model pretends that a part/structure of the input is missing and it learns to predict the missing bit. It is similar to solving an almost finished jigsaw puzzle which has just a couple of open spots. Looking vanilla distillation. We will now go over stochastic depth, a technique which can be useful if you are training very deep networks. Stochastic Depth Deep networks with hundreds of layers such as ResNets the output of the previous layer ( ) skips the layers represented by the function . The stochastic depth idea takes this one step further by probabilistically dropping a residual block with a probability

our embeddings to be more robust to the task at hand. We will demonstrate how to do this in just a bit. A Real World Example: Word2Vec In the real world, we must automate the embedding table generation (EJSM/Laplace) was a proposed joint NASA/ESA unmanned space mission slated to launch around 2020 for the in-depth exploration of Jupiter's moons with a focus on Europa Ganymede and Jupiter's magnetosphere." Let's pQRNN uses the projection operation which maps a given input token to a B-bit fingerprint using a hash function, and this B-bit fingerprint is transformed into a d-dimensional representation using a learned

performance on the image and language benchmark datasets. Moreover, their NAS model could generate variable depth child networks. Figure 7-4 shows a sketch of their search procedure. It involves a controller which performance out of a model. Traditionally, we relied on experts who used their intuition and a fair bit of trial-and-error to tune hyperparameters. However, in a fast paced environment, intuitions become

One-hot 编码，通过 one_hot 函数即可 实现。 In [1]: def one_hot(label, depth=10): # one-hot 编码函数，depth 设置向量长度 out = torch.zeros(label.size(0), depth) idx = torch.LongTensor(label).view(-1, 1) out out y = torch.tensor([0,1,2,3]) # 数字编码的 4 个样本标签 预览版202112 3.3 误差计算 7 y = one_hot(y, depth=10) # one-hot 编码，指定类别总数为 10 print(y) Out[1]: tensor( [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] # 数字 tensor([True]) # ==比对自动转换为 PyTorch 张量 4.2 数值精度 对于数值类型的张量，可以保存为不同字节长度的精度，如浮点数 3.14 既可以保存为 16 位(Bit)长度，也可以保存为 32 位甚至 64 位的精度。位越长，精度越高，同时占用的内 预览版202112 第 4 章 PyTorch 基础 4 存空间也就越大。常用的精度类型有 torch

layers.SeparableConv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, depth_multiplier=1, activation=None, use_bias=True, depthwise_initializer='glorot_uniform', pointwise bias_constraint=None) 深度方向的可分离 2D 卷积。 可分离的卷积的操作包括，首先执行深度方向的空间卷积（分别作用于每个输入通道），紧 接一个将所得输出通道混合在一起的逐点卷积。depth_multiplier 参数控制深度步骤中每个输 入通道生成多少个输出通道。 直观地说，可分离的卷积可以理解为一种将卷积核分解成两个较小的卷积核的方法，或者 作为 Inception 块的一个极端版本。 json 中找到的 image_data_format 值。如果你从未设 置它，将使用”channels_last”。 • depth_multiplier: 每个输入通道的深度方向卷积输出通道的数量。深度方向卷积输出通道 的总数将等于 filterss_in * depth_multiplier。 • activation: 要使用的激活函数 (详见 activations)。如果你不指定，则不使用激活函数

Scikit-learn主要用法 监督学习算法-分类 from sklearn.tree import DecisionTreeClassifier clf = DecisionTreeClassifier(max_depth=5) clf.fit(X_train, y_train) y_pred = clf.predict(X_test) y_prob = clf.predict_proba(X_test) 交叉验证及超参数调优 from sklearn.model_selection import cross_val_score clf = DecisionTreeClassifier(max_depth=5) scores = cross_val_score(clf, X_train, y_train, cv=5, scoring=’f1_weighted’) 使用5折交叉验证对决策树模型进行评估，

于左侧区域是纯净的（仅Iris-Setosa），因此无法进一步拆分。 •然而，右侧区域是不纯的，因此深度为1的右侧节点将其分割成花瓣 宽度= 1.75厘米（由虚线表示）。由于max_depth设置为2，因此决策 树会在那里停止。 •但是，如果将max_depth设置为3，那么两个深度为2的节点将各自添 加另一个决策边界（由点虚线表示）。 150个鸢尾花样本进行分类，特 征为花萼的长度和宽度 决策树原理 33 CART算法-回归

Building Block Repeat this 50 times © 2018 Bloomberg Finance L.P. All rights reserved. Evolution of Depth AlexNet 8 Layers ILSVRC 2012 VGG 19 Layers ILSVRC 2014 ResNet 152 Layers ILSVRC 2015 © 2018 Bloomberg

image_size 必须能够被 patch_size整除。 num_classes：int 类型参数，分类数目。 dim：int 类型参数，线性变换nn.Linear(..., dim)后输 出张量的尺寸 。 depth：int 类型参数，Transformer模块的个数。 heads：int 类型参数，多头注意力中“头”的个数。 mlp_dim：int 类型参数，多层感知机中隐藏层的神经 元个数。 channels：int

on an iPhone 6. 时空一致性深度恢复 • Guofeng Zhang, Jiaya Jia, Tien-Tsin Wong, and Hujun Bao. Consistent Depth Maps Recovery from a Video Sequence. IEEE Transactions on Pattern Analysis and Machine Intelligence