我對(duì)此頁(yè)面上的代碼感到困惑。問(wèn)題1）下面的代碼塊顯示了該頁(yè)面的輸出。在這一步之前，我沒有看到任何使用model.fit函數(shù)訓(xùn)練我們的數(shù)據(jù)的代碼。那么下面的代碼是什么？他們是否使用隨機(jī)權(quán)重顯示預(yù)測(cè)？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(wèn)題2）繼續(xù)在下面說(shuō)的代碼中。是什么initial_weights？它們是隨機(jī)值嗎？initial_weights = os.path.join(tempfile.mkdtemp(),'initial_weights')model.save_weights(initial_weights)問(wèn)題3)然后他們說(shuō)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?yōu)閷?duì)象分配了 0 偏差zero_bias_history。但是我們?nèi)绾畏峙淦奵areful_bias_history呢？它不應(yīng)該具有等于initial_bias. 如何careful_bias_history獲得偏差值？我覺得careful_bias_history應(yīng)該從使用創(chuàng)建的模型創(chuàng)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()