This is an update from my previous question.
I'll put my Model Development code here for your reference:
# Splitting the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Create a neural network model
model = Sequential()
Convolutional layers
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(img_width, img_height, 3)))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))
Flatten layer
model.add(Flatten())
Fully connected layers
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(BatchNormalization())
model.add(Dense(37, activation='sigmoid')) # 37 output nodes for 37 labels
Compile the model
model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
Print model summary
model.summary()
Train the model
history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2, verbose=1)
Evaluate the model on the test set
test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)
print(f'Test Accuracy: {test_accuracy * 100:.2f}%')
Save the trained model
model.save('my_model.keras')
My test accuracy is currently low, and I'm seeking guidance to improve my model's performance. Any code examples or detailed explanations would be appreciated.
