Vishal Goklani vgoklani

Interested in Deep Learning (self-supervised learning & LLMs), Astrophysics (exoplanets), and Cosmology (CMB).... I like to build things

spro / pytorch-simple-rnn.py

Last active November 7, 2024 11:24

PyTorch RNN training example

	import torch
	import torch.nn as nn
	from torch.nn import functional as F
	from torch.autograd import Variable
	from torch import optim
	import numpy as np
	import math, random

	# Generating a noisy multi-sin wave

reddragon / mnist-pytorch.py

Created April 22, 2017 05:12

	import torch
	import torchvision
	import torch.nn as nn
	import torch.nn.functional as F
	import torchvision.transforms as transforms
	import matplotlib.pyplot as plt
	import numpy as np
	import torch.optim as optim

	from torch.autograd import Variable

Dref360 / TFQueueKeras.py

Last active March 16, 2020 02:24

An example of using keras with tf queues, this handle BatchNorm

	import operator
	import threading
	from functools import reduce

	import keras
	import keras.backend as K
	from keras.engine import Model
	import numpy as np
	import tensorflow as tf
	import time

vgoklani / rnn-lstm.py

Created September 6, 2016 05:40 — forked from monikkinom/rnn-lstm.py

Tensorflow RNN-LSTM implementation to count number of set bits in a binary string

	#Source code with the blog post at http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/
	import numpy as np
	import random
	from random import shuffle
	import tensorflow as tf

	from tensorflow.models.rnn import rnn_cell
	from tensorflow.models.rnn import rnn

	NUM_EXAMPLES = 10000

vgoklani / ratelimit.nginxconf

Created August 17, 2016 05:02 — forked from ipmb/ratelimit.nginxconf

Nginx reverse proxy with rate limiting

	upstream myapp {
	server 127.0.0.1:8081;
	}

	limit_req_zone $binary_remote_addr zone=login:10m rate=1r/s;

	server {
	listen 443 ssl spdy;
	server_name _;

philipperemy / lstm.py

Created August 8, 2016 01:59

	from __future__ import print_function

	import numpy as np
	from keras.callbacks import Callback
	from keras.layers import Dense
	from keras.layers import LSTM
	from keras.models import Sequential
	from numpy.random import choice

	from utils import prepare_sequences

infra-0-0 / dns.py

Last active September 30, 2020 14:47

python3.5 asyncio dns resolver, based on code from who knows where

	"""
	The most simple DNS client written for Python with asyncio:

	* Only A record is support (no CNAME, no AAAA, no MX, etc.)
	* Almost no error handling
	* Doesn't support fragmented UDP packets (is it possible?)

	"""
	import asyncio
	import logging

monikkinom / rnn-lstm.py

Last active September 3, 2019 04:44

Tensorflow RNN-LSTM implementation to count number of set bits in a binary string

	#Source code with the blog post at http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/
	import numpy as np
	import random
	from random import shuffle
	import tensorflow as tf

	# from tensorflow.models.rnn import rnn_cell
	# from tensorflow.models.rnn import rnn

	NUM_EXAMPLES = 10000

fchollet / classifier_from_little_data_script_3.py

Last active February 26, 2025 01:37

Fine-tuning a Keras model. Updated to the Keras 2.0 API.

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

fchollet / classifier_from_little_data_script_2.py

Last active February 26, 2025 01:37

Updated to the Keras 2.0 API.

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats