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Elkhadri Doha
Image classification
Commits
756eda29
Commit
756eda29
authored
1 year ago
by
Elkhadri Doha
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import
numpy
as
np
import
matplotlib.pyplot
as
plt
from
read_cifar
import
read_cifar_batch
,
read_cifar
,
split_dataset
N
=
30
# number of input data
d_in
=
3
# input dimension
d_h
=
3
# number of neurons in the hidden layer
d_out
=
2
# output dimension (number of neurons of the output layer)
# Random initialization of the network weights and biaises
w1
=
2
*
np
.
random
.
rand
(
d_in
,
d_h
)
-
1
# first layer weights
b1
=
np
.
zeros
((
1
,
d_h
))
# first layer biaises
w2
=
2
*
np
.
random
.
rand
(
d_h
,
d_out
)
-
1
# second layer weights
b2
=
np
.
zeros
((
1
,
d_out
))
# second layer biaises
data
=
np
.
random
.
rand
(
N
,
d_in
)
# create a random data
targets
=
np
.
random
.
rand
(
N
,
d_out
)
# create a random targets
def
sigmoid
(
x
):
return
1
/
(
1
+
np
.
exp
(
-
np
.
clip
(
x
,
-
500
,
500
)))
def
learn_once_mse
(
w1
,
b1
,
w2
,
b2
,
data
,
targets
,
learning_rate
):
# Forward pass
a0
=
data
# the data are the input of the first layer
z1
=
np
.
matmul
(
a0
,
w1
)
+
b1
# input of the hidden layer
a1
=
1
/
(
1
+
np
.
exp
(
-
z1
))
# output of the hidden layer (sigmoid activation function)
z2
=
np
.
matmul
(
a1
,
w2
)
+
b2
# input of the output layer
a2
=
sigmoid
(
z2
)
# output of the output layer (sigmoid activation function)
predictions
=
a2
# the predicted values are the outputs of the output layer
# Compute loss (MSE)
loss
=
np
.
mean
(
np
.
square
(
predictions
-
targets
))
print
(
loss
)
# Backpropagation
dC_da2
=
2
*
np
.
sum
(
predictions
-
targets
)
/
N
dC_dz2
=
dC_da2
*
predictions
*
(
1
-
predictions
)
dC_dw2
=
np
.
matmul
(
a1
.
T
,
dC_dz2
)
dC_db2
=
np
.
sum
(
dC_dz2
,
axis
=
0
)
dC_da1
=
np
.
matmul
(
dC_dz2
,
w2
.
T
)
dC_dz1
=
dC_da1
*
a1
*
(
1
-
a1
)
dC_dw1
=
np
.
matmul
(
a0
.
T
,
dC_dz1
)
dC_db1
=
np
.
sum
(
dC_dz1
,
axis
=
0
)
# Update weights and biases
w2
=
w2
-
learning_rate
*
dC_dw2
b2
=
b2
-
learning_rate
*
dC_db2
w1
=
w1
-
learning_rate
*
dC_dw1
b1
=
b1
-
learning_rate
*
dC_db1
return
w1
,
b1
,
w2
,
b2
,
loss
#Convert a list into a matrix
def
one_hot
(
labels
):
n_samples
=
len
(
labels
)
n_unique
=
len
(
np
.
unique
(
labels
))
one_hot_matrix
=
np
.
zeros
((
n_samples
,
n_unique
))
one_hot_matrix
[
np
.
arange
(
n_samples
),
labels
]
=
1
return
one_hot_matrix
def
softmax
(
z
):
exp_z
=
np
.
exp
(
z
)
sum
=
exp_z
.
sum
()
softmax_z
=
exp_z
/
sum
return
softmax_z
def
learn_once_cross_entropy
(
w1
,
b1
,
w2
,
b2
,
data
,
labels_train
,
learning_rate
):
a0
=
data
# the data are the input of the first layer
z1
=
np
.
matmul
(
a0
,
w1
)
+
b1
# input of the hidden layer
a1
=
1
/
(
1
+
np
.
exp
(
-
z1
))
# output of the hidden layer (sigmoid activation function)
z2
=
np
.
matmul
(
a1
,
w2
)
+
b2
# input of the output layer
a2
=
softmax
(
z2
)
#Softmax activation layer
predict
=
a2
# the predicted values are the outputs of the output layer
targets
=
one_hot
(
labels_train
)
# Compute the loss
loss
=
loss
=
-
np
.
sum
(
targets
*
np
.
log
(
predict
))
# Backpropagation
dC_da2
=
2
*
np
.
sum
(
predict
-
targets
)
/
N
dC_dz2
=
(
2
/
N
)
*
(
predict
-
targets
)
*
a2
*
(
1
-
predict
)
dC_dw2
=
np
.
dot
(
a1
.
