TensorFlow对Android、iOS、树莓派都提供移动端支持。
移动端应用原理。移动端、嵌入式设备应用深度学习方式，一模型运行在云端服务器，向服务器发送请求，接收服务器响应；二在本地运行模型，PC训练模型，放到移动端预测。向服务端请求数据可行性差，移动端资源稀缺。本地运行实时性更好。加速计算，内存空间和速度优化。精简模型，节省内存空间，加快计算速度。加快框架执行速度，优化模型复杂度和每步计算速度。
精简模型，用更低权得精度，量化(quantization)、权重剪枝(weight pruning，剪小权重连接，把所有权值连接低于阈值的从网络移除)。加速框架执行，优化矩阵通用乘法(GEMM)运算，影响卷积层(先数据im2col运行，再GEMM运算)和全连接层。im2col，索引图像块重排列为矩阵列。先将大矩阵重叠划分多个子矩阵，每个子矩阵序列化成向量，得到另一个矩阵。
量化(quantitative)。《How to Quantize Neural Networks with TensorFlow》https://www.tensorflow.org/performance/quantization 。离散化。用比32位浮点数更少空间存储、运行模型，TensorFlow量化实现屏蔽存储、运行细节。神经网络预测，浮点影响速度，量化加快速度，保持较高精度。减小模型文件大小。存储模型用8位整数，加载模型运算转换回32位浮点数。降低预测过程计算资源。神经网络噪声健壮笥强，量化精度损失不会危害整体准确度。训练，反向传播需要计算梯度，不能用低精度格式直接训练。PC训练浮点数模型，转8位，移动端用8位模型预测。
量化示例。GoogleNet模型转8位模型例子。下载训练好GoogleNet模型，http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz 。
bazel build tensorflow/tools/quantization:quantization_graph
bazel-bin/tensorflow/tools/quantization/quantization_graph \
--input=/tmp/classify_image_graph_def.pb \
--output_node_names="softmax" --output=/tmp/quantized_graph.pb \
--mode=eightbit
生成量化后模型大小只有原来的1/4。执行：
bazel build tensorflow/examples/label_image:label_image
bazel-bin/tensorflow/examples/label_image/label_image \
--image=/tmp/cropped_panda.jpg \
--graph=/tmp/quantized_graph.pb \
--labels=/tmp/imagenet_synset_to_human_label_map.txt \
--input_width=299 \
--input_height=299 \
--input_mean=128 \
--input_std=128 \
--input_layer="Mul:0" \
--output_layer="softmax:0"
量化过程实现。预测操作转换成等价8位版本操作实现。原始Relu操作，输入、输出浮点数。量化Relu操作，根据输入浮点数计算最大值、最小值，进入量化(Quantize)操作输入数据转换8位。保证输出层输入数据准确性，需要反量化(Dequantize)操作，权重转回32位精度，保证预测准确性。整个模型前向传播用8位整数支行，最后一层加反量化层，8位转回32位输出层输入。每个量化操作后执行反量化操作。
量化数据表示。浮点数转8位表示，是压缩问题。权重、经过激活函数处理上层输出，是分布在一个范围内的值。量化过程，找出最大值、最小值，将浮点数线性分布，做线性扩展。
优化矩阵乘法运算。谷歌开源小型独立低精度通用矩阵乘法(General Matrix to Matrix Multiplication,GEMM)库 gemmlowp。https://github.com/google/gemmlowp 。
iOS系统实践。
环境准备。操作系统Mac OS X，集成开发工具Xcode 7.3以上版本。编译TensorFlow核心静态库。tensorflow/contrib/makefiles/download_depencies.sh 。依赖库下载到tensorflow/contrib/makefile/downloads目录。eigen #C++开源矩阵计算工具。gemmlowp #小型独立低精度通用矩阵乘法(GEMM)库。googletest #谷歌开源C++测试框架。protobuf #谷歌开源数据交换格式协议。re2 #谷歌开源正则表达式库。
编译演示程度，运行。tensorflow/contrib/makefile/build_all_iso.sh。编译生成静态库，tensorflow/contrib/makefile/gen/lib：ios_ARM64、ios_ARMV7、ios_ARMV7S、ios_I386、ios_X86_64、libtensorflow-core.a。Xcode模拟器或iOS设备运行APP预测示例。TensorFlow iOS示例。https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/ios/ 。3个目录。benchmark目录是预测基准示例。simple目录是图片预测示例。camera目录是视频流实时预测示例。下载Inception V1模型，能识别1000类图片，https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip 。解压模型，复制到benchmark、simple、camera的data目录。运行目录下xcodeproj文件。选择iPhone 7 Plus模拟器，点击运行标志，编译完成点击Run Model按钮。预测结果见Xcode 控制台。
自定义模型编译、运行。https://github.com/tensorflow/tensorflow/blob/15b1cf025da5c6ac2bcf4d4878ee222fca3aec4a/tensorflow/docs_src/tutorials/image_retraining.md 。下载花卉数据 http://download.tensorflow.org/example_images/flower_photos.tgz 。郁金香(tulips)、玫瑰(roses)、浦公英(dandelion)、向日葵(sunflowers)、雏菊(daisy)5种花卉文件目录，各800张图片。
训练原始模型。下载预训练Inception V3模型 http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz 。
python tensorflow/examples/image_retraining/retrain.py \
--bottlenectk_dir=/tmp/bottlenecks/ \
