epochs -> iterations in examples

examples/operator/advection_aligned_pideeponet.py

@@ -74,7 +74,7 @@ def periodic(x):
 
 model = dde.Model(data, net)
 model.compile("adam", lr=0.0005)
-losshistory, train_state = model.train(epochs=50000)
+losshistory, train_state = model.train(iterations=50000)
 dde.utils.plot_loss_history(losshistory)
 
 x = np.linspace(0, 1, num=100)

examples/operator/advection_aligned_pideeponet_2d.py

@@ -79,7 +79,7 @@ def periodic(x):
 
 model = dde.Model(data, net)
 model.compile("adam", lr=0.0005)
-losshistory, train_state = model.train(epochs=30000)
+losshistory, train_state = model.train(iterations=30000)
 dde.utils.plot_loss_history(losshistory)
 
 x = np.linspace(0, 1, num=100)

examples/operator/advection_unaligned_pideeponet.py

@@ -74,7 +74,7 @@ def periodic(x):
 
 model = dde.Model(data, net)
 model.compile("adam", lr=0.0005)
-losshistory, train_state = model.train(epochs=50000)
+losshistory, train_state = model.train(iterations=50000)
 dde.utils.plot_loss_history(losshistory)
 
 x = np.linspace(0, 1, num=100)

examples/operator/advection_unaligned_pideeponet_2d.py

@@ -77,7 +77,7 @@ def periodic(x):
 
 model = dde.Model(data, net)
 model.compile("adam", lr=0.0005)
-losshistory, train_state = model.train(epochs=10000)
+losshistory, train_state = model.train(iterations=10000)
 dde.utils.plot_loss_history(losshistory)
 
 x = np.linspace(0, 1, num=100)

examples/operator/antiderivative_aligned_pideeponet.py

@@ -57,7 +57,7 @@ def zero_ic(inputs, outputs):
 
 model = dde.Model(data, net)
 model.compile("adam", lr=0.0005)
-losshistory, train_state = model.train(epochs=40000)
+losshistory, train_state = model.train(iterations=40000)
 
 dde.utils.plot_loss_history(losshistory)
 

examples/operator/antiderivative_unaligned_pideeponet.py

@@ -40,7 +40,7 @@ def zero_ic(inputs, outputs):
 
 model = dde.Model(data, net)
 model.compile("adam", lr=0.0005)
-losshistory, train_state = model.train(epochs=40000)
+losshistory, train_state = model.train(iterations=40000)
 
 dde.utils.plot_loss_history(losshistory)
 

examples/operator/diff_rec_aligned_pideeponet.py

@@ -51,7 +51,7 @@ def pde(x, y, v):
 
 model = dde.Model(data, net)
 model.compile("adam", lr=0.0005)
-losshistory, train_state = model.train(epochs=20000)
+losshistory, train_state = model.train(iterations=20000)
 dde.utils.plot_loss_history(losshistory)
 
 func_feats = func_space.random(1)

examples/operator/diff_rec_unaligned_pideeponet.py

@@ -51,7 +51,7 @@ def pde(x, y, v):
 
 model = dde.Model(data, net)
 model.compile("adam", lr=0.0005)
-losshistory, train_state = model.train(epochs=50000)
+losshistory, train_state = model.train(iterations=50000)
 dde.utils.plot_loss_history(losshistory)
 
 func_feats = func_space.random(1)

examples/pinn_forward/Allen_Cahn.py

@@ -55,7 +55,7 @@ def output_transform(x, y):
 model = dde.Model(data, net)
 
 model.compile("adam", lr=1e-3)
-model.train(epochs=40000)
+model.train(iterations=40000)
 model.compile("L-BFGS")
 losshistory, train_state = model.train()
 dde.saveplot(losshistory, train_state, issave=True, isplot=True)

examples/pinn_forward/Beltrami_flow.py

@@ -166,7 +166,7 @@ def p_func(x):
 model = dde.Model(data, net)
 
 model.compile("adam", lr=1e-3, loss_weights=[1, 1, 1, 1, 100, 100, 100, 100, 100, 100])
-model.train(epochs=30000)
+model.train(iterations=30000)
 model.compile("L-BFGS", loss_weights=[1, 1, 1, 1, 100, 100, 100, 100, 100, 100])
 losshistory, train_state = model.train()
 

examples/pinn_forward/Burgers_RAR.py

@@ -52,7 +52,7 @@ def pde(x, y):
     data.add_anchors(X[x_id])
     early_stopping = dde.callbacks.EarlyStopping(min_delta=1e-4, patience=2000)
     model.compile("adam", lr=1e-3)
-    model.train(epochs=10000, disregard_previous_best=True, callbacks=[early_stopping])
+    model.train(iterations=10000, disregard_previous_best=True, callbacks=[early_stopping])
     model.compile("L-BFGS")
     losshistory, train_state = model.train()
 dde.saveplot(losshistory, train_state, issave=True, isplot=True)

examples/pinn_forward/Helmholtz_Dirichlet_2d_HPO.py

@@ -29,7 +29,7 @@
 k0 = 2 * np.pi * n
 precision_train = 10
 precision_test = 30
-epochs = 10000
+iterations = 10000
 
 
 def pde(x, y):
@@ -89,7 +89,7 @@ def create_model(config):
 
 
 def train_model(model, config):
-    losshistory, train_state = model.train(epochs=epochs)
+    losshistory, train_state = model.train(iterations=iterations)
     train = np.array(losshistory.loss_train).sum(axis=1).ravel()
     test = np.array(losshistory.loss_test).sum(axis=1).ravel()
     metric = np.array(losshistory.metrics_test).sum(axis=1).ravel()

examples/pinn_forward/Helmholtz_Sound_hard_ABC_2d.py

@@ -6,7 +6,7 @@
 
 # General parameters
 weights = 1
-epochs = 10000
+iterations = 10000
 learning_rate = 1e-3
 num_dense_layers = 3
 num_dense_nodes = 350
@@ -133,6 +133,6 @@ def func1_outer(x, y):
 model.compile(
     "adam", lr=learning_rate, loss_weights=loss_weights, metrics=["l2 relative error"]
 )
-losshistory, train_state = model.train(epochs=epochs)
+losshistory, train_state = model.train(iterations=iterations)
 
 dde.saveplot(losshistory, train_state, issave=True, isplot=True)

examples/pinn_forward/heat_resample.py

@@ -97,7 +97,7 @@ def pde(x, y):
 
 # Build and train the model:
 model.compile("adam", lr=1e-3)
-model.train(epochs=200000, callbacks=[pde_resampler])
+model.train(iterations=200000, callbacks=[pde_resampler])
 model.compile("L-BFGS")
 losshistory, train_state = model.train(callbacks=[pde_resampler])
 

examples/pinn_inverse/Navier_Stokes_inverse.py

@@ -127,7 +127,7 @@ def Navier_Stokes_Equation(x, y):
 dde.saveplot(loss_history, train_state, issave=True, isplot=True)
 model.compile("adam", lr=1e-4, external_trainable_variables=[C1, C2])
 loss_history, train_state = model.train(
-    epochs=10000, callbacks=[variable], display_every=1000, disregard_previous_best=True
+    iterations=10000, callbacks=[variable], display_every=1000, disregard_previous_best=True
 )
 dde.saveplot(loss_history, train_state, issave=True, isplot=True)
 # model.save(save_path = "./NS_inverse_model/model")

examples/pinn_inverse/elliptic_inverse_field_batch.py

@@ -1,4 +1,4 @@
-"""Backend supported: tensorflow.compat.v1, pytorch, paddle"""
+"""Backend supported: pytorch, paddle"""
 import deepxde as dde
 import matplotlib.pyplot as plt
 import numpy as np
@@ -42,7 +42,7 @@ def sol(x):
 
 model = dde.Model(data, net)
 model.compile("adam", lr=0.0001, loss_weights=[1, 100, 1000])
-losshistory, train_state = model.train(epochs=20000, callbacks=[pde_resampler])
+losshistory, train_state = model.train(iterations=20000, callbacks=[pde_resampler])
 dde.saveplot(losshistory, train_state, issave=True, isplot=True)
 
 # view results