Deep Neural Network Prediction

Step 1. Genernate random parameters and Run them sequentially : $ python3 collect_data.py -gp -ep -pp -pl pooling -num 10 -shuffle -d 1080ti

Step 2. Data Timeline parser : $ python3 preprocess_data.py -pt -pl pooling -d 1080ti

Step 3. Combine all raw data : $ python3 preprocess_data.py -c -pl pooling -d 1080ti

Step 4. Split raw data to train and test data as performance prediction inputs $ python3 preprocess_data.py -sp -pl pooling -d 1080ti

Step 5. Train Model :

python3 train_model.py -ftf ./utils/Feature_Target/conv_pre.json -log2file 1 -e 1000 -st 400 -sg 0.5 -lf maple -n perfnetA -pd 1080ti -pl convolution -psl pre
    
python3 train_model.py -ftf ./utils/Feature_Target/conv_exe.json -log2file 1 -e 1000 -st 400 -sg 0.5 -lf maple -n perfnetA -pd 1080ti -pl convolution -psl exe
        
python3 train_model.py -ftf ./utils/Feature_Target/conv_post.json -log2file 1 -e 1000 -st 400 -sg 0.5 -lf maple -n perfnetA -pd 1080ti -pl convolution -psl post

Step 6. Generate model csv file :

python3 verify_model.py -gmc --model lenet -b 1

Step 7. Predict model csv file :

python3 verify_model.py -pdm -n perfnetA -d 1080ti -l -eva -lf maple --model lenet -b 1

Or you can use script to predict all models with all batches:

python3 predict_script.py -n perfnetA -d 1080ti -lf maple

Predict the latency time of the deep learning models

Related tags

Overview

Deep Neural Network Prediction

Owner

QAQ

python library for invisible image watermark (blind image watermark)

Align before Fuse: Vision and Language Representation Learning with Momentum Distillation

Code and data accompanying our SVRHM'21 paper.

AgML is a comprehensive library for agricultural machine learning

An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

Discord bot-CTFD-Thread-Parser - Discord bot CTFD-Thread-Parser

Implementation for Paper "Inverting Generative Adversarial Renderer for Face Reconstruction"

【Arxiv】Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution

NCVX (NonConVeX): A User-Friendly and Scalable Package for Nonconvex Optimization in Machine Learning.

🛠️ SLAMcore SLAM Utilities

Software & Hardware to do multi color printing with Sharpies

subpixel: A subpixel convnet for super resolution with Tensorflow

AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition

BERTMap: A BERT-Based Ontology Alignment System

Wandb-predictions - WANDB Predictions With Python

Nightmare-Writeup - Writeup for the Nightmare CTF Challenge from 2022 DiceCTF

Workshop Materials Delivered on 28/02/2022

The official implementation of ELSA: Enhanced Local Self-Attention for Vision Transformer

Use AI to generate a optimized stock portfolio