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 y_pred = classifier.predict(X_test) y_pred = (y_pred > 0.5) score = classifier.evaluate(X_test, y_test) score # Tao ma tran hon loan
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), '%') # Single Hidden Layer ANN with Holdout from sklearn.model_selection import train_test_split from sklearn.metrics import * from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler scaler=MinMaxScaler() scaler.fit(X_train) X_train=scaler.transform(X_train) X_test=scaler.transform(X_test) from keras.models import Sequential from keras.layers import Dense, Dropout from keras.optimizers import Adam model = Sequential([Dense(128, activation='relu'), Dropout(0.5), Dense(128, activation='relu'), Dropout(0.5), Dense(11, activation='softmax')]) model.compile(loss='sparse_categorical_crossentropy', optimizer=Adam(), metrics=['mean_squared_error','accuracy']) history = model.fit( X_train, Y_train, batch_size=32, epochs=30, validation_split = 0.2, verbose=1) model.predict(testDF)