diff --git a/EIVPackage/EIVArchitectures/Networks.py b/EIVPackage/EIVArchitectures/Networks.py
index d9c0f1ba854549882ba22499232e8d33e1619334..22f48bb5b5b6f2a800c1b2066c2a951c58816963 100644
--- a/EIVPackage/EIVArchitectures/Networks.py
+++ b/EIVPackage/EIVArchitectures/Networks.py
@@ -32,7 +32,7 @@ class FNNEIV(nn.Module):
**Note**:
- To change the deming factor afterwards, use the method `change_deming`
- To change fixed_std_x afterwards, use the method `change_fixed_std_x`
- - To change std_y use the method `change_std_x`
+ - To change std_y use the method `change_std_y`
"""
LeakyReLUSlope = 1e-2
def __init__(self, p = 0.2, init_std_y=1.0, precision_prior_zeta=0.0,
@@ -349,6 +349,7 @@ class FNNBer(nn.Module):
:param h: A list specifying the number of neurons in each layer.
:param std_y_requires_grad: Whether `sigma_y` will require_grad and thus
be updated during optimization. Defaults to False.
+ To change std_y use the method `change_std_y`
"""
LeakyReLUSlope = 1e-2
def __init__(self, p=0.2, init_std_y=1.0, h=[10, 1024,1024,1024,1024, 1],
diff --git a/EIVPackage/EIVTrainingRoutines/train_and_store.py b/EIVPackage/EIVTrainingRoutines/train_and_store.py
index eb0378e953c00eee2bb0465b1aea8580b92baa01..908c4457baad75de7fbe40c66716ae3ebe7f5c7c 100644
--- a/EIVPackage/EIVTrainingRoutines/train_and_store.py
+++ b/EIVPackage/EIVTrainingRoutines/train_and_store.py
@@ -69,6 +69,15 @@ class TrainEpoch():
"""
pass
+
+ def post_train_update(self, net, epoch=None):
+ """
+ Will be executed after the training is finished
+ :param net: The current net, a torch.nn.Module
+ :param epoch: Tue last epochn number, an integer.
+ """
+ pass
+
def extra_report(self, net, step):
"""
Overwrite for reporting on state of net
@@ -156,6 +165,8 @@ def train_and_store(net, epoch_map, number_of_epochs, save_file, **kwargs):
Calls `epoch_map` with `epoch` and the current epoch number
`number_of_epochs` times and stores a list of
its output as a pickled file under `save_file`.
+ After this (the training) is done, `epoch_map.post_train_update(net,
+ epoch_number)` is called, if existent.
**Note**: The output of `epoch_map` is supposed to
consist of 4 specific arguments, see below.
:param net: A torch.nn.Module
@@ -179,6 +190,11 @@ def train_and_store(net, epoch_map, number_of_epochs, save_file, **kwargs):
test_loss_collection.append(test_loss)
std_x_collection.append(std_x)
std_y_collection.append(std_y)
+ try:
+ epoch_map.post_train_update(net, number_of_epochs-1)
+ print('Training done. Performed post training updating.')
+ except AttributeError:
+ print('Training done. No post train updating performed.')
# Saving
state_dict = net.state_dict()
to_save = {
diff --git a/Experiments/train_eiv.py b/Experiments/train_eiv.py
index 2ab2dce7f0664e2ed7c258ceca16531a3c8faff0..eb8d9d88292dd63130b38bc78db4eb1ed1db9014 100644
--- a/Experiments/train_eiv.py
+++ b/Experiments/train_eiv.py
@@ -85,48 +85,71 @@ class UpdatedTrainEpoch(train_and_store.TrainEpoch):
self.lr_scheduler = torch.optim.lr_scheduler.StepLR(
self.optimizer, lr_update, gamma)
+ def update_std_y(self, net):
+ """
+ Update the std_y of `net` via the RMSE of the prediction.