T
,
dC_dz2
)
dC_db2
=
dC_dz2
dC_da1
=
np
.
dot
(
dC_dz2
,
w2
.
T
)
dC_dz1
=
dC_da1
*
a1
*
(
1
-
a1
)
dC_dw1
=
np
.
dot
(
a0
.
T
,
dC_dz1
)
dC_db1
=
np
.
sum
(
dC_dz1
,
axis
=
0
)
# Update weights and biases
w2
=
w2
-
learning_rate
*
dC_dw2
b2
=
b2
-
learning_rate
*
dC_db2
w1
=
w1
-
learning_rate
*
dC_dw1
b1
=
b1
-
learning_rate
*
dC_db1
return
w1
,
b1
,
w2
,
b2
,
loss
def
accuracy
(
labels
,
predictions
):
return
np
.
mean
(
labels
==
predictions
)
def
train_mlp
(
w1
,
b1
,
w2
,
b2
,
data_train
,
labels_train
,
learning_rate
,
num_epoch
):
train_accuracies
=
[]
batch_size
,
d_in
=
data_train
.
shape
train_accuracies
=
[]
N
=
len
(
labels_train
)
# Number of samples in the training set
# Ensure num_epoch is an integer
num_epoch
=
int
(
np
.
isscalar
(
num_epoch
))
for
i
in
range
(
num_epoch
):
# Forward propagation
z1
=
np
.
dot
(
data_train
,
w1
)
+
b1
a1
=
sigmoid
(
z1
)
z2
=
np
.
dot
(
a1
,
w2
)
+
b2
a2
=
softmax
(
z2
)
predicted_labels
=
np
.
argmax
(
a2
,
axis
=
1
)
# the predicted labels
# Compute the accuracy on the training set
train_accuracy
=
accuracy
(
labels_train
,
predicted_labels
)
train_accuracies
.
append
(
train_accuracy
)
# train_accurary = np.mean(np.array(predicted_labels) == np.array(labels_train))
return
w1
,
b1
,
w2
,
b2
,
train_accuracies
def
test_mlp
(
w1
,
b1
,
w2
,
b2
,
data_test
,
labels_test
):
predictions
=
[]
# Forward propagation
a0
=
data_test
z1
=
np
.
dot
(
a0
,
w1
)
+
b1
a1
=
sigmoid
(
z1
)
z2
=
np
.
dot
(
a1
,
w2
)
+
b2
a2
=
sigmoid
(
z2
)
predicted_labels_test
=
np
.
argmax
(
a2
,
axis
=
1
)
test_accuracy
=
np
.
mean
(
predicted_labels_test
==
labels_test
)
return
test_accuracy
def
run_mlp_training
(
data_train
,
labels_train
,
data_test
,
labels_test
,
d_h
,
learning_rate
,
num_epochs
):
d_in
=
data_train
.
shape
[
1
]
d_out
=
len
(
np
.
unique
(
labels_train
))
# Initialize weights and biases
w1
=
np
.
random
.
randn
(
d_in
,
d_h
)
b1
=
np
.
zeros
((
1
,
d_h
))
w2
=
np
.
random
.
randn
(
d_h
,
d_out
)
b2
=
np
.
zeros
((
1
,
d_out
))
# Train MLP
w1
,
b1
,
w2
,
b2
,
train_accuracies
=
train_mlp
(
w1
,
b1
,
w2
,
b2
,
data_train
,
labels_train
,
learning_rate
,
num_epochs
)
# Test MLP
final_test_accuracy
=
test_mlp
(
w1
,
b1
,
w2
,
b2
,
data_test
,
labels_test
)
return
train_accuracies
,
final_test_accuracy
if
__name__
==
"
__main__
"
:
# Create train and test datasets
data
,
labels
=
read_cifar
(
r
"
C:\Users\etulyon1\OneDrive\Desktop\ECL\Apprentissage profond & Intelligence Artificielle\BE1\image-classification\data\cifar-10-batches-py
"
)
split_factor
=
0.9
a
,
b
,
c
,
d
=
split_dataset
(
data
,
labels
,
split_factor
)
# Define the network hyper-parameters and train it
d_h
=
64
learning_rate
=
0.1
num_epoch
=
100
train_accuracies
,
final_accuracy
=
run_mlp_training
(
a
,
b
,
c
,
d
,
d_h
,
learning_rate
,
num_epoch
)
print
(
"
The accuracy of the network on the test dataset is
"
+
str
(
final_accuracy
)
+
"
%
"
)
# Plot the evolution of the accuracy with the number training steps
plt
.
plot
(
train_accuracies
)
plt
.
xlabel
(
"
Number of training steps
"
)
plt
.
ylabel
(
"
Accuracy (in %)
"
)
plt
.
title
(
"
Accuracy of the Neural Network depending on the number of iterations of training
"
)
plt
.
show
()
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