####### Single Hidden Layer ANN from numpy import loadtxt from keras.models import Sequential from keras.layers import Dense dataset = loadtxt('labeled.csv', delimiter=',') # split into input (X) and output (y) variables X = dataset[:, 0:16] y = dataset[:, 16] # Define the keras model # Default activator: linear model = Sequential() model.add(Dense(8, input_dim=16)) model.add(Dense(4)) model.add(Dense(1)) # compile the keras model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # fit the keras model on the dataset # model.fit(X, y, epochs=100, batch_size=25) model.fit(X, y, epochs=100, batch_size=32) # evaluate the keras model _, accuracy = model.evaluate(X, y) print('Accuracy: %.2f' % (accuracy * 100), '%')
print("Training Accuracy :", model.score(x_train, y_train)) print("Testing Accuracy :", model.score(x_test, y_test)) cm = confusion_matrix(y_test, y_pred) print('cm:', cm) # Artificial Neural Networks import keras from keras.models import Sequential from keras.layers import Dense # creating the model model = Sequential() # first hidden layer model.add(Dense(output_dim=8, init='uniform', activation='relu', input_dim=14)) # second hidden layer model.add(Dense(output_dim=8, init='uniform', activation='relu')) # third hidden layer model.add(Dense(output_dim=8, init='uniform', activation='relu')) # fourth hidden layer model.add(Dense(output_dim=8, init='uniform', activation='relu')) # fifth hidden layer model.add(Dense(output_dim=8, init='uniform', activation='relu')) # output layer model.add(Dense(output_dim=1, init='uniform', activation='sigmoid'))
results # Mo hinh mang neural # Them thu vien keras va cac goi import keras from keras.models import Sequential from keras.layers import Dense # Khoi tao mang luoi classifier = Sequential() classifier.add( Dense(units=15, kernel_initializer='uniform', activation='relu', input_dim=29)) classifier.add(Dense(units=15, kernel_initializer='uniform', activation='relu')) classifier.add( Dense(units=1, kernel_initializer='uniform', activation='sigmoid')) classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Phu hop mang luoi vao tap train classifier.fit(X_train, y_train, batch_size=32, epochs=100) # Du doan ket qua tap thu
# 8.1 MLP from sklearn.neural_network import MLPClassifier classifier = MLPClassifier(hidden_layer_sizes=[10], solver='lbfgs', random_state=0).fit(X_train_scaled, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test_scaled) # 9.1 ANN # Importing the Keras libraries and packages import keras from keras.models import Sequential from keras.layers import Dense # Initialising the ANN classifier = Sequential() # Adding the input layer and the first hidden layer classifier.add( Dense(output_dim=6, init='uniform', activation='relu', input_dim=11)) # Adding the second hidden layer classifier.add(Dense(output_dim=6, init='uniform', activation='relu')) # Adding the output layer classifier.add(Dense(output_dim=1, init='uniform', activation='sigmoid')) # Compiling the ANN classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Fitting the ANN to the Training set classifier.fit(X_train, y_train, batch_size=10, nb_epoch=100) # Predicting the Test set results y_pred = classifier.predict(X_test_scaled)