Keras之MLP：利用MLP【Input(8)→(12)(relu)→O(sigmoid+二元交叉)】模型實(shí)現(xiàn)預(yù)測(cè)新數(shù)據(jù)(利用糖尿病數(shù)據(jù)集的八個(gè)特征實(shí)現(xiàn)二分類預(yù)測(cè)

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輸出結(jié)果

實(shí)現(xiàn)代碼

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輸出結(jié)果

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實(shí)現(xiàn)代碼

# 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))

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