Plane data classifier based on single hidden layer neural network algorithm @[TOC](Plane data classifier based on single hidden layer neural network algorithm) 1. Package 2. Data set 3. Use logistic regression for classification prediction 4. Neural network model 1. Determine the structure of the neural network 2. Initialize the parameters of the model—W1, b1, W2, […]
Tag: deep-learning
Handwritten digit recognition–deep learning experiment
Get the experimental source code yourself: The code I made myself has a prediction accuracy of only 94% Neural network experiment report source code.zip – Lan Zuoyun The experimental source code answers and information given by the teacher have a prediction accuracy of up to 99% Deep learning experiment report.zip – Lanzuo Cloud In a […]
[Deep Learning Case] Handwritten Number Project Implementation-2.Python Model Training
[Introduction to Deep Learning Tutorial] Handwritten Number Project Implementation-2.Python Model Training 4. Python implements model training based on the Pytorch framework 4.1 Training environment 4.2 Define data loader 4.3 Define the network (net, py) 4.4 Define the trainer (trainer.py) 4.5 Model training (main_MNIST.py) ? The source code and all source codes used in this project […]
Deep learning handwritten digit recognition model
Table of Contents 1: Construction and preservation of handwritten digital models 2: Use and testing of handwritten digital model One: Construction and Saving of Handwritten Digital Model 1 Load data set # 1Load data digits_data = load_digits() You can briefly check the handwritten number set first. As shown below, you can vaguely see that the […]
[Neural Network and Deep Learning] Using the MNIST data set to train a handwritten digit recognition model – [Attached complete training code]
[Neural Network and Deep Learning] Use the MNIST data set to train a handwritten digit recognition model – [complete training code attached] 1. Introduction to the MNIST data set MNIST data set structure 2. Model training ideas ①Load data ②Data preprocessing ③Build a model ④Configure model training method ⑤Training model ⑥Evaluation model ⑦Save the model […]
“Python Deep Learning” Notes (20): Neural Network Interpretation Methods: CAM, Grad-CAM, Grad-CAM++, LayerCAM
Principle Advantages Disadvantages GAP Reduce the dimensionality of the multi-dimensional feature map into a fixed-length feature vector ① reduces the number of parameters of the model; ② retains more spatial position information; ③ can be calculated in parallel and has high computational efficiency; ④ has a certain degree of invariance ① May cause information loss; […]
Deep learning 1
1. Simple model (similar to “hello world”) mnist: There are 60,000 training images and 10,000 test images. from tensorflow.keras.datasets import mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() train_images.shape len(train_labels) train_labels test_images.shape len(test_labels) test_labels from tensorflow import keras from tensorflow.keras import layers model = keras.Sequential([ layers.Dense(512, activation=”relu”), layers.Dense(10, activation=”softmax”) ]) model.compile(optimizer=”rmsprop”, loss=”sparse_categorical_crossentropy”, metrics=[“accuracy”]) train_images = train_images.reshape((60000, […]
Deep Learning Practical Case 1: Lesson 1: Loading Data Sets
1: How to design Class to load images, convert data sets and output them into Totensor: take Pokemon dataset as an example Download: pokemon data Link: https://pan.baidu.com/s/1o1iblvQyfYw47bk4UPFpbA?pwd=8888 Extraction code: 8888 #Dataset: Pikachu: 234, Mewtwo: 239, Squirtle: 223, Charmander: 238, Bulbasaur: 234 import torch import os,glob import random,csv from torch.utils.data import Dataset ,DataLoader from PIL import […]
dive into deep-learning
2. Preliminary knowledge 2.1 Data Operation—-The main interface for deep learning to store and operate data is tensor (n-dimensional array) 1.torch Tensor:tensor Method to create tensor: torch.arange(12), create a vector of [0, 12); Check the size of tensor: x.shape or x.numel() Reconstruct tensor dimensions: x.reshape(3,4) Create special tensors: torch.zeros(3,3,4), torch.ones(3,3,4), torch.randn(3,3,4), where random numbers are […]
Deep learning convolutional neural image classification to achieve bird recognition including training code and bird data set (supports repVGG, googlenet, resnet, inception, mobilenet)
Pytorch implements bird recognition (including training code and bird data set) Table of Contents Pytorch implements bird recognition (including training code and bird data set) 1 Introduction 2. Bird Dataset (1)Bird-Dataset26 (2) Custom data set 3. Bird classification and recognition model training (1) Project installation (2) Prepare Train and Test data (3) Configuration file: ?config.yaml? […]