In this blog post, we will quickly understand how to use state-of-the-art Deep Learning models in Keras to solve a supervised image classification problem using our own dataset with/without GPU acceleration.
We will be using the pre-trained Deep Neural Nets trained on the ImageNet challenge that are made publicly available in Keras. We will specifically use FLOWERS17 dataset from the University of Oxford.
The pre-trained models we will consider are VGG16, VGG19, Inception-v3, Xception, ResNet50, InceptionResNetv2 and MobileNet. Instead of creating and training deep neural nets from scratch (which takes time and involves many iterations), what if we use the pre-trained weights of these deep neural net architectures (trained on ImageNet dataset) and use it for our own dataset?
Let’s start feature extraction using Deep Convolutional Neural Networks! What we will be making at the end of this tutorial is shown below.
Update (16/12/2017): After installing Anaconda with Python 3.6 to work with TensorFlow in Windows 10, I found two additional pretrained models added to Keras applications module - InceptionResNetV2 and MobileNet. I have updated my code accordingly to enable these models to work for our own dataset.
Update (10/06/2018): If you use Keras 2.2.0 version, then you will not find the applications module inside keras installed directory. Keras has externalized the applications module to a separate directory called keras_applications from where all the pre-trained models will now get imported. To make changes to any <pre-trained_model>.py file, simply go to the below directory where you will find all the pre-trained models .py files.
Update (16/12/2017): You could also see the new MobileNet architecture achieves the best accuracy compared to other architectures. In addition, I found that MobileNet uses DepthwiseConvolution layers and has lesser number of parameters, reduced weights size and depth. More details about this can be found at - MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.
Beautiful Keras
Keras is an amazing library to quickly start Deep Learning for people entering into this field. Developed by François Chollet, it offers simple understandable functions and syntax to start building Deep Neural Nets right away instead of worrying too much on the programming part. Keras is a wrapper for Deep Learning libraries namely Theano and TensorFlow. I found the documentation and GitHub repo of Keras well maintained and easy to understand. If you know some technical details regarding Deep Neural Networks, then you will find the Keras documentation as the best place to learn.
Feature Extraction using ConvNets
Traditional machine learning approach uses feature extraction for images using Global feature descriptors such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HoG), Color Histograms etc. or Local descriptors such as SIFT, SURF, ORB etc. These are hand-crafted features that requires domain level expertise.
But here comes Convolutional Neural Networks (CNN)! Instead of using hand-crafted features, Deep Neural Nets automatically learns these features from images in a hierarchical fashion. Lower layers learn low-level features such as Corners, Edges whereas middle layers learn color, shape etc. and higher layers learn high-level features representing the object in the image.
Instead of making a CNN as a model to classify images, what if we use it as a Feature Extractor by taking the activations available before the last fully connected layer of the network (i.e. before the final softmax classifier). These activations will be acting as the feature vector for a machine learning model (classifier) which further learns to classify it. This type of approach is well suited for Image Classification problems, where instead of training a CNN from scratch (which is time-consuming and tedious), a pre-trained CNN could be used as a Feature Extractor - Transfer Learning.
Keras Pre-trained Models
The Deep Neural Net architectures that won the ImageNet challenge are made publicly available in Keras including the model weights. Please check this page for more details regarding each neural network architecture. Please be aware of the input image_size that are given to each model as we will be transforming our input images to these sizes. Below are the pre-trained models available in Keras at the time of writing this post.
- Xception
- VGG16
- VGG19
- ResNet50
- InceptionV3
- InceptionResNetV2
- MobileNet
The applications module of Keras provides all the necessary functions needed to use these pre-trained models right away.
Below is the table that shows image size, weights size, top-1 accuracy, top-5 accuracy, no.of.parameters and depth of each deep neural net architecture available in Keras.
| Model | Image size | Weights size | Top-1 accuracy | Top-5 accuracy | Parameters | Depth |
|---|---|---|---|---|---|---|
| Xception | 299 x 299 | 88 MB | 0.790 | 0.945 | 22,910,480 | 126 |
| VGG16 | 224 x 224 | 528 MB | 0.715 | 0.901 | 138,357,544 | 23 |
| VGG19 | 224 x 224 | 549 MB | 0.727 | 0.910 | 143,667,240 | 26 |
| ResNet50 | 224 x 224 | 99 MB | 0.759 | 0.929 | 25,636,712 | 168 |
| InceptionV3 | 299 x 299 | 92 MB | 0.788 | 0.944 | 23,851,784 | 159 |
| Inception ResNetV2 |
299 x 299 | 215 MB | 0.804 | 0.953 | 55,873,736 | 572 |
| MobileNet | 224 x 224 | 17 MB | 0.665 | 0.871 | 4,253,864 | 88 |
Note: All the above architectures can be created using either Theano or TensorFlow except Xception and MobileNet (as they depend on Separable Convolutions and Depthwise Convolutions which is available only in TensorFlow).
