伙計們，我是新手，距離我開始學(xué)習(xí)已經(jīng)兩天了。我按照tensorflow的步驟做了，并記下代碼的含義。之后，我嘗試做類似的項目。由于才兩天，我嘗試做一下圖像分類。但測試結(jié)果的準(zhǔn)確性較差，無法做出真實的評價。您能否指導(dǎo)，教我如何改進(jìn)這段代碼或者我應(yīng)該學(xué)習(xí)什么來改進(jìn)這段代碼......這是我的代碼：import tensorflow as tffrom tensorflow import kerasimport numpy as npimport matplotlib.pyplot as plt(train_i, train_l), (test_i, test_l) = tf.keras.datasets.cifar10.load_data()classnames = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']model = models.Sequential()model.add(layers.Conv2D(32, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model = keras.Sequential([? ??? ? keras.layers.Flatten(),? ? keras.layers.Dense(100, activation='relu'), #burada kaç tane node olaca??n? belirtiyoruz yani mesela burada 108 se 108 tane node vard?r. Node say?s?n? arrt?rd?kça i?lem h?z?m?z dü?üyor ama tahmin de?erlerimiz gerçe?e daha yak?n oluyor.? ? keras.layers.Dense(10) #burada ise 10 diyoruz çünkü 10 tane class içinden seçecek.])model.compile(optimizer='adam',? ? ? ? ? ? ? loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),? ? ? ? ? ? ? metrics=['accuracy'])model.fit(train_i, train_l, epochs=100)test_loss, test_acc = model.evaluate(test_i,? test_l, verbose=2)print(test_acc)prediction = tf.keras.Sequential([model, tf.keras.layers.Softmax()]).predict(test_i)i = 90prediction[i]prediction_made= np.argmax(prediction[i])f= train_l[i]s=str(train_l[i])print(str(s)[1:-1])b = int(str(s)[1:-1])y = classnames[b]x = classnames[prediction_made]img = train_iplt.grid(False)plt.xticks([])?plt.yticks([])?plt.imshow(img[i])plt.xlabel('The True Label is ' + repr(y) +?? ? ? ? ? ?', and The Predicted Label is ' + repr(x) + '...')?