我正在學習機器學習，我的數(shù)據(jù)集由 7 列組成：home_team, away_team, home_odds, away_odds, home_score, away_score, 1_if_home_wins_else_0為了能夠為團隊提供 Tensorflow，我將每個團隊都轉(zhuǎn)換為整數(shù)，因此前兩列是整數(shù)（如數(shù)據(jù)庫 ID）csv 中有 10k 行。例子我修改了pima Indians 糖尿病的代碼來預測主隊的獲勝情況。所以現(xiàn)在它“預測”主隊是否贏（1）否則為0。現(xiàn)在我想修改算法來預測準確的分數(shù)home_score，away_score。我知道輸出是錯誤的，它只是在學習。代碼# load the datasetdataset = loadtxt('football_data.csv', delimiter=',')# split into input (X) and output (y) variablesX = dataset[:, 0:4]y = dataset[:, 6]# define the keras modelmodel = Sequential()model.add(Dense(12, input_dim=4, activation='relu'))model.add(Dense(8, activation='relu'))model.add(Dense(1, activation='sigmoid'))# compile the keras modelmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])# fit the keras model on the datasetmodel.fit(X, y, epochs=150, batch_size=10)# evaluate the keras model_, accuracy = model.evaluate(X, y)print('Accuracy: %.2f' % (accuracy * 100))# make class predictions with the modelpredictions = model.predict_classes(X)# summarize the first 5 casesfor i in range(50):    print('%s => %d (expected %d)' % (X[i].tolist(), predictions[i], y[i]))你知道怎么做嗎？