GPU acceleration
GPUs are the beasts when it comes to Deep Learning and no wonder if you enable GPU in your computer, you can speed up feature extraction as well as training process. Steps to activate GPU acceleration to train deep neural nets in Windows 10 are provided in my blog post.
Dependencies
You will need the following Python packages to run the code provided in this tutorial.
- Theano or TensorFlow
- Keras
- NumPy
- scikit-learn
- matplotlib
- seaborn
- h5py
Note: If you don't have an environment to do Deep Learning in Windows or Linux, please make sure you use the below two links to do that and then follow on.
5 simple steps for Deep Learning
- Prepare the training dataset with flower images and its corresponding labels.
- Specify your own configurations in conf.json file.
- Extract and store features from the last fully connected layers (or intermediate layers) of a pre-trained Deep Neural Net (CNN) using extract_features.py.
- Train a Machine Learning model such as Logisitic Regression using these CNN extracted features and labels using train.py.
- Evaluate the trained model on unseen data and make further optimizations if necessary.
Folder structure
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|--flower_recognition
|--|--conf
|--|--|--conf.json
|--|--dataset
|--|--|--train
|--|--|--test
|--|--output
|--|--|--flower_17
|--|--|--|--inceptionv3
|--|--|--|--|--classifier.cPickle
|--|--|--|--|--labels.h5
|--|--|--|--|--features.h5
|--|--|--|--|--results.txt
|--|--|--|--vgg16
|--|--|--|--|--classifier.cPickle
|--|--|--|--|--labels.h5
|--|--|--|--|--features.h5
|--|--|--|--|--results.txt
|--|--|--|--vgg19
|--|--|--|--|--classifier.cPickle
|--|--|--|--|--labels.h5
|--|--|--|--|--features.h5
|--|--|--|--|--results.txt
|--|--|--|--resnet50
|--|--|--|--|--classifier.cPickle
|--|--|--|--|--labels.h5
|--|--|--|--|--features.h5
|--|--|--|--|--results.txt
|--|--|--|--xception
|--|--|--|--|--classifier.cPickle
|--|--|--|--|--labels.h5
|--|--|--|--|--features.h5
|--|--|--|--|--results.txt
|--|--|--|--inceptionresnetv2
|--|--|--|--|--classifier.cPickle
|--|--|--|--|--labels.h5
|--|--|--|--|--features.h5
|--|--|--|--|--results.txt
|--|--|--|--mobilenet
|--|--|--|--|--classifier.cPickle
|--|--|--|--|--labels.h5
|--|--|--|--|--features.h5
|--|--|--|--|--results.txt
|--|--extract_features.py
|--|--train.py
Training dataset
Download the FLOWER17 dataset from this website. Unzip the file and you will see all the 1360 images listed in one single folder named *.jpg. The FLOWERS17 dataset has 1360 images of 17 flower species classes with 80 images per class.
To build our training dataset, we need to create a master folder named dataset, inside which we need to create two more folders namely train and test. Inside train folder, we need to create 17 folders corresponding to the flower species labels.
To automate this task, I have a script that takes in input path that has all the 1360 images and dumps 17 folders inside train folder. In those 17 folders, each folder will be having 80 flower images belonging to that folder name. Below is the screenshot of how to organize our training dataset as well as the output folder to store features, labels, results and classifier.
Script to organize training dataset is given below. Please be aware of the input_path and output_path that you give to create folders and store the images.
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# organize imports
import os
import glob
import datetime
# print start time
print("[INFO] program started on - " + str(datetime.datetime.now))
# get the input and output path
input_path = "G:\\workspace\\machine-intelligence\\deep-learning\\flower-recognition\\17flowers\\jpg"
output_path = "G:\\workspace\\machine-intelligence\\deep-learning\\flower-recognition\\dataset\\train"
# get the class label limit
class_limit = 17
# take all the images from the dataset
image_paths = glob.glob(input_path + "\\*.jpg")
# variables to keep track
label = 0
i = 0
j = 80
# flower17 class names
class_names = ["daffodil", "snowdrop", "lilyvalley", "bluebell", "crocus",
"iris", "tigerlily", "tulip", "fritillary", "sunflower",
"daisy", "coltsfoot", "dandelion", "cowslip", "buttercup",
"windflower", "pansy"]
# change the current working directory
os.chdir(output_path)
# loop over the class labels
for x in range(1, class_limit+1):
# create a folder for that class
os.system("mkdir " + class_names[label])
# get the current path
cur_path = output_path + "\\" + class_names[label] + "\\"
# loop over the images in the dataset
for image_path in image_paths[i:j]:
original_path = image_path
image_path = image_path.split("\\")
image_path = image_path[len(image_path)-1]
os.system("copy " + original_path + " " + cur_path + image_path)
i += 80
j += 80
label += 1
# print end time
print("[INFO] program ended on - " + str(datetime.datetime.now))
Deep Learning pipeline
1. conf.json
This is the configuration file or the settings file we will be using to provide inputs to our system. This is just a json file which is a key-value pair file format to store data effectively.
- The model key takes in any of these parameters - inceptionv3, resnet50, vgg16, vgg19, xception, inceptionresnetv2 and mobilenet.
- The weights key takes the value imagenet specifying that we intend to use weights from imagenet. You can also set this to None if you wish to train the network from scratch.
- The include_top key takes the value false specifying that we are going to take the features from any intermediate layer of the network. You can set this to true if you want to extract features before the fully connected layers.
- The test_size key takes the value in the range (0.10 - 0.90). This is to make a split between your overall data into training and testing.
- The seed key takes any value to reproduce same results everytime you run the code.
- The num_classes specifies the number of classes or labels considered for the image classification problem.
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{
"model" : "inceptionv3",
"weights" : "imagenet",
"include_top" : false,
"train_path" : "dataset/train",
"test_path" : "dataset/test",
"features_path" : "output/flowers_17/inceptionv3/features.h5",
"labels_path" : "output/flowers_17/inceptionv3/labels.h5",
"results" : "output/flowers_17/inceptionv3/results.txt",
"classifier_path" : "output/flowers_17/inceptionv3/classifier.pickle",
"model_path" : "output/flowers_17/inceptionv3/model",
"test_size" : 0.10,
"seed" : 9,
"num_classes" : 17
}
Here, I have decided to use inceptionv3 architecture of GoogleNet pre-trained on imagenet including the top layers. You can extract features from any arbitrary layer using the layer name (eg: flatten), by checking the .py file of each of the model residing inside the applications directory of Keras.
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"python_installation_directory" -> "Lib" -> "site-packages" -> "keras_applications"
As we are using FLOWERS17 dataset from the University of Oxford, I have specified the num_classes as 17. We will have a test_size of 0.10, which means we use 90% of data for training and 10% for testing.
