CNN 模型仅首先预测 Class

Question

我正在研究细粒度 class化 class化汽车模型。所以我使用了迁移学习 ResNet50。据我所知，它在训练时表现良好。但是当我尝试新图像时，它总是预测单个 class。下面是我的代码。

训练：

from tensorflow.keras.layers import Input, Lambda, Dense, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.applications.resnet50 import ResNet50
from keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
from glob import glob


IMAGE_SIZE = [224, 224]

train_path = 'Datasets/train'
valid_path = 'Datasets/test'

resnet = ResNet50(input_shape = IMAGE_SIZE + [3], weights='imagenet', include_top = False)


for layer in resnet.layers:
    layer.trainable = False

folders = glob('Datasets/train/*') #training folders

x = Flatten()(resnet.output)

prediction = Dense(len(folders), activation='softmax') (x) 

model = Model(inputs = resnet.input, outputs = prediction)

model.compile(
    loss = 'categorical_crossentropy',
    optimizer = 'adam',
    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('Datasets/train',
                                                target_size = (224, 224),
                                                batch_size = 32,
                                                class_mode = 'categorical')


test_set = test_datagen.flow_from_directory('Datasets/test',
                                            target_size = (224, 224),
                                            batch_size = 32,
                                            class_mode = 'categorical')


r = model.fit_generator(
training_set,
validation_data=test_set,
epochs=200,
steps_per_epoch=len(training_set),
validation_steps=len(test_set)
)


from tensorflow.keras.models import load_model

model.save('model_updateV1.h5')

y_pred = model.predict(test_set)


import numpy as np
y_pred = np.argmax(y_pred, axis=1)

尝试新图像：

from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
from tensorflow.keras.applications.resnet50 import preprocess_input

model = load_model('model_updateV1.h5')

img = image.load_img('Datasets/test/mercedes/45.jpg', target_size=(224,224))

x = image.img_to_array(img)

x = x/255.

x = np.expand_dims(x, axis = 0)
img_data = preprocess_input(x)
img_data.shape

model.predict(img_data)

a = np.argmax(model.predict(img_data), axis=1)
a

Answer 1

我认为您的问题是您将图像重新缩放了两次。您有代码

x=x/255

然后你扩大尺寸就好了。但是你有代码

img_data = preprocess_input(x)

我相信 preprocess_input 函数会使用代码

重新缩放 -1 和 +1 之间的像素值

x=x/127.5-1.

所以现在你的像素值已经缩小了两倍。所以只需删除代码

x=x/255

CNN 模型仅首先预测 Class

CNN Model Predicting Only First Class

tensorflow

deep-learning

conv-neural-network

transfer-learning

image-classification