Table of Contents summary overall process 1.Construct Huffman tree 2. Combine CBOW and Huffman trees 3. Build word2vec model (CBOW) summary Complete code Summary This blog mainly describes in detail the specific process and code implementation of word2vec using hierarchical softmax (hierarchical softmax). For the basic implementation of word2vec, you can read my previous blog: […]
Tag: softmax
Reparameterization Trick and Gumbel-Softmax
Reparameterization Trick and Gumbel-Softmax In the learning process of RL reinforcement learning, category sampling is needed in many places, that is, the neural network outputs the probability of each category, and then needs to be sampled, and the sampling process must be able to back propagate to find the gradient. Or the neural network outputs […]
Implementation of softmax regression from scratch
import matplotlib.pyplot as plt import torch from IPython import display from d2l import torch as d2l batch_size = 256 train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size) # Return the iterator of the training set and test set # Will flatten each image, treating them as vectors of length 784. The image is 28*28, stretched to 784 # Because […]
2. softmax regression
Learning video: 09 Softmax regression + loss function + image classification data set [hands-on learning deep learning v2]_bilibili_bilibili http://localhost:8888/notebooks/chapter_linear-networks/softmax-regression-scratch.ipynb Table of Contents 1. Loss function 1.Mean square loss: 2. Absolute value loss function: ?Edit 3.Huber’s Robust Loss?Edit?Edit 2. Softmax implementation: 1. Initialize model parameters: 2. Define softmax operation: 3. Define the model: 4. Implement the […]
Practice: Complete the iris classification task based on Softmax regression
The file codes in the nndl package referenced in the file can be introduced in detail in previous blogs, so I will not go into details. Multi-classification tasks based on Softmax regression_The blog of patients who stay up late – CSDN Bloghttps://blog.csdn.net/m0_70026215/article/details/133690588?spm=1001.2014.3001.5501 1.Data processing Dataset introduction Missing value analysis from sklearn.datasets import load_iris import pandas […]
The one-dimensional vibration signal is converted into a two-dimensional grayscale image, and the local binary pattern (LBP) is used to deepen the grayscale image features, and then CNN is used for feature extraction, and finally the softmax classifier and SVM are used for classification comparison (Python code, after decompression run directly)
Operation effect: The one-dimensional vibration signal is converted into a two-dimensional grayscale image, the local binary pattern (LBP) is used to deepen the grayscale image features, and then CNN is used for feature extraction, and finally the softmax classifier and SVM are used for classification comparison (Python_bee bilibili_bilibili Versions of all libraries used 1. Data […]
The process of softmax derivation
(Picture from teacher Li Hongyi’s PPT) Students who are familiar with machine learning/deep learning will definitely be familiar with softmax. It sometimes appears in multi-classification to obtain the probability of each category, and sometimes appears in binary classification to obtain the probability of each category. The probability of a positive sample (of course, at this […]
Softmax-Multiple classification problem notes-Station B: Mr. Liu Er’s “PyTorch Deep Learning Practice”
Table of Contents 1.Softmax formula 2.MNIST data set 3. Steps (1) Prepare data set (2)Construction model (3) Choose the appropriate loss function and optimizer (4) Conduct training and testing 4.Experimental results 5.Homework 1.Softmax formula example: CrossEntropyLoss included via Softmax Take And take the loss value CrossEntropyLoss == LogSoftmax + NLLLoss 2.MNIST data set If you […]
3.6 Simple implementation of softmax regression
Use deep learning framework to concisely implement softmax regression model Reference materials: Li Mu’s “Hands-on Learning of Deep Learning-Pytorch Edition”ch3 Linear Neural Network Open source address: hands-on learning of deep learning Link to the previous section: 3.5 Implementation of softmax regression from scratch This article is just a learning record. For more detailed content, you […]
Softmax, Cross-entropy Loss and Gradient derivation and Implementation
Table of Contents 1. Overview 2. Sigmoid function 3. Softmax function 3.1 Definition 3.2 Partial derivatives of softmax function 3.3 Gradient of softmax function 3.4 python implementation of softmax and its gradient 4. cross-entropy loss 4.2 logistic loss 4.3 logistic loss gradient 4.4 cross-entropy loss 4.5 cross-entropy loss gradient 5. Gradient of “softmax + cross-entropy” […]