+ """
+ net_train_state = net.training
+ net_noise_state = net.noise_is_on
+ net.train()
+ net.noise_on()
+ pred_collection = []
+ y_collection = []
+ for i, (x,y) in enumerate(self.train_dataloader):
+ if i >= eiv_prediction_number_of_batches:
+ break
+ if len(y.shape) <= 1:
+ y = y.view((-1,1))
+ x,y = x.to(device), y.to(device)
+ pred, _ = net.predict(x,
+ number_of_draws=eiv_prediction_number_of_draws,
+ remove_graph = True,
+ take_average_of_prediction=True)
+ pred_collection.append(pred)
+ y_collection.append(y)
+ pred_collection = torch.cat(pred_collection, dim=0)
+ y_collection = torch.cat(y_collection, dim=0)
+ assert pred_collection.shape == y_collection.shape
+ rmse = torch.sqrt(torch.mean((pred_collection - y_collection)**2))
+ net.change_std_y(rmse)
+ if not net_train_state:
+ net.eval()
+ if not net_noise_state:
+ net.noise_off()
+
+ def check_if_update_std_y(self, epoch):
+ """
+ Check whether to update std_y according to `epoch_number` and
+ `std_y_update_points`. If the later is an integer, after all epochs
+ greater than this number an update will be made (i.e. `True` will
+ be returned). If it is a list, only `epoch_number` greater than
+ `std_y_update_points[0]` that divide `std_y_update_points[1]` will
+ result in a True.
+ """
+ if type(std_y_update_points) is int:
+ return epoch >= std_y_update_points
+ else:
+ assert type(std_y_update_points) is list
+ return epoch >= std_y_update_points[0]\
+ and epoch % std_y_update_points[1] == 0
def post_epoch_update(self, net, epoch):
"""
Overwrites the corresponding method
"""
- def update_std_y(epoch_number):
- """
- Check whether to update std_y according to `epoch_number` and
- `std_y_update_points`. If the later is an integer, after all epochs
- greater than this number an update will be made (i.e. `True` will
- be returned). If it is a list, only `epoch_number` greater than
- `std_y_update_points[0]` that divide `std_y_update_points[1]` will
- result in a True.
- """
- if type(std_y_update_points) is int:
- return epoch >= std_y_update_points
- else:
- assert type(std_y_update_points) is list
- return epoch_number >= std_y_update_points[0]\
- and epoch_number % std_y_update_points[1] == 0
- if update_std_y(epoch):
- pred_collection = []
- y_collection = []
- for i, (x,y) in enumerate(self.train_dataloader):
- if i >= eiv_prediction_number_of_batches:
- break
- if len(y.shape) <= 1:
- y = y.view((-1,1))
- x,y = x.to(device), y.to(device)
- pred, _ = net.predict(x,
- number_of_draws=eiv_prediction_number_of_draws,
- remove_graph = True,
- take_average_of_prediction=True)
- pred_collection.append(pred)
- y_collection.append(y)
- pred_collection = torch.cat(pred_collection, dim=0)
- y_collection = torch.cat(y_collection, dim=0)
- assert pred_collection.shape == y_collection.shape
- rmse = torch.sqrt(torch.mean((pred_collection - y_collection)**2))
- net.change_std_y(rmse)
+ if self.check_if_update_std_y(epoch):
+ self.update_std_y(net)
self.lr_scheduler.step()
+ def post_train_update(self, net, epoch):
+ """
+ Overwrites the corresponding method. If std_y of `net` was not updated
+ in the last training step, update it when finished with training.
+ `epoch` should be the number of the last training epoch.
+ """
+ if not self.check_if_update_std_y(epoch):
+ self.update_std_y(net)
+
def extra_report(self, net, i):
"""
Overwrites the corresponding method
diff --git a/Experiments/train_noneiv.py b/Experiments/train_noneiv.py
index 1f27ceb4505b818ec5c28353776d16c24fd1e879..316dda1e8f5fbf42f46ae9135dadd5c803623df8 100644
--- a/Experiments/train_noneiv.py
+++ b/Experiments/train_noneiv.py
@@ -84,48 +84,67 @@ class UpdatedTrainEpoch(train_and_store.TrainEpoch):
self.lr_scheduler = torch.optim.lr_scheduler.StepLR(
self.optimizer, lr_update, gamma)
+ def update_std_y(self, net):
+ """
+ Update the std_y of `net` via the RMSE of the prediction.
+ """
+ net_train_state = net.training
+ net.train()
+ pred_collection = []
+ y_collection = []
+ for i, (x,y) in enumerate(self.train_dataloader):
+ if i >= noneiv_prediction_number_of_batches:
+ break
+ if len(y.shape) <= 1:
+ y = y.view((-1,1))
+ x,y = x.to(device), y.to(device)
+ pred, _ = net.predict(x,
+ number_of_draws=noneiv_prediction_number_of_draws,
+ remove_graph = True,
+ take_average_of_prediction=True)
+ pred_collection.append(pred)
+ y_collection.append(y)
+ pred_collection = torch.cat(pred_collection, dim=0)
+ y_collection = torch.cat(y_collection, dim=0)
+ assert pred_collection.shape == y_collection.shape
+ rmse = torch.sqrt(torch.mean((pred_collection - y_collection)**2))
+ net.change_std_y(rmse)
+ if not net_train_state:
+ net.eval()
+
+ def check_if_update_std_y(self, epoch):
+ """
+ Check whether to update std_y according to `epoch_number` and
+ `std_y_update_points`. If the later is an integer, after all epochs
+ greater than this number an update will be made (i.e. `True` will
+ be returned). If it is a list, only `epoch_number` greater than
+ `std_y_update_points[0]` that divide `std_y_update_points[1]` will
+ result in a True.
+ """
+ if type(std_y_update_points) is int:
+ return epoch >= std_y_update_points
+ else:
+ assert type(std_y_update_points) is list
+ return epoch >= std_y_update_points[0]\
+ and epoch % std_y_update_points[1] == 0
def post_epoch_update(self, net, epoch):
"""
Overwrites the corresponding method
"""
- def update_std_y(epoch_number):
- """
- Check whether to update std_y according to `epoch_number` and
- `std_y_update_points`. If the later is an integer, after all epochs
- greater than this number an update will be made (i.e. `True` will
- be returned). If it is a list, only `epoch_number` greater than
- `std_y_update_points[0]` that divide `std_y_update_points[1]` will
- result in a True.
- """
- if type(std_y_update_points) is int:
- return epoch >= std_y_update_points
- else:
- assert type(std_y_update_points) is list
- return epoch_number >= std_y_update_points[0]\
- and epoch_number % std_y_update_points[1] == 0
- if update_std_y(epoch):
- pred_collection = []
- y_collection = []
- for i, (x,y) in enumerate(self.train_dataloader):
- if i >= noneiv_prediction_number_of_batches:
- break
- if len(y.shape) <= 1:
- y = y.view((-1,1))
- x,y = x.to(device), y.to(device)
- pred, _ = net.predict(x,
- number_of_draws=noneiv_prediction_number_of_draws,
- remove_graph = True,
- take_average_of_prediction=True)
- pred_collection.append(pred)
- y_collection.append(y)
- pred_collection = torch.cat(pred_collection, dim=0)
- y_collection = torch.cat(y_collection, dim=0)
- assert pred_collection.shape == y_collection.shape
- rmse = torch.sqrt(torch.mean((pred_collection - y_collection)**2))
- net.change_std_y(rmse)
+ if self.check_if_update_std_y(epoch):
+ self.update_std_y(net)
self.lr_scheduler.step()
+ def post_train_update(self, net, epoch):
+ """
+ Overwrites the corresponding method. If std_y of `net` was not updated
+ in the last training step, update it when finished with training.
+ `epoch` should be the number of the last training epoch.
+ """
+ if not self.check_if_update_std_y(epoch):
+ self.update_std_y(net)
+
def extra_report(self, net, i):
"""
Overwrites the corresponding method