Skip to content
GitLab
Explore
Sign in
Primary navigation
Search or go to…
Project
I
Image classification
Manage
Activity
Members
Labels
Plan
Issues
Issue boards
Milestones
Wiki
Code
Merge requests
Repository
Branches
Commits
Tags
Repository graph
Compare revisions
Snippets
Deploy
Releases
Package registry
Model registry
Operate
Terraform modules
Monitor
Incidents
Analyze
Value stream analytics
Contributor analytics
Repository analytics
Model experiments
Help
Help
Support
GitLab documentation
Compare GitLab plans
Community forum
Contribute to GitLab
Provide feedback
Terms and privacy
Keyboard shortcuts
?
Snippets
Groups
Projects
Show more breadcrumbs
Brudy Saintespes Baptiste
Image classification
Commits
02e604a2
Commit
02e604a2
authored
1 year ago
by
BaptisteBrd
Browse files
Options
Downloads
Patches
Plain Diff
mlp final
parent
abf8537c
Branches
Branches containing commit
No related tags found
No related merge requests found
Changes
1
Show whitespace changes
Inline
Side-by-side
Showing
1 changed file
mlp.py
+150
-0
150 additions, 0 deletions
mlp.py
with
150 additions
and
0 deletions
mlp.py
+
150
−
0
View file @
02e604a2
import
numpy
as
np
import
matplotlib.pyplot
as
plt
import
scipy.special
as
sp
from
tqdm
import
tqdm
import
read_cifar
def
learn_once_mse
(
w1
,
b1
,
w2
,
b2
,
data
,
targets
,
learning_rate
):
"""
Perform one iteration of training using Mean Squared Error (MSE) loss.
"""
# Forward propagation
a0
=
data
# Input layer receives the data
z1
=
np
.
matmul
(
a0
,
w1
)
+
b1
# Compute hidden layer input
a1
=
1
/
(
1
+
np
.
exp
(
-
z1
))
# Apply sigmoid activation in hidden layer
z2
=
np
.
matmul
(
a1
,
w2
)
+
b2
# Compute output layer input
a2
=
1
/
(
1
+
np
.
exp
(
-
z2
))
# Apply sigmoid activation in output layer
predictions
=
a2
# Final predictions from the network
# Calculate MSE loss
loss
=
np
.
mean
(
np
.
square
(
predictions
-
targets
))
# Backward propagation to compute gradients
grad_a2
=
2
*
(
predictions
-
targets
)
grad_z2
=
grad_a2
*
a2
*
(
1
-
a2
)
grad_w2
=
np
.
matmul
(
a1
.
T
,
grad_z2
)
grad_b2
=
np
.
sum
(
grad_z2
,
axis
=
0
)
grad_a1
=
np
.
matmul
(
grad_z2
,
w2
.
T
)
grad_z1
=
grad_a1
*
a1
*
(
1
-
a1
)
grad_w1
=
np
.
matmul
(
a0
.
T
,
grad_z1
)
grad_b1
=
np
.
sum
(
grad_z1
,
axis
=
0
)
# Update the network parameters
w1
-=
learning_rate
*
grad_w1
w2
-=
learning_rate
*
grad_w2
b1
-=
learning_rate
*
grad_b1
b2
-=
learning_rate
*
grad_b2
return
w1
,
b1
,
w2
,
b2
,
loss
def
one_hot
(
labels
):
"""
Convert labels to one-hot encoded format.
"""
if
isinstance
(
labels
,
np
.
int64
):
labels
=
np
.
array
([
labels
])
one_hot_matrix
=
np
.
zeros
((
len
(
labels
),
10
))
one_hot_matrix
[
np
.
arange
(
len
(
labels
)),
labels
]
=
1
return
one_hot_matrix
def
learn_once_cross_entropy
(
w1
,
b1
,
w2
,
b2
,
data
,
targets
,
learning_rate
,
batch_size
):
"""
Perform one iteration of training using Cross-Entropy loss.
"""
# Forward propagation
a0
=
data
z1
=
np
.
matmul
(
a0
,
w1
)
+
b1
a1
=
1
/
(
1
+
np
.
exp
(
-
z1
))
z2
=
np
.
matmul
(
a1
,
w2
)
+
b2
a2
=
sp
.
softmax
(
z2
,
axis
=
1
)
predictions
=
a2
# Convert targets to one-hot format and calculate accuracy
pred_labels
=
a2
.
argmax
(
axis
=
1
)
correct_predictions
=
np
.
sum
(
pred_labels
==
targets
)
targets_one_hot
=
one_hot
(
targets
)
# Compute Cross-Entropy loss
loss
=
-
np
.
sum
(
targets_one_hot
*
np
.
log
(
predictions
+
1e-8
))
/
batch_size
grad_z2
=
(
predictions
-
targets_one_hot
)
/
batch_size
grad_w2
=
np
.
matmul
(
a1
.
