softmax regression + loss function + image classification data set

Accumulation of knowledge points # mnist_train[0][0].shape, mnist_train[0] represents the first sample in the MNIST training set, and [0] represents the first element of the sample. # In the MNIST dataset, each sample consists of a pair of input features and labels. Therefore, mnist_train[0] represents the first example, which contains the input features and labels. And […]

There is a bug in the Attention mechanism? Softmax is the culprit, affecting all Transformers

Source: Machine Heart PaperWeekly This article is about 2800 words, it is recommended to read for 6 minutes Statistical engineers discovered why reanformers are difficult to compress. “Mockup developers, you are wrong.” “I found a bug in the attention formula, and no one has discovered it for eight years. All Transformer models, including GPT and […]

PyTorch Lecture 9 Softmax Classifier

Softmax classifier In multi-classification problems, we hope that the final output is a distribution, satisfying P ( the y = i ) ≥ 0 P(y=i)\geq0 P(y=i)≥0 and ∑ i = 0 no P ( the y = i ) = 1 \sum_{i=0}^nP(y=i)=1 ∑i=0n?P(y=i)=1, so we use Softmax instead of the previous Sigmoid P ( the […]

Multi Class Classfication – Softmax

Article directory Multi Class Classification Softmax Equation Loss & Cost Neural Network with Softmax output Improved Implements Round out Errors More numerically accurate implement of logistic loss More numerically accurate implement of softmax Multi Label Classification Adaptive Moment estimation Additional Layer Types Convolutional Layer refer to Multi Class Classification In the binary classification task, y […]

Softmax regression realizes handwritten digit recognition

Classification of handwritten digits 1-9 by logistic regression Objective: To understand the difference between softmax and sigmoid, Deepen understanding of logistic regression and softmax regression Knowledge points: one-hot encoding, softmax regression Data preprocessing + training set division import numpy as np import pandas as pd data = pd.read_csv(“./digits.csv”) data[:5] label pixel0 pixel1 pixel2 pixel3 pixel4 […]