机器学习迁移模型到IOS

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saver.save(sess, MODEL_SAVEPATH, global_step=50)

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if i %SAVE_EPOCH == 0:

tf.train.write_graph(sess.graph, MODEL_SAVE_DIR, 'model.pbtxt')

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with open(TF_MODEL_FILE, 'rb') as f:

serialized = f.read()

tf.reset_default_graph()

original_gdef = tf.GraphDef()

original_gdef.ParseFromString(serialized)

with tf.Graph().as_default() as g:

tf.import_graph_def(original_gdef, name='')

ops = g.get_operations()

try:

for i in range(10000):

print('op id {} : op name: {}, op type: "{}"'.format(str(i),ops[i].name, ops[i].type))

except:

print("全部节点已打印完毕.")

pass

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poo3_frist = sess.run(poo3, feed_dict={inpiut_x: image})   # 按照模型的顺序要, 先喂给它图片, 然后图片提取到瓶颈的tensor

result     = sess.run(predict, feed_dict={change_input:poo3_frist}) # 瓶颈的tensor再转入input传入, 得到我们最后的predict

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- (instancetype)initWithBottleneckInputPlaceholder__0:(MLMultiArray *)BottleneckInputPlaceholder__0 import__Mul__0:(CVPixelBufferRef)import__Mul__0;

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- (inception_v3 *)tfModel {

if (!_tfModel) {

// 1 加载模型, 本身代码会调用init的时候, 方法会调用initWithContentsOfURL, 找到inception文件进行初始化

_tfModel = [[inception_v3 alloc] init];

}

return _tfModel;

}

- (NSString *)predictWithFoodImage:(UIImage *)foodImage

{

// step1: 标准为size, 转为可传入的参数.

UIImage *img = [foodImage scaleToSize:CGSizeMake(299, 299)];             // 转换为可传参的图片大小

CVPixelBufferRef refImage = [[UIImage new] pixelBufferFromCGImage:img];  // 转换为可传参的类型

// step2.1: 由于一开始是没有BottleneckInputPlaceholder, 直接0值初始一个传入

MLMultiArray *holder = [[MLMultiArray alloc] initWithShape:@[@2048] dataType:MLMultiArrayDataTypeDouble error:nil];

// step2.2: 启动预测, 预测完成后得到import__pool_3___reshape__0

inception_v3Output *output = [self.tfModel predictionFromBottleneckInputPlaceholder__0:holder import__Mul__0:refImage error:nil];

// step3: 从第二步, 完整得到了想要的BottleneckInputPlaceholder, 直接代入, 图片也代入.

inception_v3Output *output1 = [self.tfModel predictionFromBottleneckInputPlaceholder__0:output.import__pool_3___reshape__0 import__Mul__0:refImage error:nil];

// step4: 从final_train_ops__softMax_last__0提取预测结果

MLMultiArray *__final = output1.final_train_ops__softMax_last__0;

return [self poAccu:__final];

}

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//触发事件：拍照

- (void)addCamera

{

UIImagePickerController *picker = [[UIImagePickerController alloc] init];

picker.delegate = self;

picker.allowsEditing = YES; //可编辑

//判断是否可以打开照相机

if ([UIImagePickerController isSourceTypeAvailable:UIImagePickerControllerSourceTypeCamera]) {

//摄像头

picker.sourceType = UIImagePickerControllerSourceTypeCamera;

} else { //否则打开照片库

picker.sourceType = UIImagePickerControllerSourceTypePhotoLibrary;

}

[self presentViewController:picker animated:YES completion:nil];

}

//拍摄完成后要执行的代理方法

- (void)imagePickerController:(UIImagePickerController *)picker didFinishPickingMediaWithInfo:(NSDictionary *)info

{

NSString *mediaType = [info objectForKey:UIImagePickerControllerMediaType];

if ([mediaType isEqualToString:@"public.image"]) {

//得到照片

UIImage *image = [info objectForKey:UIImagePickerControllerOriginalImage];

image = [image scaleToSize:self.imageView.frame.size];

self.imageView.image = image;

// 异步处理, 不要占用主线程:

dispatch_async(dispatch_queue_create(0, 0), ^{

NSString *preString = [self.prediction predictWithFoodImage:image];

dispatch_async(dispatch_get_main_queue(), ^{

self.preLabel.text = preString;

});

});

}

[self dismissViewControllerAnimated:YES completion:nil];

}

//进入拍摄页面点击取消按钮

- (void)imagePickerControllerDidCancel:(UIImagePickerController *)picker

{

[self dismissViewControllerAnimated:YES completion:nil];

}