T
,
grad_z2
)
grad_b2
=
np
.
sum
(
grad_z2
,
axis
=
0
)
grad_a1
=
np
.
matmul
(
grad_z2
,
w2
.
T
)
grad_z1
=
grad_a1
*
a1
*
(
1
-
a1
)
a0
=
a0
.
reshape
(
-
1
,
batch_size
)
grad_w1
=
np
.
matmul
(
a0
,
grad_z1
)
grad_b1
=
np
.
sum
(
grad_z1
,
axis
=
0
)
# Update weights and biases
w1
-=
learning_rate
*
grad_w1
w2
-=
learning_rate
*
grad_w2
b1
-=
learning_rate
*
grad_b1
b2
-=
learning_rate
*
grad_b2
accuracy
=
correct_predictions
/
len
(
pred_labels
)
return
w1
,
b1
,
w2
,
b2
,
accuracy
def
train_mlp
(
w1
,
b1
,
w2
,
b2
,
data_train
,
labels_train
,
learning_rate
=
0.01
,
nb_epochs
=
100
,
batch_size
=
1
):
training_accuracies
=
[]
for
epoch
in
range
(
nb_epochs
):
batch_accuracies
=
[]
batch_count
=
len
(
data_train
)
//
batch_size
for
i
in
tqdm
(
range
(
batch_count
)):
batch_start
,
batch_end
=
i
*
batch_size
,
(
i
+
1
)
*
batch_size
w1
,
b1
,
w2
,
b2
,
acc
=
learn_once_cross_entropy
(
w1
,
b1
,
w2
,
b2
,
data_train
[
batch_start
:
batch_end
],
labels_train
[
batch_start
:
batch_end
],
learning_rate
,
batch_size
)
batch_accuracies
.
append
(
acc
)
# Handling remaining data if total data is not a multiple of batch size
if
len
(
data_train
)
%
batch_size
!=
0
:
remaining
=
len
(
data_train
)
-
batch_count
*
batch_size
w1
,
b1
,
w2
,
b2
,
acc
=
learn_once_cross_entropy
(
w1
,
b1
,
w2
,
b2
,
data_train
[
-
remaining
:],
labels_train
[
-
remaining
:],
learning_rate
,
remaining
)
batch_accuracies
.
append
(
acc
)
epoch_accuracy
=
sum
(
batch_accuracies
)
/
len
(
batch_accuracies
)
print
(
f
"
Epoch
{
epoch
+
1
}
Accuracy:
{
epoch_accuracy
:
.
4
f
}
"
)
training_accuracies
.
append
(
epoch_accuracy
)
return
w1
,
b1
,
w2
,
b2
,
training_accuracies
def
test_mlp
(
w1
,
b1
,
w2
,
b2
,
data_test
,
labels_test
):
# Forward pass
a0
=
data_test
z1
=
np
.
matmul
(
a0
,
w1
)
+
b1
a1
=
1
/
(
1
+
np
.
exp
(
-
z1
))
z2
=
np
.
matmul
(
a1
,
w2
)
+
b2
a2
=
sp
.
softmax
(
z2
)
# Compute accuracy
correct_count
=
np
.
sum
(
a2
.
argmax
(
axis
=
1
)
==
labels_test
)
return
correct_count
/
len
(
labels_test
)
def
run_mlp_training
(
data_train
,
labels_train
,
data_test
,
labels_test
,
d_h
,
learning_rate
=
0.1
,
nb_epochs
=
100
,
batch_size
=
200
):
# Initialize network parameters
w1
=
np
.
random
.
uniform
(
-
1
,
1
,
(
3072
,
d_h
))
b1
=
np
.
zeros
((
1
,
d_h
))
w2
=
np
.
random
.
uniform
(
-
1
,
1
,
(
d_h
,
10
))
b2
=
np
.
zeros
((
1
,
10
))
# Training phase
w1
,
b1
,
w2
,
b2
,
train_accuracies
=
train_mlp
(
w1
,
b1
,
w2
,
b2
,
data_train
,
labels_train
,
learning_rate
,
nb_epochs
,
batch_size
)
# Testing phase
test_accuracy
=
test_mlp
(
w1
,
b1
,
w2
,
b2
,
data_test
,
labels_test
)
print
(
f
"
Test Accuracy:
{
test_accuracy
:
.
4
f
}
"
)
return
train_accuracies
,
test_accuracy
if
__name__
==
"
__main__
"
:
# Load and preprocess data
data
,
labels
=
read_cifar
.
read_cifar
(
'
data/cifar-10-batches-py
'
)
data_train
,
labels_train
,
data_test
,
labels_test
=
read_cifar
.
split_dataset
(
data
,
labels
,
0.9
)
# Execute training and testing
train_accuracies
,
test_accuracy
=
run_mlp_training
(
data_train
,
labels_train
,
data_test
,
labels_test
,
64
,
0.1
,
100
,
100
)
plt
.
plot
(
train_accuracies
,
label
=
"
Training Accuracy
"
)
plt
.
legend
()
plt
.
show
()
\ No newline at end of file
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