--how_many_training_steps 10 \
--model_dir=/tmp/inception \
--output_graph=/tmp/retrained_graph.pb \
--output_labels=/tmp/retrained_labels.txt \
--image_dir /tmp/flower_photos
训练完成，/tmp目录有模型文件retrained_graph.pb、标签文件上retrained_labels.txt。“瓶颈”(bottlenecks)文件，描述实际分类最终输出层前一层(倒数第二层)。倒数第二层训练很好，瓶颈值是有意义紧凑图像摘要，包含足够信息使分类选择。第一次训练，retrain.py文件代码先分析所有图片，计算每张图片瓶颈值存储下来。每张图片被使用多次，不必重复计算。
编译iOS支持模型。https://petewarden.com/2016/09/27/tensorflow-for-mobile-poets/ 。原始模型到iOS模型，先去掉iOS系统不支持操作，优化模型，再将模型量化，权重变8位常数，缩小模型，最后模型内存映射。
去掉iOS系统不支持操作，优化模型。iOS版本TensorFlow仅支持预测阶段常见没有大外部依赖关系操作。支持操作列表：https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/makefile/tf_op_files.txt 。DecodeJpeg不支持，JPEG格式图片解码，依赖libjpeg。从摄像头实时识别花卉种类，直接处理相机图像缓冲区，不存JPEG文件再解码。预训练模型Inception V3 从图片数据集训练，包含DecodeJpeg操作。输入数据直接提供(feed)Decode后Mul操作，绕过Decode操作。优化加速预测，显式批处理规范化(explicit batch normalization)操作合并到卷积权重，减少计算次数。
bazel build tensorflow/python/tools:optimize_for_inference
bazel-bin/tensorflow/python/tools/optimize_for_inference \
--input=/tmp/retrained_graph.pb \
--output=/tmp/optimized_graph.pb \
--input_names=Mul \
--output_names=final_result \
label_image命令预测：
bazel-bin/tensorflow/examples/label_image/label_image \
--output_layer=final_result \
--labels=/tmp/output_labels.txt \
--image=/tmp/flower_photos/daisy/5547758_eea9edfd54_n.jpg
--graph=/tmp/output_graph.pb \
--input_layer=Mul \
--input_mean=128 \
--input_std=128 \
量化模型。苹果系统在.ipa包分发应用程度，所有应用程度资源都用zip压缩。模型权重从浮点数转整数(范围0~255)，损失准确度，小于1%。
bazel build tensorflow/tools/quantization:quantization_graph
bazel-bin/tensorflow/tools/quantization/quantization_graph \
--input=/tmp/optimized_graph.pb \
--output=/tmp/rounded_graph.pb \
--output_node_names=final_result \
--mode=weights_rounded
内存映射 memory mapping。物理内存映射到进程地址空间内，应用程序直接用输入/输出地址空间，提高读写效率。模型全部一次性加载到内存缓冲区，会对iOS RAM施加过大压力，操作系统会杀死内存占用过多程序。模型权值缓冲区只读，可映射到内存。重新排列模型，权重分部分逐块从主GraphDef加载到内存。
bazel build tensorflow/contrib/util:convert_graphdef_memmapped_format
bazel-bin/tensorflow/contrib/util/convert_graphdef_memmapped_format \
--in_graph=/tmp/rounded_graph.pb \
--out_graph=/tmp/mmapped_graph.pb
生成iOS工程文件运行。视频流实进预测演示程序例子。https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/ios/camera 。模型文件、标记文件复制到data目录。修改CameraExampleViewController.mm，更改加载模型文件名称、输入图片尺寸、操作节点名字、缩放像素大小。
#import
#import
#import
#import
#import "CameraExampleViewController.h"
#include
#include "tensorflow_utils.h"
// If you have your own model, modify this to the file name, and make sure
// you've added the file to your app resources too.
static NSString* model_file_name = @"tensorflow_inception_graph";
static NSString* model_file_type = @"pb";
// This controls whether we'll be loading a plain GraphDef proto, or a
// file created by the convert_graphdef_memmapped_format utility that wraps a
// GraphDef and parameter file that can be mapped into memory from file to
// reduce overall memory usage.
const bool model_uses_memory_mapping = false;
// If you have your own model, point this to the labels file.
static NSString* labels_file_name = @"imagenet_comp_graph_label_strings";
static NSString* labels_file_type = @"txt";
// These dimensions need to match those the model was trained with.
// 以下尺寸需要和模型训练时相匹配
const int wanted_input_width =299;// 224;
const int wanted_input_height = 299;//224;
const int wanted_input_channels = 3;
const float input_mean = 128.0f;//117.0f;
const float input_std = 128.0f;//1.0f;
const std::string input_layer_name = "Mul";//"input";
const std::string output_layer_name = "final_result";//"softmax1";
static void *AVCaptureStillImageIsCapturingStillImageContext =
&AVCaptureStillImageIsCapturingStillImageContext;
@interface CameraExampleViewController (InternalMethods)
- (void)setupAVCapture;
- (void)teardownAVCapture;
@end
@implementation CameraExampleViewController
- (void)setupAVCapture {
NSError *error = nil;
session = [AVCaptureSession new];
if ([[UIDevice currentDevice] userInterfaceIdiom] ==
UIUserInterfaceIdiomPhone)
[session setSessionPreset:AVCaptureSessionPreset640x480];
else
[session setSessionPreset:AVCaptureSessionPresetPhoto];
AVCaptureDevice *device =
[AVCaptureDevice defaultDeviceWithMediaType:AVMediaTypeVideo];
AVCaptureDeviceInput *deviceInput =
[AVCaptureDeviceInput deviceInputWithDevice:device error:&error];
assert(error == nil);
isUsingFrontFacingCamera = NO;
if ([session canAddInput:deviceInput]) [session addInput:deviceInput];
stillImageOutput = [AVCaptureStillImageOutput new];
[stillImageOutput
addObserver:self
forKeyPath:@"capturingStillImage"
options:NSKeyValueObservingOptionNew
context:(void *)(AVCaptureStillImageIsCapturingStillImageContext)];
if ([session canAddOutput:stillImageOutput])
[session addOutput:stillImageOutput];
videoDataOutput = [AVCaptureVideoDataOutput new];
NSDictionary *rgbOutputSettings = [NSDictionary
dictionaryWithObject:[NSNumber numberWithInt:kCMPixelFormat_32BGRA]
forKey:(id)kCVPixelBufferPixelFormatTypeKey];
[videoDataOutput setVideoSettings:rgbOutputSettings];
[videoDataOutput setAlwaysDiscardsLateVideoFrames:YES];
videoDataOutputQueue =
dispatch_queue_create("VideoDataOutputQueue", DISPATCH_QUEUE_SERIAL);
[videoDataOutput setSampleBufferDelegate:self queue:videoDataOutputQueue];
if ([session canAddOutput:videoDataOutput])
[session addOutput:videoDataOutput];
[[videoDataOutput connectionWithMediaType:AVMediaTypeVideo] setEnabled:YES];
previewLayer = [[AVCaptureVideoPreviewLayer alloc] initWithSession:session];
[previewLayer setBackgroundColor:[[UIColor blackColor] CGColor]];
[previewLayer setVideoGravity:AVLayerVideoGravityResizeAspect];
CALayer *rootLayer = [previewView layer];
[rootLayer setMasksToBounds:YES];
[previewLayer setFrame:[rootLayer bounds]];
[rootLayer addSublayer:previewLayer];
[session startRunning];
if (error) {
NSString *title = [NSString stringWithFormat:@"Failed with error %d", (int)[error code]];
UIAlertController *alertController =
[UIAlertController alertControllerWithTitle:title
message:[error localizedDescription]
preferredStyle:UIAlertControllerStyleAlert];
UIAlertAction *dismiss =
[UIAlertAction actionWithTitle:@"Dismiss" style:UIAlertActionStyleDefault handler:nil];
[alertController addAction:dismiss];
[self presentViewController:alertController animated:YES completion:nil];
[self teardownAVCapture];
}
}
- (void)teardownAVCapture {
[stillImageOutput removeObserver:self forKeyPath:@"isCapturingStillImage"];
[previewLayer removeFromSuperlayer];
}
- (void)observeValueForKeyPath:(NSString *)keyPath
ofObject:(id)object
change:(NSDictionary *)change
context:(void *)context {
if (context == AVCaptureStillImageIsCapturingStillImageContext) {
BOOL isCapturingStillImage =
[[change objectForKey:NSKeyValueChangeNewKey] boolValue];
if (isCapturingStillImage) {
// do flash bulb like animation
flashView = [[UIView alloc] initWithFrame:[previewView frame]];
[flashView setBackgroundColor:[UIColor whiteColor]];
[flashView setAlpha:0.f];
[[[self view] window] addSubview:flashView];
[UIView animateWithDuration:.4f
animations:^{
[flashView setAlpha:1.f];
}];
} else {
[UIView animateWithDuration:.4f
animations:^{
[flashView setAlpha:0.f];
}
completion:^(BOOL finished) {
[flashView removeFromSuperview];
flashView = nil;
}];
}
}
}
- (AVCaptureVideoOrientation)avOrientationForDeviceOrientation:
(UIDeviceOrientation)deviceOrientation {
AVCaptureVideoOrientation result =
(AVCaptureVideoOrientation)(deviceOrientation);
if (deviceOrientation == UIDeviceOrientationLandscapeLeft)
result = AVCaptureVideoOrientationLandscapeRight;
else if (deviceOrientation == UIDeviceOrientationLandscapeRight)
