Question

从我的 .h5 转换后，我的 tflite 输出出现问题，我很确定这与我的模型设置方式有关，有人可以看看并指出我哪里出错了吗？ ?

base_model=vgg16.VGG16(weights='imagenet',include_top=False) #imports the VGG model and discards the last 1000 neuron layer.

x=base_model.output
x=GlobalAveragePooling2D()(x)
#x=Dense(1024,activation='relu')(x) #we add dense layers so that the model can learn more complex #functions and classify for better results.
#x=Dense(1024,activation='relu')(x) #dense layer 2
#Dense(512,activation='relu')(x) #dense layer 3
preds=Dense(1085,activation='softmax')(x) #final layer with softmax activation

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

model.summary()

#now a model has been created based on our architecture

for layer in model.layers[:20]:
    layer.trainable=False
for layer in model.layers[20:]:
    layer.trainable=True

train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input) #included in our dependencies

train_generator=train_datagen.flow_from_directory(r'D:/Desktop/4doors', # this is where you specify the path to the main data folder
                                                 target_size=(224,224),
                                                 color_mode='rgb',
                                                 batch_size=10,
                                                 class_mode='categorical',
                                                 shuffle=True)
opt = SGD(lr= 0.001, momentum=0.9)
model.compile(optimizer=opt,loss='categorical_crossentropy',metrics=['accuracy'])
checkpoint = keras.callbacks.ModelCheckpoint(r'D:/Desktop/5wcars/model{epoch:08d}.h5', period=1)
# Adam optimizer
# loss function will be categorical cross entropy


# evaluation metric will be accuracy

step_size_train=train_generator.n//train_generator.batch_size
model.fit_generator(generator=train_generator,
                   steps_per_epoch=step_size_train,
                   epochs=200,callbacks=[checkpoint])

VGG 模型到 tflite 输出的问题

0 个答案: