jinyu121

SimpleImageHTTPServer

Modified from SimpleHTTPServer, this file can setup a simple image server, to view folder, image, and compare images between folders.

How to use

Start

python image_server.py --path path/to/your/image/folder

shravankumar147

# USAGE
# python face_detection.py --image face1.jpg

# import the necessary packages
# from imutils import face_utils
# import numpy as np
import argparse
import imutils
import dlib
import cv2

ikhlestov

import torch
from torch.autograd import Variable

# new way with `init` module
w = torch.Tensor(3, 5)
torch.nn.init.normal(w)
# work for Variables also
w2 = Variable(w)
torch.nn.init.normal(w2)
# old styled direct access to tensors data attribute

fsodogandji

# from https://www.kaggle.com/nothxplz/dogs-vs-cats-redux-kernels-edition/cats-vs-dogs-05-pytorch-example/run/761413
from __future__ import print_function

import argparse
import csv
import os
import os.path
import shutil
import time

fchollet

from keras.models import Sequential
from keras.layers import Dense

x, y = ...
x_val, y_val = ...

# 1-dimensional MSE linear regression in Keras
model = Sequential()
model.add(Dense(1, input_dim=x.shape[1]))
model.compile(optimizer='rmsprop', loss='mse')

fchollet

'''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

tomokishii

#
#  mnist_cnn_bn.py   date. 5/21/2016
#                    date. 6/2/2017 check TF 1.1 compatibility
#

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

albertomontesg

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites