modify tutorials for plotter compatibility

docs/source/_rst/tutorials/tutorial2/tutorial.rst

@@ -115,28 +115,26 @@ These parameters can be modified as desired. We use the
 
 .. parsed-literal::
 
-    /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:546: UserWarning: Can't initialize NVML
-      warnings.warn("Can't initialize NVML")
-    /u/d/dcoscia/.local/lib/python3.9/site-packages/torch/cuda/__init__.py:651: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)
-      return torch._C._cuda_getDeviceCount() if nvml_count < 0 else nvml_count
     GPU available: False, used: False
     TPU available: False, using: 0 TPU cores
     IPU available: False, using: 0 IPUs
     HPU available: False, using: 0 HPUs
-    Missing logger folder: /u/d/dcoscia/PINA/tutorials/tutorial2/lightning_logs
-
 
 
 .. parsed-literal::
 
-    Training: 0it [00:00, ?it/s]
-
+    Epoch 999: : 1it [00:00, 152.98it/s, v_num=9, mean_loss=0.000239, D_loss=0.000793, gamma1_loss=8.51e-5, gamma2_loss=0.000103, gamma3_loss=0.000122, gamma4_loss=9.14e-5]  
 
 .. parsed-literal::
 
     `Trainer.fit` stopped: `max_epochs=1000` reached.
 
 
+.. parsed-literal::
+
+    Epoch 999: : 1it [00:00, 119.21it/s, v_num=9, mean_loss=0.000239, D_loss=0.000793, gamma1_loss=8.51e-5, gamma2_loss=0.000103, gamma3_loss=0.000122, gamma4_loss=9.14e-5]
+
+
 Now the ``Plotter`` class is used to plot the results. The solution
 predicted by the neural network is plotted on the left, the exact one is
 represented at the center and on the right the error between the exact
@@ -145,7 +143,7 @@ and the predicted solutions is showed.
 .. code:: ipython3
 
     plotter = Plotter()
-    plotter.plot(trainer)
+    plotter.plot(solver=pinn)
 
 
 
@@ -214,17 +212,20 @@ new extra feature.
     HPU available: False, using: 0 HPUs
 
 
-
 .. parsed-literal::
 
-    Training: 0it [00:00, ?it/s]
-
+    Epoch 999: : 1it [00:00, 119.36it/s, v_num=10, mean_loss=8.97e-7, D_loss=4.43e-6, gamma1_loss=1.37e-8, gamma2_loss=1.68e-8, gamma3_loss=1.22e-8, gamma4_loss=1.77e-8]     
 
 .. parsed-literal::
 
     `Trainer.fit` stopped: `max_epochs=1000` reached.
 
 
+.. parsed-literal::
+
+    Epoch 999: : 1it [00:00, 95.23it/s, v_num=10, mean_loss=8.97e-7, D_loss=4.43e-6, gamma1_loss=1.37e-8, gamma2_loss=1.68e-8, gamma3_loss=1.22e-8, gamma4_loss=1.77e-8] 
+
+
 The predicted and exact solutions and the error between them are
 represented below. We can easily note that now our network, having
 almost the same condition as before, is able to reach additional order
@@ -232,7 +233,7 @@ of magnitudes in accuracy.
 
 .. code:: ipython3
 
-    plotter.plot(trainer_feat)
+    plotter.plot(solver=pinn_feat)
 
 
 
@@ -297,17 +298,20 @@ need, and they are managed by ``autograd`` module!
     HPU available: False, using: 0 HPUs
 
 
-
 .. parsed-literal::
 
-    Training: 0it [00:00, ?it/s]
-
+    Epoch 999: : 1it [00:00, 103.14it/s, v_num=14, mean_loss=1.39e-6, D_loss=6.04e-6, gamma1_loss=4.19e-7, gamma2_loss=2.8e-8, gamma3_loss=4.05e-7, gamma4_loss=3.49e-8]    
 
 .. parsed-literal::
 
     `Trainer.fit` stopped: `max_epochs=1000` reached.
 
 
+.. parsed-literal::
+
+    Epoch 999: : 1it [00:00, 84.50it/s, v_num=14, mean_loss=1.39e-6, D_loss=6.04e-6, gamma1_loss=4.19e-7, gamma2_loss=2.8e-8, gamma3_loss=4.05e-7, gamma4_loss=3.49e-8] 
+
+
 Umh, the final loss is not appreciabily better than previous model (with
 static extra features), despite the usage of learnable parameters. This
 is mainly due to the over-parametrization of the network: there are many
@@ -328,7 +332,7 @@ removing all the hidden layers in the ``FeedForward``, keeping only the
         output_dimensions=len(problem.output_variables),
         input_dimensions=len(problem.input_variables)+1
     )
-    pinn_learn = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.006, 'weight_decay':1e-8})
+    pinn_learn = PINN(problem, model_lean, extra_features=[SinSinAB()], optimizer_kwargs={'lr':0.01, 'weight_decay':1e-8})
     trainer_learn = Trainer(pinn_learn, max_epochs=1000, callbacks=[MetricTracker()], accelerator='cpu', enable_model_summary=False) # we train on CPU and avoid model summary at beginning of training (optional)
     
     # train
@@ -343,17 +347,20 @@ removing all the hidden layers in the ``FeedForward``, keeping only the
     HPU available: False, using: 0 HPUs
 
 
-
 .. parsed-literal::
 
-    Training: 0it [00:00, ?it/s]
-
+    Epoch 999: : 1it [00:00, 130.55it/s, v_num=17, mean_loss=1.34e-14, D_loss=6.7e-14, gamma1_loss=5.13e-17, gamma2_loss=9.68e-18, gamma3_loss=5.14e-17, gamma4_loss=9.75e-18] 
 
 .. parsed-literal::
 
     `Trainer.fit` stopped: `max_epochs=1000` reached.
 
 
+.. parsed-literal::
+
+    Epoch 999: : 1it [00:00, 104.91it/s, v_num=17, mean_loss=1.34e-14, D_loss=6.7e-14, gamma1_loss=5.13e-17, gamma2_loss=9.68e-18, gamma3_loss=5.14e-17, gamma4_loss=9.75e-18]
+
+
 In such a way, the model is able to reach a very high accuracy! Of
 course, this is a toy problem for understanding the usage of extra
 features: similar precision could be obtained if the extra features are
@@ -368,7 +375,7 @@ features.
 
 .. code:: ipython3
 
-    plotter.plot(trainer_learn)
+    plotter.plot(solver=pinn_learn)
 
 
 
@@ -379,9 +386,9 @@ Let us compare the training losses for the various types of training
 
 .. code:: ipython3
 
-    plotter.plot_loss(trainer, label='Standard')
-    plotter.plot_loss(trainer_feat, label='Static Features')
-    plotter.plot_loss(trainer_learn, label='Learnable Features')
+    plotter.plot_loss(trainer, logy=True, label='Standard')
+    plotter.plot_loss(trainer_feat, logy=True,label='Static Features')
+    plotter.plot_loss(trainer_learn, logy=True, label='Learnable Features')