Keras之MLP：利用MLP【Input(8)→(12)(relu)→O(sigmoid+二元交叉)】模型实现预测新数据(利用糖尿病数据集的八个特征实现二分类预测
目录
输出结果
实现代码
输出结果
实现代码
# load and prepare the dataset
dataset = numpy.loadtxt("data/pima-indians-diabetes.csv", delimiter=",")
X = dataset[:,0:8]
Y = dataset[:,8]
# 1. define the network
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# 2. compile the network
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# 3. fit the network
history = model.fit(X, Y, epochs=100, batch_size=10)
# 4. evaluate the network
loss, accuracy = model.evaluate(X, Y)
print("\nLoss: %.2f, Accuracy: %.2f%%" % (loss, accuracy*100))
# 5. make predictions
probabilities = model.predict(X)
predictions = [float(numpy.round(x)) for x in probabilities] #numpy.round
accuracy = numpy.mean(predictions == Y)
print("Prediction Accuracy: %.2f%%" % (accuracy*100))
