所以我設計了一個 CNN 并使用以下參數進行編譯，training_file_loc = "8-SignLanguageMNIST/sign_mnist_train.csv"testing_file_loc = "8-SignLanguageMNIST/sign_mnist_test.csv"def getData(filename): images = [] labels = [] with open(filename) as csv_file: file = csv.reader(csv_file, delimiter = ",") next(file, None) for row in file: label = row[0] data = row[1:] img = np.array(data).reshape(28,28) images.append(img) labels.append(label) images = np.array(images).astype("float64") labels = np.array(labels).astype("float64") return images, labelstraining_images, training_labels = getData(training_file_loc)testing_images, testing_labels = getData(testing_file_loc)print(training_images.shape, training_labels.shape)print(testing_images.shape, testing_labels.shape)training_images = np.expand_dims(training_images, axis = 3)testing_images = np.expand_dims(testing_images, axis = 3)training_datagen = ImageDataGenerator( rescale = 1/255, rotation_range = 45, width_shift_range = 0.2, height_shift_range = 0.2, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, fill_mode = "nearest")training_generator = training_datagen.flow( training_images, training_labels, batch_size = 64,)validation_datagen = ImageDataGenerator( rescale = 1/255, rotation_range = 45, width_shift_range = 0.2, height_shift_range = 0.2, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True, fill_mode = "nearest")validation_generator = training_datagen.flow( testing_images, testing_labels, batch_size = 64,])但是，當我運行 model.fit() 時，出現以下錯誤，ValueError: Shapes (None, 1) and (None, 24) are incompatible將損失函數更改為 后sparse_categorical_crossentropy，程序運行良好。我不明白為什么會這樣。誰能解釋這一點以及這些損失函數之間的區別？
2 回答
largeQ
TA貢獻2039條經驗 獲得超8個贊
問題是，categorical_crossentropy期望單熱編碼標簽，這意味著，對于每個樣本，它期望一個長度張量，num_classes其中label第 th 個元素設置為 1，其他所有元素都為 0。
另一方面，sparse_categorical_crossentropy直接使用整數標簽（因為這里的用例是大量的類，所以單熱編碼標簽會浪費大量零的內存）。我相信，但我無法證實這一點，它categorical_crossentropy比它的稀疏對應物運行得更快。
對于您的情況，對于 26 個類，我建議使用非稀疏版本并將您的標簽轉換為單熱編碼，如下所示：
def getData(filename):
images = []
labels = []
with open(filename) as csv_file:
file = csv.reader(csv_file, delimiter = ",")
next(file, None)
for row in file:
label = row[0]
data = row[1:]
img = np.array(data).reshape(28,28)
images.append(img)
labels.append(label)
images = np.array(images).astype("float64")
labels = np.array(labels).astype("float64")
return images, tf.keras.utils.to_categorical(labels, num_classes=26) # you can omit num_classes to have it computed from the data
旁注：除非你有理由使用float64圖像，否則我會切換到float32（它將數據集所需的內存減半，并且模型可能會將它們轉換為float32第一個操作）
BIG陽
TA貢獻1859條經驗 獲得超6個贊
很簡單，對于輸出類為整數的分類問題，使用 sparse_categorical_crosentropy，對于標簽在一個熱編碼標簽中轉換的問題，我們使用 categorical_crosentropy。
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