result = AVCaptureVideoOrientationLandscapeLeft;
return result;
}
- (IBAction)takePicture:(id)sender {
if ([session isRunning]) {
[session stopRunning];
[sender setTitle:@"Continue" forState:UIControlStateNormal];
flashView = [[UIView alloc] initWithFrame:[previewView frame]];
[flashView setBackgroundColor:[UIColor whiteColor]];
[flashView setAlpha:0.f];
[[[self view] window] addSubview:flashView];
[UIView animateWithDuration:.2f
animations:^{
[flashView setAlpha:1.f];
}
completion:^(BOOL finished) {
[UIView animateWithDuration:.2f
animations:^{
[flashView setAlpha:0.f];
}
completion:^(BOOL finished) {
[flashView removeFromSuperview];
flashView = nil;
}];
}];
} else {
[session startRunning];
[sender setTitle:@"Freeze Frame" forState:UIControlStateNormal];
}
}
+ (CGRect)videoPreviewBoxForGravity:(NSString *)gravity
frameSize:(CGSize)frameSize
apertureSize:(CGSize)apertureSize {
CGFloat apertureRatio = apertureSize.height / apertureSize.width;
CGFloat viewRatio = frameSize.width / frameSize.height;
CGSize size = CGSizeZero;
if ([gravity isEqualToString:AVLayerVideoGravityResizeAspectFill]) {
if (viewRatio > apertureRatio) {
size.width = frameSize.width;
size.height =
apertureSize.width * (frameSize.width / apertureSize.height);
} else {
size.width =
apertureSize.height * (frameSize.height / apertureSize.width);
size.height = frameSize.height;
}
} else if ([gravity isEqualToString:AVLayerVideoGravityResizeAspect]) {
if (viewRatio > apertureRatio) {
size.width =
apertureSize.height * (frameSize.height / apertureSize.width);
size.height = frameSize.height;
} else {
size.width = frameSize.width;
size.height =
apertureSize.width * (frameSize.width / apertureSize.height);
}
} else if ([gravity isEqualToString:AVLayerVideoGravityResize]) {
size.width = frameSize.width;
size.height = frameSize.height;
}
CGRect videoBox;
videoBox.size = size;
if (size.width < frameSize.width)
videoBox.origin.x = (frameSize.width - size.width) / 2;
else
videoBox.origin.x = (size.width - frameSize.width) / 2;
if (size.height < frameSize.height)
videoBox.origin.y = (frameSize.height - size.height) / 2;
else
videoBox.origin.y = (size.height - frameSize.height) / 2;
return videoBox;
}
- (void)captureOutput:(AVCaptureOutput *)captureOutput
didOutputSampleBuffer:(CMSampleBufferRef)sampleBuffer
fromConnection:(AVCaptureConnection *)connection {
CVPixelBufferRef pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer);
CFRetain(pixelBuffer);
[self runCNNOnFrame:pixelBuffer];
CFRelease(pixelBuffer);
}
- (void)runCNNOnFrame:(CVPixelBufferRef)pixelBuffer {
assert(pixelBuffer != NULL);
OSType sourcePixelFormat = CVPixelBufferGetPixelFormatType(pixelBuffer);
int doReverseChannels;
if (kCVPixelFormatType_32ARGB == sourcePixelFormat) {
doReverseChannels = 1;
} else if (kCVPixelFormatType_32BGRA == sourcePixelFormat) {
doReverseChannels = 0;
} else {
assert(false); // Unknown source format
}
const int sourceRowBytes = (int)CVPixelBufferGetBytesPerRow(pixelBuffer);
const int image_width = (int)CVPixelBufferGetWidth(pixelBuffer);
const int fullHeight = (int)CVPixelBufferGetHeight(pixelBuffer);
CVPixelBufferLockFlags unlockFlags = kNilOptions;
CVPixelBufferLockBaseAddress(pixelBuffer, unlockFlags);
unsigned char *sourceBaseAddr =
(unsigned char *)(CVPixelBufferGetBaseAddress(pixelBuffer));
int image_height;
unsigned char *sourceStartAddr;
if (fullHeight <= image_width) {
image_height = fullHeight;
sourceStartAddr = sourceBaseAddr;
} else {
image_height = image_width;
const int marginY = ((fullHeight - image_width) / 2);
sourceStartAddr = (sourceBaseAddr + (marginY * sourceRowBytes));
}
const int image_channels = 4;
assert(image_channels >= wanted_input_channels);
tensorflow::Tensor image_tensor(
tensorflow::DT_FLOAT,
tensorflow::TensorShape(
{1, wanted_input_height, wanted_input_width, wanted_input_channels}));
auto image_tensor_mapped = image_tensor.tensor();
tensorflow::uint8 *in = sourceStartAddr;
float *out = image_tensor_mapped.data();
for (int y = 0; y < wanted_input_height; ++y) {
float *out_row = out + (y * wanted_input_width * wanted_input_channels);
for (int x = 0; x < wanted_input_width; ++x) {
const int in_x = (y * image_width) / wanted_input_width;
const int in_y = (x * image_height) / wanted_input_height;
tensorflow::uint8 *in_pixel =
in + (in_y * image_width * image_channels) + (in_x * image_channels);
float *out_pixel = out_row + (x * wanted_input_channels);
for (int c = 0; c < wanted_input_channels; ++c) {
out_pixel[c] = (in_pixel[c] - input_mean) / input_std;
}
}
}
CVPixelBufferUnlockBaseAddress(pixelBuffer, unlockFlags);
if (tf_session.get()) {
std::vector outputs;
tensorflow::Status run_status = tf_session->Run(
{{input_layer_name, image_tensor}}, {output_layer_name}, {}, &outputs);
if (!run_status.ok()) {
LOG(ERROR) << "Running model failed:" << run_status;
} else {
tensorflow::Tensor *output = &outputs[0];
auto predictions = output->flat();
NSMutableDictionary *newValues = [NSMutableDictionary dictionary];
for (int index = 0; index < predictions.size(); index += 1) {
const float predictionValue = predictions(index);
if (predictionValue > 0.05f) {
std::string label = labels[index % predictions.size()];
NSString *labelObject = [NSString stringWithUTF8String:label.c_str()];
NSNumber *valueObject = [NSNumber numberWithFloat:predictionValue];
[newValues setObject:valueObject forKey:labelObject];
}
}
dispatch_async(dispatch_get_main_queue(), ^(void) {
[self setPredictionValues:newValues];
});
}
}
CVPixelBufferUnlockBaseAddress(pixelBuffer, 0);
}
- (void)dealloc {
[self teardownAVCapture];
}
// use front/back camera
- (IBAction)switchCameras:(id)sender {
AVCaptureDevicePosition desiredPosition;
if (isUsingFrontFacingCamera)
desiredPosition = AVCaptureDevicePositionBack;
else
desiredPosition = AVCaptureDevicePositionFront;
for (AVCaptureDevice *d in
[AVCaptureDevice devicesWithMediaType:AVMediaTypeVideo]) {
if ([d position] == desiredPosition) {
[[previewLayer session] beginConfiguration];
AVCaptureDeviceInput *input =
[AVCaptureDeviceInput deviceInputWithDevice:d error:nil];
for (AVCaptureInput *oldInput in [[previewLayer session] inputs]) {
[[previewLayer session] removeInput:oldInput];
}
[[previewLayer session] addInput:input];
[[previewLayer session] commitConfiguration];
break;
}
}
isUsingFrontFacingCamera = !isUsingFrontFacingCamera;
}
- (void)didReceiveMemoryWarning {
[super didReceiveMemoryWarning];
}
- (void)viewDidLoad {
[super viewDidLoad];
square = [UIImage imageNamed:@"squarePNG"];
synth = [[AVSpeechSynthesizer alloc] init];
labelLayers = [[NSMutableArray alloc] init];
oldPredictionValues = [[NSMutableDictionary alloc] init];
tensorflow::Status load_status;
if (model_uses_memory_mapping) {
load_status = LoadMemoryMappedModel(
model_file_name, model_file_type, &tf_session, &tf_memmapped_env);
} else {
load_status = LoadModel(model_file_name, model_file_type, &tf_session);
}
if (!load_status.ok()) {
LOG(FATAL) << "Couldn't load model: " << load_status;
}
tensorflow::Status labels_status =
LoadLabels(labels_file_name, labels_file_type, &labels);
if (!labels_status.ok()) {
LOG(FATAL) << "Couldn't load labels: " << labels_status;
}
[self setupAVCapture];
}
- (void)viewDidUnload {
[super viewDidUnload];
}
- (void)viewWillAppear:(BOOL)animated {
[super viewWillAppear:animated];
}
- (void)viewDidAppear:(BOOL)animated {
[super viewDidAppear:animated];
}
- (void)viewWillDisappear:(BOOL)animated {
[super viewWillDisappear:animated];
}
- (void)viewDidDisappear:(BOOL)animated {
[super viewDidDisappear:animated];
}
- (BOOL)shouldAutorotateToInterfaceOrientation:
(UIInterfaceOrientation)interfaceOrientation {
return (interfaceOrientation == UIInterfaceOrientationPortrait);
}
- (BOOL)prefersStatusBarHidden {
return YES;
}
- (void)setPredictionValues:(NSDictionary *)newValues {
const float decayValue = 0.75f;
const float updateValue = 0.25f;
const float minimumThreshold = 0.01f;
NSMutableDictionary *decayedPredictionValues =
[[NSMutableDictionary alloc] init];
for (NSString *label in oldPredictionValues) {
NSNumber *oldPredictionValueObject =
[oldPredictionValues objectForKey:label];
const float oldPredictionValue = [oldPredictionValueObject floatValue];
const float decayedPredictionValue = (oldPredictionValue * decayValue);
if (decayedPredictionValue > minimumThreshold) {
NSNumber *decayedPredictionValueObject =
[NSNumber numberWithFloat:decayedPredictionValue];
[decayedPredictionValues setObject:decayedPredictionValueObject
forKey:label];
}
}
oldPredictionValues = decayedPredictionValues;
for (NSString *label in newValues) {
NSNumber *newPredictionValueObject = [newValues objectForKey:label];
NSNumber *oldPredictionValueObject =
[oldPredictionValues objectForKey:label];
if (!oldPredictionValueObject) {
oldPredictionValueObject = [NSNumber numberWithFloat:0.0f];
}
const float newPredictionValue = [newPredictionValueObject floatValue];
const float oldPredictionValue = [oldPredictionValueObject floatValue];
const float updatedPredictionValue =
(oldPredictionValue + (newPredictionValue * updateValue));
NSNumber *updatedPredictionValueObject =
[NSNumber numberWithFloat:updatedPredictionValue];
[oldPredictionValues setObject:updatedPredictionValueObject forKey:label];
}
NSArray *candidateLabels = [NSMutableArray array];
for (NSString *label in oldPredictionValues) {
NSNumber *oldPredictionValueObject =
[oldPredictionValues objectForKey:label];
const float oldPredictionValue = [oldPredictionValueObject floatValue];
if (oldPredictionValue > 0.05f) {
NSDictionary *entry = @{
@"label" : label,
@"value" : oldPredictionValueObject
};
candidateLabels = [candidateLabels arrayByAddingObject:entry];
}
}
NSSortDescriptor *sort =
[NSSortDescriptor sortDescriptorWithKey:@"value" ascending:NO];
NSArray *sortedLabels = [candidateLabels
sortedArrayUsingDescriptors:[NSArray arrayWithObject:sort]];
const float leftMargin = 10.0f;
const float topMargin = 10.0f;
const float valueWidth = 48.0f;
const float valueHeight = 26.0f;
const float labelWidth = 246.0f;
const float labelHeight = 26.0f;
const float labelMarginX = 5.0f;
const float labelMarginY = 5.0f;
[self removeAllLabelLayers];
int labelCount = 0;
for (NSDictionary *entry in sortedLabels) {
NSString *label = [entry objectForKey:@"label"];
NSNumber *valueObject = [entry objectForKey:@"value"];
const float value = [valueObject floatValue];
const float originY =
(topMargin + ((labelHeight + labelMarginY) * labelCount));
const int valuePercentage = (int)roundf(value * 100.0f);
const float valueOriginX = leftMargin;
NSString *valueText = [NSString stringWithFormat:@"%d%%", valuePercentage];
[self addLabelLayerWithText:valueText
originX:valueOriginX
originY:originY
width:valueWidth
height:valueHeight
alignment:kCAAlignmentRight];
const float labelOriginX = (leftMargin + valueWidth + labelMarginX);
[self addLabelLayerWithText:[label capitalizedString]
originX:labelOriginX
originY:originY
width:labelWidth
height:labelHeight
alignment:kCAAlignmentLeft];
if ((labelCount == 0) && (value > 0.5f)) {
[self speak:[label capitalizedString]];
}
labelCount += 1;
if (labelCount > 4) {
break;
}
}
}
- (void)removeAllLabelLayers {
for (CATextLayer *layer in labelLayers) {
[layer removeFromSuperlayer];
}
[labelLayers removeAllObjects];
}
- (void)addLabelLayerWithText:(NSString *)text
originX:(float)originX
originY:(float)originY
width:(float)width
height:(float)height
alignment:(NSString *)alignment {
CFTypeRef font = (CFTypeRef) @"Menlo-Regular";
const float fontSize = 20.0f;
const float marginSizeX = 5.0f;
const float marginSizeY = 2.0f;
const CGRect backgroundBounds = CGRectMake(originX, originY, width, height);
const CGRect textBounds =
CGRectMake((originX + marginSizeX), (originY + marginSizeY),
(width - (marginSizeX * 2)), (height - (marginSizeY * 2)));
CATextLayer *background = [CATextLayer layer];
[background setBackgroundColor:[UIColor blackColor].CGColor];
[background setOpacity:0.5f];
[background setFrame:backgroundBounds];
background.cornerRadius = 5.0f;
[[self.view layer] addSublayer:background];
[labelLayers addObject:background];
CATextLayer *layer = [CATextLayer layer];
[layer setForegroundColor:[UIColor whiteColor].CGColor];
[layer setFrame:textBounds];
[layer setAlignmentMode:alignment];
[layer setWrapped:YES];
[layer setFont:font];
[layer setFontSize:fontSize];
layer.contentsScale = [[UIScreen mainScreen] scale];
[layer setString:text];
[[self.view layer] addSublayer:layer];
[labelLayers addObject:layer];
}
- (void)setPredictionText:(NSString *)text withDuration:(float)duration {
if (duration > 0.0) {
CABasicAnimation *colorAnimation =
[CABasicAnimation animationWithKeyPath:@"foregroundColor"];
colorAnimation.duration = duration;
colorAnimation.fillMode = kCAFillModeForwards;
colorAnimation.removedOnCompletion = NO;
colorAnimation.fromValue = (id)[UIColor darkGrayColor].CGColor;
colorAnimation.toValue = (id)[UIColor whiteColor].CGColor;
colorAnimation.timingFunction =
[CAMediaTimingFunction functionWithName:kCAMediaTimingFunctionLinear];
