Summary: This project is an introduction to artificial neural networks, with the implementation of a multilayer perceptron
| Requirements | Skills |
|---|---|
- python3.11- numpy- pandas- matplotlib |
- DB & Data- Algorithms & AI |
This project is based on the multilayer perceptron from 42 Paris, and I have added more functionalities. For those who want to refer to this project within the scope of 42, here is the link to the multilayer perceptron - 42 Project.
I have another GitHub repository called data_science for any additional use cases involving the multilayer_perceptron project."
It is a CSV file of 32 columns, the column diagnosis being the label you want to learn given all the other features of an example, it can be either the value
The features of the dataset describe the characteristics of a cell nucleus of breast mass extracted with fine-needle aspiration. (for more detailed information, go here).
842302,M,17.99,10.38,122.8,1001,0.1184,0.2776,0.3001,0.1471,0.2419,0.07871,1.095,0.9053,8.589,153.4,0.006399,0.04904,0.05373,0.01587,0.03003,0.006193,25.38,17.33,184.6,2019,0.1622,0.6656,0.7119,0.2654,0.4601,0.1189
842517,M,20.57,17.77,132.9,1326,0.08474,0.07864,0.0869,0.07017,0.1812,0.05667,0.5435,0.7339,3.398,74.08,0.005225,0.01308,0.0186,0.0134,0.01389,0.003532,24.99,23.41,158.8,1956,0.1238,0.1866,0.2416,0.186,0.275,0.08902
84300903,M,19.69,21.25,130,1203,0.1096,0.1599,0.1974,0.1279,0.2069,0.05999,0.7456,0.7869,4.585,94.03,0.00615,0.04006,0.03832,0.02058,0.0225,0.004571,23.57,25.53,152.5,1709,0.1444,0.4245,0.4504,0.243,0.3613,0.08758
84348301,M,11.42,20.38,77.58,386.1,0.1425,0.2839,0.2414,0.1052,0.2597,0.09744,0.4956,1.156,3.445,27.23,0.00911,0.07458,0.05661,0.01867,0.05963,0.009208,14.91,26.5,98.87,567.7,0.2098,0.8663,0.6869,0.2575,0.6638,0.173
84358402,M,20.29,14.34,135.1,1297,0.1003,0.1328,0.198,0.1043,0.1809,0.05883,0.7572,0.7813,5.438,94.44,0.01149,0.02461,0.05688,0.01885,0.01756,0.005115,22.54,16.67,152.2,1575,0.1374,0.205,0.4,0.1625,0.2364,0.07678
...- ID number
- Diagnosis (M = malignant, B = benign)
3-32)
Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from the center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension ("coastline approximation" - 1)
The mean, standard error, and "worst" or largest (mean of the three
largest values) of these features were computed for each image,
resulting in 30 features. For instance, field 3 is Mean Radius, field
13 is Radius SE, field 23 is Worst Radius.
All feature values are recorded with four significant digits.
Missing attribute values: none
Class distribution: 357 benign, 212 malignant
usage: main.py [-h] [-s SPLIT] [-t] [-p] [-c [{models,optimizers}]]
This program is an implementation of a Multilayer Perceptron (MLP), a type of artificial neural network designed for tasks such as classification, regression, and pattern recognition.
options:
-h, --help show this help message and exit
-s SPLIT, --split SPLIT
Split dataset into train and validation sets.
-t, --train Train with dataset.
-p, --predict Predict using saved model.
-c [{models,optimizers}], --compare [{models,optimizers}]
Compare models by plotting learning curves.For training with the saved model topology
python3 main.py --topology topologies/binary_sigmoid.json -t
It saves its weight and bias in a binary_sigmoid.npz file
For prediction
python3 main.py --topology topologies/binary_sigmoid.json -p
input_shape = 30
output_shape = 1
loss='binary_crossentropy', learning_rate=1e-3, batch_size=1, epochs=30
network = model.create_network([
Dense(input_shape, 20, activation='relu'),
Dense(20, 10, activation='relu'),
Dense(10, 5, activation='relu'),
Dense(5, output_shape, activation='sigmoid')
])| loss, accuracy for training and validation |
|---|
 |
| Train accuracy: 0.9845 Validation accuracy: 0.9912 |
input_shape = 30
output_shape = 1
loss='binary_crossentropy', learning_rate=1e-3, batch_size=1, epochs=50
model1 = Model()
model1.create_network([
Dense(input_shape, 20, activation='relu'),
Dense(20, 10, activation='relu'),
Dense(10, 5, activation='relu'),
Dense(5, output_shape, activation='sigmoid')
])
model2 = Model()
model2.create_network([
Dense(input_shape, 15, activation='relu'),
Dense(15, 5, activation='relu'),
Dense(5, output_shape, activation='sigmoid')
])
model3 = Model()
model3.create_network([
Dense(input_shape, 5, activation='relu'),
Dense(5, output_shape, activation='sigmoid')
])| models |
|---|
 |
| Model1 - Train accuracy: 0.9604 Validation accuracy: 0.9736 |
| Model2 - Train accuracy: 0.9560 Validation accuracy: 0.9824 |
| Model3 - Train accuracy: 0.8879 Validation accuracy: 0.8771 |
input_shape = 30
output_shape = 1
loss='binary_crossentropy', learning_rate=1e-3, batch_size=1, epochs=30
model = Model()
model.create_network([
Dense(input_shape, 20, activation='relu'),
Dense(20, 10, activation='relu'),
Dense(10, 5, activation='relu'),
Dense(5, output_shape, activation='sigmoid')
])
model_list = [
(model1, optimizers.SGD(learning_rate=1e-3)),
(model2, optimizers.RMSprop(learning_rate=1e-3)),
(model3, optimizers.Adam(learning_rate=1e-3)),
]| optimizers |
|---|
 |
| Model1 - Train accuracy: 0.9318 Validation accuracy: 0.9473 |
| Model2 - Train accuracy: 0.9340 Validation accuracy: 0.9649 |
| Model2 - Train accuracy: 0.9582 Validation accuracy: 0.9561 |