Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality. Learn more →
Lottery_Ticket_Hypothesis-TensorFlow_2 Alternatives
Similar projects and alternatives to Lottery_Ticket_Hypothesis-TensorFlow_2 based on common topics and language
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
-
-
Generalizing-Lottery-Tickets
This repository contains code to replicate the experiments given in NeurIPS 2019 paper "One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers"
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better Lottery_Ticket_Hypothesis-TensorFlow_2 alternative or higher similarity.
Lottery_Ticket_Hypothesis-TensorFlow_2 reviews and mentions
Posts with mentions or reviews of Lottery_Ticket_Hypothesis-TensorFlow_2.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-05-10.
-
Freeze certain weights - TensorFlow 2
I have already implemented "The Lottery Ticket Hypothesis" by Frankle et al. using TensorFlow 2. You can refer to the code here. Here, a binary mask (0, 1) is used for element-wise multiplication to keep the number of pruned parameters constant because by default, when you apply gradient descent algorithm, then using the weight update rule, all of the weights are updated.
-
[R] Remove pruned connections
Some of my recent experiments in GitHub can be referred: Lottery Ticket Hypothesis implementation and Neural Network Pruning.
-
TensorFlow Lite: RuntimeError
I am using TensorFlow version: 2.3.0 and Python3. I am experimenting in Quantizing a pruned and trained Conv-2 CNN model. The model architecture is: conv -> conv -> max pool -> dense -> dense -> output for CIFAR-10. You can see the Jupyter-notebook here.
-
Iterative Pruning: LeNet-300-100 - PyTorch
The code can be accessed here
-
Neural Network Compression - Implementation benefits
here
-
ValueError: TensorFlow2 Input 0 is incompatible with layer model
True, removing he_normal initialization does increase the accuracy. For most of my previous experiments I have usually used the kernel initialization as mentioned in the different author's paper(s). Therefore for ResNet, I thought of using Kaiming He initialization as he is the author of the research paper. However, the default kernel initialization in TF2 is 'glorot_uniform' which leads to 60.04% val_accuracy.
-
A note from our sponsor - InfluxDB
www.influxdata.com | 24 Apr 2024
Stats
Basic Lottery_Ticket_Hypothesis-TensorFlow_2 repo stats
6
33
4.1
28 days ago
The primary programming language of Lottery_Ticket_Hypothesis-TensorFlow_2 is Jupyter Notebook.
Sponsored
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com