multi-classification

multi_labels.py

model = Sequential()
model.add(layers.Dense(32, input_dim = np.array(X).shape[1], activation='relu'))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(train_y.shape[1], activation='softmax'))

model.compile(loss='categorical_crossentropy',optimizer='adam', metrics='accuracy')
print("Training for attack classification model:")
model.fit(train_x, train_y, verbose=2, batch_size=15, epochs=50, validation_data=(test_x, test_y),
callbacks=[EarlyStopping(monitor='loss', patience=3)], class_weight= class_weights_dict)