Skip to content
GitLab
Explore
Sign in
Primary navigation
Search or go to…
Project
T
Troubled Cell Detection
Manage
Activity
Members
Labels
Plan
Issues
Issue boards
Milestones
Wiki
Requirements
Code
Merge requests
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Snippets
Locked files
Build
Pipelines
Jobs
Pipeline schedules
Test cases
Artifacts
Deploy
Releases
Package registry
Container registry
Model registry
Operate
Environments
Terraform modules
Monitor
Incidents
Analyze
Value stream analytics
Contributor analytics
CI/CD analytics
Repository analytics
Code review analytics
Issue analytics
Insights
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
GitLab community forum
Contribute to GitLab
Provide feedback
Keyboard shortcuts
?
Snippets
Groups
Projects
Show more breadcrumbs
Laura Christine Kühle
Troubled Cell Detection
Commits
87920cbb
Commit
87920cbb
authored
Aug 20, 2021
by
Laura Christine Kühle
Browse files
Options
Downloads
Patches
Plain Diff
Removed unnecessary comments.
parent
1c790d43
No related branches found
No related tags found
No related merge requests found
Changes
1
Show whitespace changes
Inline
Side-by-side
Showing
1 changed file
ANN_Training.py
+10
-72
10 additions, 72 deletions
ANN_Training.py
with
10 additions
and
72 deletions
ANN_Training.py
+
10
−
72
View file @
87920cbb
...
@@ -5,9 +5,10 @@
...
@@ -5,9 +5,10 @@
TODO: Improve
'
epoch_training()
'
TODO: Improve
'
epoch_training()
'
TODO: Add ANN testing from Soraya
TODO: Add ANN testing from Soraya
TODO: Add ANN classification from Soraya
TODO: Add ANN classification from Soraya
TODO: Improve naming of training data (maybe different folders?)
TODO: Improve naming of training data
/model
(maybe different folders?)
TODO: Adjust input file naming to fit training data -> Done
TODO: Adjust input file naming to fit training data -> Done
TODO: Change code to add model directory if not existing -> Done
TODO: Change code to add model directory if not existing -> Done
TODO: Remove unnecessary comments -> Done
"""
"""
import
numpy
as
np
import
numpy
as
np
...
@@ -67,72 +68,23 @@ class ModelTrainer(object):
...
@@ -67,72 +68,23 @@ class ModelTrainer(object):
@staticmethod
@staticmethod
def
_read_data
(
input_file
,
output_file
):
def
_read_data
(
input_file
,
output_file
):
return
map
(
torch
.
tensor
,
(
np
.
load
(
input_file
),
np
.
load
(
output_file
)))
return
map
(
torch
.
tensor
,
(
np
.
load
(
input_file
),
np
.
load
(
output_file
)))
# return DataLoader(TensorDataset(input_data, output_data), batch_size=self._batch_size, shuffle=True)
def
epoch_training
(
self
):
def
epoch_training
(
self
):
# Get Training/validation Datasets from Saved Files and Map to Torch Tensor and batched-datasets
# Get Training/validation Datasets from Saved Files and Map to Torch Tensor and batched-datasets
# nt_smooth = 24000
x_train
,
y_train
=
self
.
_training_data
[
'
train
'
]
# nt_troubled = 24000
# normalize = 1
# batch = 500
# train_input = "TrainInput"+str(int(nt_smooth / 1000))+"k"+str(int(nt_troubled / 1000))+"k"+"Normalized"+str(
# normalize)+".npy"
# train_output = "TrainOutput"+str(int(nt_smooth / 1000))+"k"+str(int(nt_troubled / 1000))+"k"+"Normalized"+str(
# 0)+".npy"
# x_train = np.load(train_input)
# y_train = np.load(train_output)
[
x_train
,
y_train
]
=
self
.
_training_data
[
'
train
'
]
train_ds
=
TensorDataset
(
x_train
,
y_train
)
train_ds
=
TensorDataset
(
x_train
,
y_train
)
train_dl
=
DataLoader
(
train_ds
,
batch_size
=
self
.
_batch_size
,
shuffle
=
True
)
train_dl
=
DataLoader
(
train_ds
,
batch_size
=
self
.
_batch_size
,
shuffle
=
True
)
#
# nv_smooth = 8000
x_valid
,
y_valid
=
self
.
_training_data
[
'
validation
'
]
# nv_troubled = 8000
#
# valid_input = "ValidInput"+str(int(nv_smooth / 1000))+"k"+str(int(nv_troubled / 1000))+"k"+"Normalized"+str(
# normalize)+".npy"
# valid_output = "ValidOutput"+str(int(nv_smooth / 1000))+"k"+str(int(nv_troubled / 1000))+"k"+"Normalized"+str(
# 0)+".npy"
# x_valid = np.load(valid_input)
# y_valid = np.load(valid_output)
[
x_valid
,
y_valid
]
=
self
.
_training_data
[
'
validation
'
]
valid_ds
=
TensorDataset
(
x_valid
,
y_valid
)
valid_ds
=
TensorDataset
(
x_valid
,
y_valid
)
valid_dl
=
DataLoader
(
valid_ds
,
batch_size
=
self
.
