| #!/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 |
| .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"} |
| #python3 | |
| def hook(t): | |
| def inner(bytes_amount): | |
| t.update(bytes_amount) | |
| return inner | |
| BUCKET_NAME = 'your_s3_bucket_name' | |
| FILE_NAME = 'your_s3_file_name' | |
| s3 = boto3.resource('s3') |
| 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"] |
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.
Usually you would create a custom layer to implement a funcionality that isn't available in Caffe, tuning it for your requirements.
Probably just Python and Caffe installed.
| # 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 |
| # 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. |
| # 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 |
| sudo add-apt-repository ppa:ubuntu-toolchain-r/test | |
| sudo apt-get update | |
| sudo apt-get install gcc-5 g++-5 | |
| sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 60 --slave /usr/bin/g++ g++ /usr/bin/g++-5 |
| #include <QtGui> | |
| #include <QGraphicsRectItem> | |
| #include <QGraphicsView> | |
| #include <QApplication> | |
| #include <QGraphicsSceneMouseEvent> | |
| class CustomItem : public QGraphicsEllipseItem | |
| { | |
| protected: | |
| void mousePressEvent(QGraphicsSceneMouseEvent *event) |
