网站设计对网站搜索引擎友好性的影响,百度推广怎么优化关键词的质量,京东云建站,pacharm做腾讯视频网站1 原理
tflite就是Tensorflow的轻量化模型#xff0c;核心处理就是量化和剪枝。不过这部分目前是在Tensorflow中封装了#xff0c;所以这里也不会去看细节#xff0c;主要就是看看原理和使用方法。
量化Quantization#xff0c;其实就是把原来的float32换成int8。这样一个…1 原理
tflite就是Tensorflow的轻量化模型核心处理就是量化和剪枝。不过这部分目前是在Tensorflow中封装了所以这里也不会去看细节主要就是看看原理和使用方法。
量化Quantization其实就是把原来的float32换成int8。这样一个是减少了模型中的参数大小理论上缩减到之前的1/4另外也加快了运算。换算就是把之前的浮点数映射到int8的256个格子里面。 原因是根据分析大部分的权重都分布在一些相对狭窄的区域也就是说很多区域其实是浪费了。所以可以压缩这也是压缩的理论基础。 测试后量化的数据运行速度基本能达到之前的1/2甚至1/4。 剪枝pruning则是去掉一些不必要的神经元。这样可以减少体积和加快推理速度具体怎么剪的目前不知道。。。 2 练习
2.1 线性回归
练习https://colab.research.google.com/github/tinyMLx/colabs/blob/master/3-3-4-TFLiteConverter.ipynb
整理的代码
import tensorflow as tf
if tf.__version__ ! 2.14.0:print(fCurrent TensorFlow version: {tf.__version__}, switching to 2.14.0)# Uninstall current TensorFlow version!pip uninstall -y tensorflow# Install TensorFlow 2.10!pip install numpy1.26 --force-reinstall!pip install tensorflow2.14.0# After installation, restart runtimeprint(TensorFlow 2.14.0 installed.)print(Please click on the Runtime Restart session and run all.)
else:print(TensorFlow 2.14.0 is already installed.)import tensorflow as tf
import numpy as np
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Densel0 Dense(units1, input_shape[1])
model Sequential([l0])
model.compile(optimizersgd, lossmean_squared_error)xs np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtypefloat)
ys np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtypefloat)model.fit(xs, ys, epochs500)print(model.predict(np.array([10.0])))
print(Here is what I learned: {}.format(l0.get_weights()))export_dir saved_model/1
tf.saved_model.save(model, export_dir)# Convert the model.
converter tf.lite.TFLiteConverter.from_saved_model(export_dir)
tflite_model converter.convert()import pathlib
tflite_model_file pathlib.Path(model.tflite)
tflite_model_file.write_bytes(tflite_model)# Load TFLite model and allocate tensors.
interpreter tf.lite.Interpreter(model_contenttflite_model)
interpreter.allocate_tensors()# Get input and output tensors.
input_details interpreter.get_input_details()
output_details interpreter.get_output_details()
print(input_details)
print(output_details)to_predict np.array([[10.0]], dtypenp.float32)
print(to_predict)
interpreter.set_tensor(input_details[0][index], to_predict)
interpreter.invoke()
tflite_results interpreter.get_tensor(output_details[0][index])
print(tflite_results)
