訓練二進制CNN（Keras）-訓練時間慢

Question

我正在訓練Smiling/Not_smiling的二進制CNN，以對情緒的極性（表達）進行分類，例如Smiling/Not_smiling 。 這是我的代碼。 我正在多GPU機器上進行此培訓，但對培訓所需的時間感到驚訝。 每個類的二進制模型需要5到6個小時。 這正常嗎？

我以前曾訓練過一個結合所有課程的multi-class模型，總共花了大約4個小時。

注意：每個pos / neg類包含約5000-10000張圖像。

我這樣做對嗎？ 這個培訓時間是預期的嗎？

class_names = ["smiling","frowning","surprised","sad"]
## set vars!
for cname in class_names:
    print("[+] training: ",model_name,cname)

    dp_path_train = './emotion_data/{0}/train/{1}'.format(model_name,cname)
    dp_path_val = './emotion_data/{0}/val/{1}'.format(model_name,cname)
    dir_checkpoint = './models'
    G = 2 # no. of gpus to use

    batch_size = 32 * G
    step_size = 1000//G
    print("[*] batch size & step size: ", batch_size,step_size)

    model = Sequential()
    model.add(Conv2D(32, kernel_size = (3, 3), activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 3)))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(BatchNormalization())
    model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(BatchNormalization())
    model.add(Conv2D(64, kernel_size=(3,3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(BatchNormalization())
    model.add(Conv2D(96, kernel_size=(3,3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(BatchNormalization())
    model.add(Conv2D(32, kernel_size=(3,3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(BatchNormalization())
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.3))
    model.add(Dense(1, activation = 'sigmoid'))
    model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

    train_datagen = ImageDataGenerator(rescale = 1./255,
        shear_range = 0.2,
        zoom_range = 0.2,
        horizontal_flip = True)
    test_datagen = ImageDataGenerator(rescale = 1./255)

    training_set = train_datagen.flow_from_directory(dp_path_train,
        target_size = (224, 224),
        batch_size = batch_size,
        class_mode = 'binary')

    test_set = test_datagen.flow_from_directory(dp_path_val,
        target_size = (224, 224),
        batch_size = batch_size,
        class_mode = 'binary')

    model.fit_generator(training_set,
        steps_per_epoch = step_size,
        epochs = 50,
        validation_data = test_set,
        validation_steps = 2000)

    print("[+] saving model: ",model_name,cname)
    model.save("./models2/{0}_{1}.hdf5".format(model_name,cname))

Answer 1

刪除所有BatchNormalization層應有助於加快處理速度，或者您可以在網絡體系結構層之間較少地使用它