我正在尝试为图像分类问题训练resnet50模型。在我拥有的图像数据集上训练模型之前，我已经加载了"imagenet"预训练权重。我正在使用 keras 函数 flow_from_directory(( 从目录中加载图像。
train_datagen = ImageDataGenerator()
train_generator = train_datagen.flow_from_directory(
'./train_qcut_2_classes',
batch_size=batch_size,
shuffle=True,
target_size=input_size[1:],
class_mode='categorical')
test_datagen = ImageDataGenerator()
validation_generator = test_datagen.flow_from_directory(
'./validate_qcut_2_classes',
batch_size=batch_size,
target_size=input_size[1:],
shuffle=True,
class_mode='categorical')
我将生成器作为参数传递到fit_generator函数中。
hist2=model.fit_generator(train_generator,
samples_per_epoch=102204,
validation_data=validation_generator,
nb_val_samples=25547,
nb_epoch=80, callbacks=callbacks,
verbose=1)
问题：
通过此设置，如何使用 preprocess_input(( 函数在将输入图像传递给模型之前对其进行预处理？
from keras.applications.resnet50 import preprocess_input
我尝试使用preprocessing_function参数如下
train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input)
train_generator = train_datagen.flow_from_directory(
'./train_qcut_2_classes',
batch_size=batch_size,
shuffle=True,
target_size=input_size[1:],
class_mode='categorical')
test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
validation_generator = test_datagen.flow_from_directory(
'./validate_qcut_2_classes',
batch_size=batch_size,
target_size=input_size[1:],
shuffle=True,
class_mode='categorical')
当我尝试提取预处理的输出时，我得到了以下结果。
train_generator.next()[0][0]
array([[[ 91.06099701, 80.06099701, 96.06099701, ..., 86.06099701,
52.06099701, 12.06099701],
[ 101.06099701, 104.06099701, 118.06099701, ..., 101.06099701,
63.06099701, 19.06099701],
[ 117.06099701, 103.06099701, 88.06099701, ..., 88.06099701,
74.06099701, 18.06099701],
...,
[-103.93900299, -103.93900299, -103.93900299, ..., -24.93900299,
-38.93900299, -24.93900299],
[-103.93900299, -103.93900299, -103.93900299, ..., -52.93900299,
-27.93900299, -39.93900299],
[-103.93900299, -103.93900299, -103.93900299, ..., -45.93900299,
-29.93900299, -28.93900299]],
[[ 81.22100067, 70.22100067, 86.22100067, ..., 69.22100067,
37.22100067, -0.77899933],
[ 91.22100067, 94.22100067, 108.22100067, ..., 86.22100067,
50.22100067, 6.22100067],
[ 107.22100067, 93.22100067, 78.22100067, ..., 73.22100067,
62.22100067, 6.22100067],
...,
[-116.77899933, -116.77899933, -116.77899933, ..., -36.77899933,
-50.77899933, -36.77899933],
[-116.77899933, -116.77899933, -116.77899933, ..., -64.77899933,
-39.77899933, -51.77899933],
[-116.77899933, -116.77899933, -116.77899933, ..., -57.77899933,
-41.77899933, -40.77899933]],
[[ 78.31999969, 67.31999969, 83.31999969, ..., 61.31999969,
29.31999969, -7.68000031],
[ 88.31999969, 91.31999969, 105.31999969, ..., 79.31999969,
43.31999969, -0.68000031],
[ 104.31999969, 90.31999969, 75.31999969, ..., 66.31999969,
53.31999969, -2.68000031],
...,
[-123.68000031, -123.68000031, -123.68000031, ..., -39.68000031,
-53.68000031, -39.68000031],
[-123.68000031, -123.68000031, -123.68000031, ..., -67.68000031,
-42.68000031, -54.68000031],
[-123.68000031, -123.68000031, -123.68000031, ..., -60.68000031,
-44.68000031, -43.68000031]]], dtype=float32)
为了确认这一点，我直接对特定图像使用了预处理功能，
import cv2
img = cv2.imread('./images.jpg')
img = img_to_array(img)
x = np.expand_dims(img, axis=0)
x = x.astype(np.float64)
x = preprocess_input(x)
给出以下输出，
array([[[[ 118.061, 125.061, 134.061, ..., 97.061, 99.061, 102.061],
[ 118.061, 125.061, 133.061, ..., 98.061, 100.061, 102.061],
[ 113.061, 119.061, 126.061, ..., 100.061, 101.061, 102.061],
...,
[ 65.061, 64.061, 64.061, ..., 60.061, 61.061, 57.061],
[ 64.061, 64.061, 63.061, ..., 66.061, 67.061, 59.061],
[ 56.061, 59.061, 62.061, ..., 61.061, 60.061, 59.061]],
[[ 113.221, 120.221, 129.221, ..., 112.221, 114.221, 113.221],
[ 116.221, 123.221, 131.221, ..., 113.221, 115.221, 113.221],
[ 118.221, 124.221, 131.221, ..., 115.221, 116.221, 113.221],
...,
[ 56.221, 55.221, 55.221, ..., 51.221, 52.221, 51.221],
[ 55.221, 55.221, 54.221, ..., 57.221, 58.221, 53.221],
[ 47.221, 50.221, 53.221, ..., 52.221, 51.221, 50.221]],
[[ 109.32 , 116.32 , 125.32 , ..., 106.32 , 108.32 , 108.32 ],
[ 111.32 , 118.32 , 126.32 , ..., 107.32 , 109.32 , 108.32 ],
[ 111.32 , 117.32 , 124.32 , ..., 109.32 , 110.32 , 108.32 ],
...,
[ 34.32 , 33.32 , 33.32 , ..., 30.32 , 31.32 , 26.32 ],
[ 33.32 , 33.32 , 32.32 , ..., 36.32 , 37.32 , 28.32 ],
[ 25.32 , 28.32 , 31.32 , ..., 30.32 , 29.32 , 28.32 ]]]])
关于为什么会发生这种情况的任何想法？
作为创建ImageDataGenerator时的参数：
train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)