oak-tree

public interface Attachable {
  
  public File getFile(); 
  public String getTitle();
  public String getDescription();
  public String getRealFileName();
  public Double getSize();//double?
  public void saveFile(File destination);
  
}

oak-tree

for i in \"*\";do echo "$i"; mv "$i" "$(echo "$i" | tr -d \")";done;

oak-tree

1 #include <Python.h>
2 #include <iostream>
3 #include "theano_mod_helper.h"
4 #include "cuda_ndarray.cuh"
5 //////////////////////
6 ////  Support Code
7 //////////////////////
8
9
10     namespace {

oak-tree

from keras import backend as K
if K.backend() == "tensorflow":
    print("Using tensorflow - try to use jit")
    import keras.backend.tensorflow_backend as bck

    config = bck.tf.ConfigProto()
    config.graph_options.optimizer_options.global_jit_level = bck.tf.OptimizerOptions.ON_1
    bck.set_session(bck.tf.Session(config=config))

oak-tree

'''Train a Siamese MLP on pairs of digits from the MNIST dataset.

It follows Hadsell-et-al.'06 [1] by computing the Euclidean distance on the
output of the shared network and by optimizing the contrastive loss (see paper
for mode details).

[1] "Dimensionality Reduction by Learning an Invariant Mapping"
    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf

Gets to 99.5% test accuracy after 20 epochs.

oak-tree

sudo apt-get install xvfb

nohup Xvfb :1 > /dev/null 2>&1 &

#testing with
python

import matplotlib
import matplotlib.pyplot as plt

oak-tree

sudo apt-get install xvfb

nohup Xvfb :1 > /dev/null 2>&1 &

#testing with
python

import matplotlib
import matplotlib.pyplot as plt

oak-tree

sudo apt-get install qt-sdk
then follow
#http://docs.opencv.org/master/d7/d9f/tutorial_linux_install.html
sudo apt-get install build-essential
#install python header and includes 
#
sudo apt-get install python2.7-dev
sudo apt-get install python3.4-dev

sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev

oak-tree

  msg_locked_until = dateutil.parser.parse(msg.broker_properties['LockedUntilUtc'])

            if (datetime.now(utc) + timedelta(0, lock_buff_seconds)) > msg_locked_until:
                logger.info("Renewing message lock")
                try:
                    msg.renew_lock()
                    logger.info("Done.")
                except Exception as e:
                    logger.error("Failed renewing the message lock.")
                    logger.error(e.message)

oak-tree

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

def do_something(img):
    return np.dot(img[...,:3], [0.299, 0.587, 0.114])

img = mpimg.imread('image.png')     
result = do_something(img)    
plt.imshow(result, cmap = plt.get_cmap('gray'))