ottokart

# coding: utf-8

# A little demo illustrating the effect of momentum in neural network training.
# Try using different values for MOMENTUM constant below (e.g. compare 0.0 with 0.9).
# This neural network is actually more like logistic regression, but I have used
# squared error to make the error surface more interesting.

import numpy as np
import pylab

BlGene

class DataProvider(object):
    def __init__(self,label_dict, top_blobs = None):
        # for determinist evaluation
        self.random_state = None
        self.label_dict = label_dict

        if top_blobs is None:
            x = label_dict.values()[0]
            if type(x) == int:
                self.top_blobs = ["img", "label"]

ducha-aiki

# The train/test net protocol buffer definition
net: "examples/cifar10/cifar10_full_sigmoid_train_test.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 100
# Carry out testing every 1000 training iterations.
test_interval: 1000
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.001

dulacp

# Builds a Libpng framework for the iPhone and the iPhone Simulator.
# Creates a set of universal libraries that can be used on an iPhone and in the
# iPhone simulator. Then creates a pseudo-framework to make using libpng in Xcode
# less painful.
#
# To configure the script, define:
#    IPHONE_SDKVERSION: iPhone SDK version (e.g. 8.1)
#
# Then go get the source tar.bz of the libpng you want to build, shove it in the
# same directory as this script, and run "./libpng.sh". Grab a cuppa. And voila.

agelastic

.card {
  font-family: arial;
  font-size: 20px;
  text-align: center;
  color: black;
  background-color: white;
}
.card { word-wrap: break-word; }
.win .chinese { font-family: "MS Mincho", "ＭＳ 明朝"; }
.mac .chinese { font-family: "STFangsong"}

djhoese

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# vispy: gallery 50
"""
This example shows how to display 3D objects.
You should see a colored outlined spinning cube.
"""

import numpy as np
from vispy import app, gloo

msiemens

import os
import inspect
from functools import wraps


_file_cache = {}


def cache_file(path):
    def cache_is_fresh(name):

benjbaron

#include <QtGui>
#include <QGraphicsRectItem>
#include <QGraphicsView>
#include <QApplication>
#include <QGraphicsSceneMouseEvent>

class CustomItem : public QGraphicsEllipseItem
{
protected:
    void mousePressEvent(QGraphicsSceneMouseEvent *event)

rafaspadilha

How to create a custom Caffe layer in Python?

This tutorial will guide through the steps to create a simple custom layer for Caffe using python. By the end of it, there are some examples of custom layers.

- Why would I want to do that?

Usually you would create a custom layer to implement a funcionality that isn't available in Caffe, tuning it for your requirements.

- What will I need?

Probably just Python and Caffe installed.

- Is there any downside?

mstankie

# A yaml constructor is for loading from a yaml node.
# This is taken from @misha 's answer: http://stackoverflow.com/a/15942429
def opencv_matrix_constructor(loader, node):
    mapping = loader.construct_mapping(node, deep=True)
    mat = np.array(mapping["data"])
    mat.resize(mapping["rows"], mapping["cols"])
    return mat
yaml.add_constructor(u"tag:yaml.org,2002:opencv-matrix", opencv_matrix_constructor)

# A yaml representer is for dumping structs into a yaml node.