阿掖山
阿掖山

智力活动是一种生活态度 https://mountaye.github.io/blog/

.py | 深度学习暑期学校知识点

如题(Matters 的工程师们真的不打算支持多级 list 吗)
电脑设置、python 入门
  • NoMachine, ssh, python, conda, jupyter
  • 文件夹操作 pathlib, 图片操作 skimage
  • 数据增强 (data augmentation): imgaug
  • TensorBoard
机器学习简介
  • Linear Classifier
  • sklearn**.**model_selection**.**train_test_split()
  • Stochastic gradient descent
  • TensorFlow:
  1. tensorflow_addons as tfa
  2. tfa.image.rotate()tf.image.random_flip_left_right()
  3. **from** tensorflow.keras **import** Model, **from** tensorflow.keras.models **import** Sequential, **from** tensorflow.keras.layers **import** Input, Flatten, Dense, Activation, BatchNormalization, Conv2D, MaxPool2D, Softmax
  4. tf**.**keras**.**losses**.**CategoricalCrossentropy()tf**.**keras**.**optimizers**.**Adam(lr**=**1e-3, clipnorm**=**0.001)
  5. linear_classifier **=**Model(...)linear_classifier.compile()linear_classifier.fit()linear_classifier.predict()
深度学习简介
  • Perceptron
  • Perceptron-based XOR gate
  • decision boundary of your model: np.meshgrid
图像恢复 (image restoration)
  • CARE network
  • Noise2Nosie, Noise2Void
图像翻译 (image translation)
  • micro-DL: a tool to generate and train U-net from config files.
图像语义分割 (image semantic segmentation): 比较详细,前两节有点水了。
  • **from** PIL **import** Image, **import** imageio, **from** torchvision **import** transforms
  • **from** torch.utils.data **import** Dataset, DataLoader, **import** torch.nn **as** nn, **from** torch.nn **import** functional **as** F, **from** torch.utils.tensorboard **import** SummaryWriter,
  • U-net on PyTorch
图像实例分割 (instance segmentation)
  • Foreground segmentation:
  1. Receptive Field of View: The term is borrowed from biology where it describes the “portion of sensory space that can elicit neuronal responses when stimulated” (wikipedia). Each output pixel can look at/depends on an input patch with that diameter centered at its position. Based on this patch, the network has to be able to make a decision about the prediction for the respective pixel.
  2. Early Stopping to avoid overfitting: define an EarlyStopping class
  • Instance Segmentation:
  1. Three-class model (foreground, background, boundary),
  2. Distance transform (label for each pixel is the distance to the closest boundary),
  3. Edge affinity (consider not just the pixel but also its direct neighbors, predicts the probability that there is an edge, this is called affinity.) 听的时候懂了,回来看的时候没太看懂
  4. Metric learning (learns to predict an embedding vector for each pixel.)
  • Tile and Stitch:
  1. 当需要处理的图片过大时,将图片切分成多个小图,分别预测之后拼接在一起。
  2. 文中说图片尺寸不是 某个参数的整数倍的时候拼贴结果会不连续,但是代码注释中说等于这个整数倍的时候会不连续,晕。
  3. https://arxiv.org/pdf/2101.05846.pdf
  • 一个实例,epithelia cells
失败模式:极其之水,就是科普了一下训练参数错误的后果,以及一点对抗学习的内容
追踪:比较水,因为机器学习追踪的运算量极大,且主讲人感觉就是来做广告的,所以就直接用 CoLab 体验了一下就完事了。(就这还加州理工呢~)
知识提取:
  • 前面的基本上是从像素到像素的映射,这里的知识提取是从图片到标签的映射。
  • CycleGAN
  • Create a balanced Dataloader


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