-
Notifications
You must be signed in to change notification settings - Fork 0
/
classifier.py
26 lines (21 loc) · 966 Bytes
/
classifier.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
# Assuming X contains preprocessed data and y contains labels
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu'),
Flatten(),
Dense(64, activation='relu'),
Dropout(0.5),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))
loss, accuracy = model.evaluate(X_test, y_test)
print(f'Test Accuracy: {accuracy}')