因为里面指定了版本是2.14.0但是Colab上面已经没有2.14.0了。后面改成2.16倒是可以安装但是会在后面报错所以这次只看代码了。 核心就是三个地方。
转换
# Convert the model.
converter tf.lite.TFLiteConverter.from_saved_model(export_dir)
tflite_model converter.convert()
加载
interpreter tf.lite.Interpreter(model_contenttflite_model)
interpreter.allocate_tensors()
推理
interpreter.set_tensor(input_details[0][index], to_predict)
interpreter.invoke() 2.2 猫狗识别
练习代码是在https://colab.research.google.com/github/tinyMLx/colabs/blob/master/3-3-7-RunningTFLiteModels.ipynb
本来是想运行的但是和上面一样Google的colab用着确实有点问题更换了库之后死活也无法重启colab。所以只能手搓代码学习了。。。
代码整理
tf_version 2.14.0
hub_version 0.15.0
datasets_version 4.6.0
numpy_version 1.26.4
protobuf_version 3.20.3import tensorflow as tf
import numpy as nptry:import tensorflow_hub as hubimport tensorflow_datasets as tfds
except:hub Nonetfds None# Check if versions match
if (tf.__version__ ! tf_version or (hub and hub.__version__ ! hub_version) or (tfds and tfds.__version__ ! datasets_version) or not np.__version__.startswith(numpy_version)):print(fCurrent TensorFlow version: {tf.__version__} → {tf_version})if hub: print(fCurrent TensorFlow Hub version: {hub.__version__} → {hub_version})if tfds: print(fCurrent TensorFlow Datasets version: {tfds.__version__} → {datasets_version})print(fCurrent NumPy version: {np.__version__} → {numpy_version})# Uninstall old versions!pip uninstall -y tensorflow tensorflow_hub tensorflow_datasets numpy protobuf# Install specific versions!pip install tensorflow{tf_version} \tensorflow_hub{hub_version} \tensorflow_datasets{datasets_version} \numpy{numpy_version} \protobuf{protobuf_version}# Prompt user to restartprint(\n Specified versions installed successfully.)print( Please restart the runtime (Runtime Restart session) and re-run the notebook.\n)
else:print( All packages are already at the specified versions.)import numpy as np
import matplotlib.pylab as pltimport tensorflow as tf
import tensorflow_hub as hub
import tensorflow_datasets as tfds
setattr(tfds.image_classification.cats_vs_dogs, _URL,https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip)def format_image(image, label):image tf.image.resize(image, (224, 224)) / 255.0return image, label(raw_train, raw_validation, raw_test), metadata tfds.load(cats_vs_dogs,split[train[:80%], train[80%:90%], train[90%:]],with_infoTrue,as_supervisedTrue,
)num_examples metadata.splits[train].num_examples
num_classes metadata.features[label].num_classes
print(num_examples)
print(num_classes)BATCH_SIZE 32
train_batches raw_train.shuffle(num_examples // 4).map(format_image).batch(BATCH_SIZE).prefetch(1)
validation_batches raw_validation.map(format_image).batch(BATCH_SIZE).prefetch(1)
test_batches raw_test.map(format_image).batch(1)for image_batch, label_batch in train_batches.take(1):passimage_batch.shapemodule_selection (mobilenet_v2, 224, 1280)
handle_base, pixels, FV_SIZE module_selection
MODULE_HANDLE https://tfhub.dev/google/tf2-preview/{}/feature_vector/4.format(handle_base)
IMAGE_SIZE (pixels, pixels)
print(Using {} with input size {} and output dimension {}.format(MODULE_HANDLE, IMAGE_SIZE, FV_SIZE))feature_extractor hub.KerasLayer(MODULE_HANDLE,input_shapeIMAGE_SIZE (3,), output_shape[FV_SIZE],trainableFalse)print(Building model with, MODULE_HANDLE)model tf.keras.Sequential([feature_extractor,tf.keras.layers.Dense(num_classes, activationsoftmax)
])model.summary()model.compile(optimizeradam,losssparse_categorical_crossentropy,metrics[accuracy])EPOCHS 5hist model.fit(train_batches,epochsEPOCHS,validation_datavalidation_batches)CATS_VS_DOGS_SAVED_MODEL exp_saved_model
tf.saved_model.save(model, CATS_VS_DOGS_SAVED_MODEL)import pathlib
converter tf.lite.TFLiteConverter.from_saved_model(CATS_VS_DOGS_SAVED_MODEL)tflite_model converter.convert()
tflite_models_dir pathlib.Path(/tmp/)tflite_model_file tflite_models_dir/model1.tflite
tflite_model_file.write_bytes(tflite_model)
# This will report back the file size in bytesfrom tqdm import tqdm
# Load TFLite model and allocate tensors.
tflite_model_file /tmp/model1.tflite
interpreter tf.lite.Interpreter(model_pathtflite_model_file)
interpreter.allocate_tensors()input_index interpreter.get_input_details()[0][index]
output_index interpreter.get_output_details()[0][index]predictions []# This will report how many iterations per second, where each
# iteration is 100 predictions
test_labels, test_imgs [], []
for img, label in tqdm(test_batches.take(100)):interpreter.set_tensor(input_index, img)interpreter.invoke()predictions.append(interpreter.get_tensor(output_index))test_labels.append(label.numpy()[0])test_imgs.append(img)# This will tell you how many of the predictions were correct
score 0
for item in range(0,len(predictions)):predictionnp.argmax(predictions[item])label test_labels[item]if predictionlabel:scorescore1print(Out of 100 predictions I got str(score) correct)#title Utility functions for plotting
# Utilities for plottingclass_names [cat, dog]def plot_image(i, predictions_array, true_label, img):predictions_array, true_label, img predictions_array[i], true_label[i], img[i]plt.grid(False)plt.xticks([])plt.yticks([])img np.squeeze(img)plt.imshow(img, cmapplt.cm.binary)predicted_label np.argmax(predictions_array)if predicted_label true_label:color greenelse:color redplt.xlabel({} {:2.0f}% ({}).format(class_names[predicted_label],100*np.max(predictions_array),class_names[true_label]), colorcolor)#title Visualize the outputs { run: auto }
max_index 73 #param {type:slider, min:1, max:100, step:1}
for index in range(0,max_index):plt.figure(figsize(6,3))plt.subplot(1,2,1)plot_image(index, predictions, test_labels, test_imgs)plt.show()
关键的解读如下
首先就是约束版本。。。感觉真的挺复杂的以后整合这些版本也是很麻烦的。 tf_version 2.14.0 hub_version 0.15.0 datasets_version 4.6.0 numpy_version 1.26.4 protobuf_version 3.20.3 tf就是tensorflownumpy倒是不用说protobuf之前是谷歌网络框架gPRC里面的没想到在AI领域又拿出来了是直接用来替代json保存模型。hub是提供了一些预先训练好的模型可以直接用。datasets不是数据库因为如果要把数据库都下载下来体积是非常大。datasets是数据库的管理工具。 2.2.1 训练模型
首先是数据划分。
split[train[:80%], train[80%:90%], train[90%:]],
训练集80%验证集10%测试集10%。
之前做了归一化打乱这些处理。
之后使用Hub加载了MobileNet V2 预训练特征提取器。之后直接在后面增加一个全连接层就搞定。。
module_selection (mobilenet_v2, 224, 1280)
MODULE_HANDLE https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4
feature_extractor hub.KerasLayer(MODULE_HANDLE, input_shape(224,224,3), output_shape[1280], trainableFalse)model tf.keras.Sequential([feature_extractor,tf.keras.layers.Dense(num_classes, activationsoftmax)
])
2.2.2 模型转换
其实也就是几行代码。
tf.saved_model.save(model, exp_saved_model)converter tf.lite.TFLiteConverter.from_saved_model(exp_saved_model)
tflite_model converter.convert() 2.2.3 使用tflite推理
interpreter tf.lite.Interpreter(model_path/tmp/model1.tflite)
interpreter.allocate_tensors()input_index interpreter.get_input_details()[0][index]
output_index interpreter.get_output_details()[0][index]for img, label in tqdm(test_batches.take(100)):interpreter.set_tensor(input_index, img)interpreter.invoke()predictions.append(interpreter.get_tensor(output_index))test_labels.append(label.numpy()[0])test_imgs.append(img)
后面还有显示预测准确性还有做了一个可视化。
不过可惜我在Colab上面运行一直报错这里暂时看不到了。。。 2.3 大作业
链接https://colab.research.google.com/github/tinyMLx/colabs/blob/master/3-3-14-Assignment.ipynb
依旧还是2.14版本的问题。 代码如下
import subprocess
import tensorflow as tf
import numpy as np# Desired versions
tf_version 2.14.0
hub_version 0.15.0
datasets_version 4.6.0
numpy_version 1.26.4
protobuf_version 3.20.3# Try importing optional packages
try:import tensorflow_hub as hub
except ImportError:hub Nonetry:import tensorflow_datasets as tfds
except ImportError:tfds None# Helper to install a specific version of a package
def install_package(package, version):subprocess.check_call([pip, install, f{package}{version}])# Helper to uninstall multiple packages
def uninstall_packages(packages):subprocess.check_call([pip, uninstall, -y] packages)# Version mismatch check
version_mismatch (tf.__version__ ! tf_version or(hub and hub.__version__ ! hub_version) or(tfds and tfds.__version__ ! datasets_version) ornot np.__version__.startswith(numpy_version)
)# Act on mismatches
if version_mismatch:print(fTensorFlow: {tf.__version__} → {tf_version})if hub: print(fTensorFlow Hub: {hub.__version__} → {hub_version})if tfds: print(fTensorFlow Datasets: {tfds.__version__} → {datasets_version})print(fNumPy: {np.__version__} → {numpy_version})uninstall_packages([tensorflow, tensorflow_hub, tensorflow_datasets,numpy, protobuf])install_package(tensorflow, tf_version)install_package(tensorflow_hub, hub_version)install_package(tensorflow_datasets, datasets_version)install_package(numpy, numpy_version)install_package(protobuf, protobuf_version)print(\nRequired versions installed successfully.)print(Please restart the runtime (Runtime Restart session) and re-run the notebook.)
else:print(All packages are already at the specified versions.)import numpy as np
import matplotlib.pylab as plt
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_datasets as tfds
from tqdm import tqdm
import pathlibdef format_image(image, label):image tf.image.resize(image, (224, 224)) / 255.0return image, label(raw_train, raw_validation, raw_test), metadata tfds.load(rock_paper_scissors, split[train[:80%], train[80%:], test], with_infoTrue, as_supervisedTrue)
num_examples metadata.splits[train].num_examples
num_classes metadata.features[label].num_classesBATCH_SIZE 32
train_batches raw_train.shuffle(num_examples // 4).map(format_image).batch(BATCH_SIZE).prefetch(1)
validation_batches raw_validation.map(format_image).batch(BATCH_SIZE).prefetch(1)
test_batches raw_test.map(format_image).batch(1)for image_batch, label_batch in train_batches.take(1):passmodule_selection (mobilenet_v2, 224, 1280)
handle_base, pixels, FV_SIZE module_selection
MODULE_HANDLE https://tfhub.dev/google/tf2-preview/{}/feature_vector/4.format(handle_base)
IMAGE_SIZE (pixels, pixels)
print(Using {} with input size {} and output dimension {}.format(MODULE_HANDLE, IMAGE_SIZE, FV_SIZE))feature_extractor hub.KerasLayer(MODULE_HANDLE,input_shapeIMAGE_SIZE (3,), output_shape[FV_SIZE],trainableFalse)print(Building model with, MODULE_HANDLE)model tf.keras.Sequential([feature_extractor,tf.keras.layers.Dense(num_classes, activationsoftmax)
])model.summary()model.compile(optimizeradam,losssparse_categorical_crossentropy,metrics[accuracy])EPOCHS 5hist model.fit(train_batches,epochsEPOCHS,validation_datavalidation_batches)ROCK_PAPER_SCISSORS_SAVED_MODEL exp_saved_model
tf.saved_model.save(model, ROCK_PAPER_SCISSORS_SAVED_MODEL)converter # YOUR CODE GOES HERE #
tflite_model # YOUR CODE GOES HERE #tflite_models_dir pathlib.Path(/tmp/)
tflite_model_file tflite_models_dir/model1.tflite
tflite_model_file.write_bytes(tflite_model)
# This will report back the file size in bytes
# you will note that this model is too big for our Arduino
# but would work on a mobile phone# Load TFLite model and allocate tensors.
TFLITE_MODEL_FILE /tmp/model1.tflite
interpreter # YOUR CODE GOES HERE #
interpreter.allocate_tensors()input_index interpreter.get_input_details()[0][index]
output_index interpreter.get_output_details()[0][index]
predictions []# This will report how many iterations per second, where each
# iteration is 100 predictions
test_labels, test_imgs [], []
for img, label in tqdm(test_batches.take(100)):interpreter.set_tensor(input_index, img)interpreter.invoke()predictions.append(interpreter.get_tensor(output_index)) test_labels.append(label.numpy()[0])test_imgs.append(img)# This will tell you how many of the predictions were correct
score 0
for item in range(0,99):predictionnp.argmax(predictions[item])label test_labels[item]if predictionlabel:scorescore1print(\nOut of 100 predictions I got str(score) correct)# Utilities for plottingclass_names [rock, paper, scissors]def plot_image(i, predictions_array, true_label, img):predictions_array, true_label, img predictions_array[i], true_label[i], img[i]plt.grid(False)plt.xticks([])plt.yticks([])img np.squeeze(img)plt.imshow(img, cmapplt.cm.binary)predicted_label np.argmax(predictions_array)if predicted_label true_label:color greenelse:color redplt.xlabel({} {:2.0f}% ({}).format(class_names[predicted_label],100*np.max(predictions_array),class_names[true_label]), colorcolor)#title Visualize the outputs { run: auto }
max_index 73 #param {type:slider, min:0, max:99, step:1}
for index in range(0,max_index):plt.figure(figsize(6,3))plt.subplot(1,2,1)plot_image(index, predictions, test_labels, test_imgs)plt.show()填写的应该和之前的类似。 ROCK_PAPER_SCISSORS_SAVED_MODEL exp_saved_model tf.saved_model.save(model, ROCK_PAPER_SCISSORS_SAVED_MODEL) interpreter tf.lite.Interpreter(model_path/tmp/model1.tflite)
