Question

当我尝试使用RestNet50冻结所有图层来对Fashion MNIST数据集进行分类时，我只能获得大约78％的训练准确度和41％的预测准确度。下面是代码片段：

from keras import optimizers
from keras.applications.resnet50 import ResNet50
from keras.datasets import fashion_mnist
from keras.layers import Activation, Flatten, Dense
from keras.models import Model

(x, y), (x_test, y_test) = fashion_mnist.load_data()

dat_train, dat_val, train_lbs, val_lbs = train_test_split(x, y, test_size=10000, random_state=42)

... # transform dat_train, dat_val, x_test from shapes (28, 28, ) to (32, 32, 3) and re-scale to value range [0, 1], also one hot encoding train_lbs, val_lbs, y_test to shape (, 10)

resnet50_base = ResNet50(include_top=False, 
                         weights='imagenet', 
                         input_shape=(32, 32, 3))          

for layer in resnet50_base.layers:  
    layer.trainable = False     

base_out = resnet50_base.output                             
base_out = Flatten()(base_out)                             
base_out = Dense(128)(base_out)                             
base_out = Activation("relu")(base_out)                   
preds = Dense(10, activation="softmax")(base_out)          

model = Model(inputs=resnet50_base.input, outputs=preds) 

model.compile(loss="categorical_crossentropy",            
                optimizer=optimizers.Adam(lr=0.0005),       
                metrics=["accuracy"])

产生了这个结果

我做错了还是ResNet50不适合Fashion MNIST数据集？

Answer 1

嘿，您可以看到我的仓库https://github.com/rushu570/Fashion_Mnist以了解其工作原理

为什么最新的网络在Fashion MNIST上表现不佳

1 个答案: