我對此頁面上的代碼感到困惑。問題1）下面的代碼塊顯示了該頁面的輸出。在這一步之前，我沒有看到任何使用model.fit函數訓練我們的數據的代碼。那么下面的代碼是什么？他們是否使用隨機權重顯示預測？model.predict(train_features[:10])array([[0.6296253 ],       [0.82509124],       [0.75135857],       [0.73724824],       [0.82174015],       [0.33519754],       [0.6719973 ],       [0.30910844],       [0.6378555 ],       [0.8381703 ]], dtype=float32)model = make_model(output_bias = initial_bias)model.predict(train_features[:10])array([[0.00124893],       [0.00185736],       [0.00164955],       [0.00123761],       [0.00137692],       [0.00182851],       [0.00170887],       [0.00239349],       [0.0024704 ],       [0.00517672]], dtype=float32)results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)print("Loss: {:0.4f}".format(results[0]))Loss: 0.0157問題2）繼續在下面說的代碼中。是什么initial_weights？它們是隨機值嗎？initial_weights = os.path.join(tempfile.mkdtemp(),'initial_weights')model.save_weights(initial_weights)問題3)然后他們說Before moving on, confirm quick that the careful bias initialization actually helped.Train the model for 20 epochs, with and without this careful initialization, and compare the losses:, 但我不確定他們是如何分配初始偏差的。我知道我們為對象分配了 0 偏差zero_bias_history。但是我們如何分配偏見careful_bias_history呢？它不應該具有等于initial_bias. 如何careful_bias_history獲得偏差值？我覺得careful_bias_history應該從使用創建的模型創建model = make_model(output_bias = initial_bias)### Confirm that the bias fix helpsBefore moving on, confirm quick that the careful bias initialization actually helped.Train the model for 20 epochs, with and without this careful initialization, and compare the losses: model = make_model()model.load_weights(initial_weights)model.layers[-1].bias.assign([0.0])zero_bias_history = model.fit(    train_features,    train_labels,    batch_size=BATCH_SIZE,    epochs=20,    validation_data=(val_features, val_labels),     verbose=0)print (type(model))#model.load_weights()