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)
