我尝试使用自定义数据集（由2个类组成）微调InceptionV3模型，但是在训练和验证方面我都获得了非常低的准确性。我该怎么做才能提高准确性？还是为此目的有其他网络构想/实现？
我的代码：
from keras.datasets import cifar10
from keras.utils import *
from keras.optimizers import SGD
from keras.layers import Input,Dense,Flatten,Dropout,GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
from keras.models import Model
from keras.applications.inception_v3 import InceptionV3
import numpy as np
import cv2
epochs = 10
steps_per_epoch = 300
validation_steps = 300
input_shape=(64, 64, 3)
image_rows=64
image_cols=64
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
'dataset/train',
target_size=(image_rows, image_cols),
batch_size=32,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
'dataset/evaluate',
target_size=(image_rows, image_cols),
batch_size=32,
class_mode='categorical')
inputs = Input(shape=input_shape)
base_model = InceptionV3(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(2, activation='softmax')(x)
model = Model(input=base_model.input, output=predictions)
for layer in base_model.layers:
layer.trainable = False
model.compile(
optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit_generator(
train_generator,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_steps)
最佳答案
您的问题在于，根据Keras InceptionV3 documentation的要求-最小输入大小为139。因此-由于您的网络输入大小为64，因此您的网络无法正常运行。要解决此问题：
将输入大小更改为n，其中n > 139
在每个flow_from_directory中-将target_size更改为(n, n)。
关于python - InceptionV3微调:为什么使用InceptionV3微调会获得非常低的(.37)精度？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/44879991/