[self.predictionTextLayer addAnimation:colorAnimation
forKey:@"colorAnimation"];
} else {
self.predictionTextLayer.foregroundColor = [UIColor whiteColor].CGColor;
}
[self.predictionTextLayer removeFromSuperlayer];
[[self.view layer] addSublayer:self.predictionTextLayer];
[self.predictionTextLayer setString:text];
}
- (void)speak:(NSString *)words {
if ([synth isSpeaking]) {
return;
}
AVSpeechUtterance *utterance =
[AVSpeechUtterance speechUtteranceWithString:words];
utterance.voice = [AVSpeechSynthesisVoice voiceWithLanguage:@"en-US"];
utterance.rate = 0.75 * AVSpeechUtteranceDefaultSpeechRate;
[synth speakUtterance:utterance];
}
@end
连上iPhone手机，双击tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj编译运行。手机安装好APP，打开APP，找到玫瑰花识别。训练迭代次数10000次后，识别率99%以上。模拟器打包，生成打包工程文件位于/Users/libinggen/Library/Developer/Xcode/DeriveData/camera_example-dhfdsdfesfmrwtfb1fpfkfjsdfhdskf/Build/Products/Debug-iphoneos。打开CameraExample.app，有可执行文件CameraExample、资源文件模型文件mmapped_graph.pb、标记文件retrained_labels.txt。
Android系统实践。
环境准备。MacBook Pro。Oracle官网下载JDK1.8版本。http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html 。jdk-8u111-macosx-x64.dmg。双击安装。设置Java环境变量：
JAVA_HOME='/usr/libexec/java_home'
export JAVA_HOME
搭建Android SDK环境。Android官网下载Android SDK，https://developer.android.com 。25.0.2版本。android-sdk_r25.0.2-macosx.zip。解压到~/Library/Android/sdk目录。build-tools、extras、patcher、platform-tools #各版本SDK 根据API Level划分SDK版本、platforms、sources、system-images、temp #临时文件夹 在SDK更新安装时用到、tools #各版本通用SDK工具 有adb、aapt、aidl、dx文件。
搭建Android NDK环境。Android官网下载Android NDK Mac OS X版本，https://developer.android.com/ndk/downloads/index.html 。android-ndk-r13b-darwin-x86_64.zip文件。解压，CHANGELOG.md、build、ndk-build、ndk-depends、ndk-gdb、ndk-stack、ndk-which、platforms、prebuilt、python-packages、shader-tools、simpleperf、source.properties、sources、toolchains。搭建Bazel。brew安装bazel:
brew install bazel
更新bazel:
brew upgrade bazel
编译演示程序运行。修改tensorflow-1.1.0根目录WORKSPACE文件。android_sdk_repository、android_ndk_repository配置改为用户自己安装目录、版本。
android_sdk_repository(
name = "androidsdk",
api_level = 25,
build_tools_version = "25.0.2",
# Replace with path to Android SDK on your system
path = "~/Library/Android/sdk"
)
android_ndk_repository(
name = "androidndk",
api_level = 23,
path = "~/Downloads/android-ndk-r13b"
)
在根目录用bazel构建：
bazel build // tensorflow/examples/android:tensorflow_demo
编译成功，默认在tensorflow-1.1.0/bazel-bin/tensorflow/examples/android目录生成TensorFlow演示程序。
运行。生成apk文件传输到手机，手机摄像头看效果。Android 6.0.1。开启“开发者模式”。手机用数据线与计算机相连，进入SDK所在目录，进入platform-tools文件夹，找到adb命令，执行：
./adb install tensorflow-0.12/bazel-bin/tensorflow/examples/android/tensorflow_demo.apk
tensorflow_demo.apk自动安装到手机。打开TF Detec App。App 调起手机摄像头，摄像头返回数据流实时监测。
自定义模型编译运行。训练原始模型、编译Android系统支持模型、生成Android apk文件运行。
训练原始模型、编译Android系统支持模型。用项目根目录tensorflow/python/tools/optimize_for_inference.py、tensorflow/tools/quantization/quantize_graph.py、tensorflow/contrib/util/convert_graphdef_memmapped_format.cc对模型优化。将第一步生成原始模型文件retrained_graph.pb、标记文件retrained_labels.txt放在tensorflow/examples/android/assets目录。修改tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowImageClassifier.java要加载模型文件名称，输入图片尺寸、操作节点名字、缩放像素大小。
package org.tensorflow.demo;
import android.content.res.AssetManager;
import android.graphics.Bitmap;
import android.os.Trace;
import android.util.Log;
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;
import java.util.PriorityQueue;
import java.util.Vector;
import org.tensorflow.Operation;
import org.tensorflow.contrib.android.TensorFlowInferenceInterface;
/** A classifier specialized to label images using TensorFlow. */
public class TensorFlowImageClassifier implements Classifier {
private static final String TAG = "TensorFlowImageClassifier";
// Only return this many results with at least this confidence.
private static final int MAX_RESULTS = 3;
private static final float THRESHOLD = 0.1f;
// Config values.
private String inputName;
private String outputName;
private int inputSize;
private int imageMean;
private float imageStd;
// Pre-allocated buffers.
private Vector labels = new Vector();
private int[] intValues;
private float[] floatValues;
private float[] outputs;
private String[] outputNames;
private boolean logStats = false;
private TensorFlowInferenceInterface inferenceInterface;
private TensorFlowImageClassifier() {}
/**
* Initializes a native TensorFlow session for classifying images.
*
* @param assetManager The asset manager to be used to load assets.
* @param modelFilename The filepath of the model GraphDef protocol buffer.
* @param labelFilename The filepath of label file for classes.
* @param inputSize The input size. A square image of inputSize x inputSize is assumed.
* @param imageMean The assumed mean of the image values.
* @param imageStd The assumed std of the image values.
* @param inputName The label of the image input node.
* @param outputName The label of the output node.
* @throws IOException
*/
public static Classifier create(
AssetManager assetManager,
String modelFilename,
String labelFilename,
int inputSize,
int imageMean,
float imageStd,
String inputName,
String outputName) {
TensorFlowImageClassifier c = new TensorFlowImageClassifier();
c.inputName = inputName;
c.outputName = outputName;
// Read the label names into memory.
// TODO(andrewharp): make this handle non-assets.
String actualFilename = labelFilename.split("file:///android_asset/")[1];
Log.i(TAG, "Reading labels from: " + actualFilename);
BufferedReader br = null;
try {
br = new BufferedReader(new InputStreamReader(assetManager.open(actualFilename)));
String line;
while ((line = br.readLine()) != null) {
c.labels.add(line);
}
br.close();
} catch (IOException e) {
throw new RuntimeException("Problem reading label file!" , e);
}
c.inferenceInterface = new TensorFlowInferenceInterface(assetManager, modelFilename);
// The shape of the output is [N, NUM_CLASSES], where N is the batch size.
final Operation operation = c.inferenceInterface.graphOperation(outputName);
final int numClasses = (int) operation.output(0).shape().size(1);
Log.i(TAG, "Read " + c.labels.size() + " labels, output layer size is " + numClasses);
// Ideally, inputSize could have been retrieved from the shape of the input operation. Alas,
// the placeholder node for input in the graphdef typically used does not specify a shape, so it
// must be passed in as a parameter.
c.inputSize = inputSize;
c.imageMean = imageMean;
c.imageStd = imageStd;
// Pre-allocate buffers.
c.outputNames = new String[] {outputName};
c.intValues = new int[inputSize * inputSize];
c.floatValues = new float[inputSize * inputSize * 3];
c.outputs = new float[numClasses];
return c;
}
@Override
public List recognizeImage(final Bitmap bitmap) {
// Log this method so that it can be analyzed with systrace.
Trace.beginSection("recognizeImage");
Trace.beginSection("preprocessBitmap");
// Preprocess the image data from 0-255 int to normalized float based
// on the provided parameters.
bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
for (int i = 0; i < intValues.length; ++i) {
final int val = intValues[i];
floatValues[i * 3 + 0] = (((val >> 16) & 0xFF) - imageMean) / imageStd;
floatValues[i * 3 + 1] = (((val >> 8) & 0xFF) - imageMean) / imageStd;
floatValues[i * 3 + 2] = ((val & 0xFF) - imageMean) / imageStd;
}
Trace.endSection();
// Copy the input data into TensorFlow.
Trace.beginSection("feed");
inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3);
Trace.endSection();
// Run the inference call.
Trace.beginSection("run");
inferenceInterface.run(outputNames, logStats);
Trace.endSection();
// Copy the output Tensor back into the output array.
Trace.beginSection("fetch");
inferenceInterface.fetch(outputName, outputs);
Trace.endSection();
// Find the best classifications.
PriorityQueue pq =
new PriorityQueue(
3,
new Comparator() {
@Override
public int compare(Recognition lhs, Recognition rhs) {
// Intentionally reversed to put high confidence at the head of the queue.
return Float.compare(rhs.getConfidence(), lhs.getConfidence());
}
});
for (int i = 0; i < outputs.length; ++i) {
if (outputs[i] > THRESHOLD) {
pq.add(
new Recognition(
"" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i], null));
}
}
final ArrayList recognitions = new ArrayList();
int recognitionsSize = Math.min(pq.size(), MAX_RESULTS);
for (int i = 0; i < recognitionsSize; ++i) {
recognitions.add(pq.poll());
}
Trace.endSection(); // "recognizeImage"
return recognitions;
}
@Override
public void enableStatLogging(boolean logStats) {
this.logStats = logStats;
}
@Override
public String getStatString() {
return inferenceInterface.getStatString();
}
@Override
public void close() {
inferenceInterface.close();
}
}
重新编译apk，连接Android手机，安装apk：
bazel buld //tensorflow/examples/android:tensorflow_demo
adb install -r -g bazel-bin/tensorflow/examples/android/tensorflow_demo.apk
树莓派实践。
Tensorflow可以在树莓派(Raspberry Pi)运行。树莓派，只有信用卡大小微型电脑，系统基于Linux，有音频、视频功能。应用，输入1万张自己的面部图片，在树莓派训练人脸识别模型，教会它认识你，你进入家门后，帮你开灯、播放音乐各种功能。树莓派编译方法和直接在Linux环境上用相似。
参考资料：
《TensorFlow技术解析与实战》
欢迎推荐上海机器学习工作机会，我的微信：qingxingfengzi