst

@@ -257,7 +257,7 @@ We then train the model for 5000 iterations:
 
 .. code-block:: python
 
-    losshistory, train_state = model.train(epochs=5000)
+    losshistory, train_state = model.train(iterations=5000)
 
 Complete code
 --------------

docs/demos/pinn_forward/helmholtz.2d.neumann.hole.rst

@@ -54,7 +54,7 @@ First, the DeepXDE, Numpy and Matplotlib modules are imported:
   import matplotlib.pyplot as plt
   import numpy as np
 
-We begin by defining the general parameters for the problem. We use a collocation points density of 15 (resp. 30) points per wavelength for the training (resp. testing) data along each direction. The PINN will be trained over 5000 epochs. We define the learning rate, the number of dense layers and nodes, and the activation function.
+We begin by defining the general parameters for the problem. We use a collocation points density of 15 (resp. 30) points per wavelength for the training (resp. testing) data along each direction. The PINN will be trained over 5000 iterations. We define the learning rate, the number of dense layers and nodes, and the activation function.
 
 .. code-block:: python