_batch_size
*
2
)
valid_dl
=
DataLoader
(
valid_ds
,
batch_size
=
self
.
_batch_size
*
2
)
# Model using torch.nn.Sequential, another format
# model = torch.nn.Sequential(
# torch.nn.Linear(d_in, h),
# torch.nn.ReLU(),
# torch.nn.Linear(h, d_out),
# )
# d_in is input dimension; h is hidden dimension; d_out is output dimension.
# d_in, h, d_out = 5, 10, 2
# d_in is input dimension; h1 is first hidden dimension;
# h2 is second hidden dimension; d_out is output dimension.
# d_in_1, h1_1, h2_1, d_out_1 = 5, 8, 4, 2
# Defining Model
# model = ThreeLayerNetDifferentNeuronsSoftMax(d_in_1, h1_1, h2_1, d_out_1)
# Define Loss Function and Optimization method
# loss_fn = torch.nn.MSELoss(reduction='sum')
# loss_fn = torch.nn.CrossEntropyLoss()
# loss_fn = torch.nn.BCELoss()
# loss_fn = torch.nn.BCEWithLogitsLoss()
# learning_rate = 1e-2
# optimizer = torch.optim.Adam(self._model.parameters(), lr=learning_rate)
# optimizer = torch.optim.SGD(self._model.parameters(), lr=learning_rate, momentum=0.5)
# Training with Validation
# Training with Validation
# validation_loss = torch.zeros(10)
for
epoch
in
range
(
self
.
_num_epochs
):
for
epoch
in
range
(
self
.
_num_epochs
):
self
.
_model
.
train
()
self
.
_model
.
train
()
for
x_batch
,
y_batch
in
train_dl
:
for
x_batch
,
y_batch
in
train_dl
:
pred
=
self
.
_model
(
x_batch
.
float
())
pred
=
self
.
_model
(
x_batch
.
float
())
loss
=
self
.
_loss_function
(
pred
,
y_batch
.
float
()).
mean
()
loss
=
self
.
_loss_function
(
pred
,
y_batch
.
float
()).
mean
()
# if Loss_fn = torch.nn.CrossEntropyLoss() use below:
# loss = loss_fn(pred, y_batch.long()[:,0]).mean()
# Run back propagation, update the weights, and zero gradients for next epoch
# Run back propagation, update the weights, and zero gradients for next epoch
loss
.
backward
()
loss
.
backward
()
...
@@ -145,10 +97,6 @@ class ModelTrainer(object):
...
@@ -145,10 +97,6 @@ class ModelTrainer(object):
self
.
_loss_function
(
self
.
_model
(
x_batch_valid
.
float
()),
y_batch_valid
.
float
())
self
.
_loss_function
(
self
.
_model
(
x_batch_valid
.
float
()),
y_batch_valid
.
float
())
for
x_batch_valid
,
y_batch_valid
in
valid_dl
)
for
x_batch_valid
,
y_batch_valid
in
valid_dl
)
# if Loss_fn = torch.nn.CrossEntropyLoss() use below:
# valid_loss = sum(loss_fn(model(x_batch_valid.float()), y_batch_valid.long()[:,
# 0]) for x_batch_valid, y_batch_valid in valid_dl)
if
epoch
%
100
==
99
:
if
epoch
%
100
==
99
:
self
.
_validation_loss
[
int
(
epoch
/
99
)
-
1
]
=
valid_loss
/
len
(
valid_dl
)
self
.
_validation_loss
[
int
(
epoch
/
99
)
-
1
]
=
valid_loss
/
len
(
valid_dl
)
print
(
epoch
,
valid_loss
/
len
(
valid_dl
))
print
(
epoch
,
valid_loss
/
len
(
valid_dl
))
...
@@ -174,18 +122,8 @@ class ModelTrainer(object):
...
@@ -174,18 +122,8 @@ class ModelTrainer(object):
pass
pass
# model_config = {'input_size': 5, 'first_hidden_size': 8, 'second_hidden_size': 4, 'output_size': 2,
# Loss Functions: BCELoss, BCEWithLogitsLoss, CrossEntropyLoss (not working), MSELoss (with reduction='sum')
# 'activation_function': 'Softmax', 'activation_config': {'dim': 1}}
# Optimizer: Adam, SGD
# model = ANN_Model.ThreeLayerReLu(model_config)
trainer
=
ModelTrainer
({
'
loss_function
'
:
'
MSELoss
'
,
'
loss_config
'
:
{
'
reduction
'
:
'
sum
'
}})
# learning_rate = 1e-10
trainer
.
epoch_training
()
# config = {'lr': learning_rate}
trainer
.
save_model
()
# # optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# optimizer = torch.optim.Adam(model.parameters(), **config)
# print(optimizer.__dict__)
# optimizer = torch.optim.SGD(model.parameters(), **config)
# print(optimizer.__class__)
# print(optimizer.__dict__)
# loss_fn = torch.nn.BCEWithLogitsLoss()
# print(loss_fn.__class__)
# trainer = ModelTrainer({})
# trainer.save_model()
This diff is collapsed.
Click to expand it.
Preview
0%
Loading
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Save comment
Cancel
Please
register
or
sign in
to comment