2. Feature Extraction using ConvNets
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# filter warnings
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)
# keras imports
from keras.applications.vgg16 import VGG16, preprocess_input
from keras.applications.vgg19 import VGG19, preprocess_input
from keras.applications.xception import Xception, preprocess_input
from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
from keras.applications.mobilenet import MobileNet, preprocess_input
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.preprocessing import image
from keras.models import Model
from keras.models import model_from_json
from keras.layers import Input
# other imports
from sklearn.preprocessing import LabelEncoder
import numpy as np
import glob
import cv2
import h5py
import os
import json
import datetime
import time
# load the user configs
with open('conf/conf.json') as f:
config = json.load(f)
# config variables
model_name = config["model"]
weights = config["weights"]
include_top = config["include_top"]
train_path = config["train_path"]
features_path = config["features_path"]
labels_path = config["labels_path"]
test_size = config["test_size"]
results = config["results"]
model_path = config["model_path"]
# start time
print("[STATUS] start time - {}".format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M")))
start = time.time()
# create the pretrained models
# check for pretrained weight usage or not
# check for top layers to be included or not
if model_name == "vgg16":
base_model = VGG16(weights=weights)
model = Model(input=base_model.input, output=base_model.get_layer('fc1').output)
image_size = (224, 224)
elif model_name == "vgg19":
base_model = VGG19(weights=weights)
model = Model(input=base_model.input, output=base_model.get_layer('fc1').output)
image_size = (224, 224)
elif model_name == "resnet50":
base_model = ResNet50(weights=weights)
model = Model(input=base_model.input, output=base_model.get_layer('flatten').output)
image_size = (224, 224)
elif model_name == "inceptionv3":
base_model = InceptionV3(include_top=include_top, weights=weights, input_tensor=Input(shape=(299,299,3)))
model = Model(input=base_model.input, output=base_model.get_layer('custom').output)
image_size = (299, 299)
elif model_name == "inceptionresnetv2":
base_model = InceptionResNetV2(include_top=include_top, weights=weights, input_tensor=Input(shape=(299,299,3)))
model = Model(input=base_model.input, output=base_model.get_layer('custom').output)
image_size = (299, 299)
elif model_name == "mobilenet":
base_model = MobileNet(include_top=include_top, weights=weights, input_tensor=Input(shape=(224,224,3)), input_shape=(224,224,3))
model = Model(input=base_model.input, output=base_model.get_layer('custom').output)
image_size = (224, 224)
elif model_name == "xception":
base_model = Xception(weights=weights)
model = Model(input=base_model.input, output=base_model.get_layer('avg_pool').output)
image_size = (299, 299)
else:
base_model = None
print("[INFO] successfully loaded base model and model...")
# path to training dataset
train_labels = os.listdir(train_path)
# encode the labels
print("[INFO] encoding labels...")
le = LabelEncoder()
le.fit([tl for tl in train_labels])
# variables to hold features and labels
features = []
labels = []
# loop over all the labels in the folder
count = 1
for i, label in enumerate(train_labels):
cur_path = train_path + "/" + label
count = 1
for image_path in glob.glob(cur_path + "/*.jpg"):
img = image.load_img(image_path, target_size=image_size)
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
feature = model.predict(x)
flat = feature.flatten()
features.append(flat)
labels.append(label)
print("[INFO] processed - " + str(count))
count += 1
print("[INFO] completed label - " + label)
# encode the labels using LabelEncoder
le = LabelEncoder()
le_labels = le.fit_transform(labels)
# get the shape of training labels
print("[STATUS] training labels: {}".format(le_labels))
print("[STATUS] training labels shape: {}".format(le_labels.shape))
# save features and labels
h5f_data = h5py.File(features_path, 'w')
h5f_data.create_dataset('dataset_1', data=np.array(features))
h5f_label = h5py.File(labels_path, 'w')
h5f_label.create_dataset('dataset_1', data=np.array(le_labels))
h5f_data.close()
h5f_label.close()
# save model and weights
model_json = model.to_json()
with open(model_path + str(test_size) + ".json", "w") as json_file:
json_file.write(model_json)
# save weights
model.save_weights(model_path + str(test_size) + ".h5")
print("[STATUS] saved model and weights to disk..")
print("[STATUS] features and labels saved..")
# end time
end = time.time()
print("[STATUS] end time - {}".format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M")))
-
How to run this script?
Open up a command prompt and go into the folder where you saved this file. Type python extract_features.py. It will extract all the features from the images in your dataset and store it in HDF5 format locally. -
What this script does?
The pre-trained models are loaded from the application module of Keras library and the model is constructed based on the user specified configurations in the conf.json file. After that, features are extracted from the user-specified layer in the model pre-trained with ImageNet dataset. These features along with its labels are stored locally using HDF5 file format. Also, the model and the weights are saved just to show that these could also be done in Keras.
The below table shows the feature vector size for each image for a particular deep neural net model that I used.
| Model | Feature vector size |
|---|---|
| VGG16 | (1, 4096) |
| VGG19 | (1, 4096) |
| InceptionV3 | (1, 131072) |
| ResNet50 | (1, 2048) |
| InceptionResNetV2 | (1, 98304) |
| Xception | (1, 2048) |
| MobileNet | (1, 50176) |
3. Training a machine learning model
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# organize imports
from __future__ import print_function
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import numpy as np
import h5py
import os
import json
import pickle
import seaborn as sns
import matplotlib.pyplot as plt
# load the user configs
with open('conf/conf.json') as f:
config = json.load(f)
# config variables
test_size = config["test_size"]
seed = config["seed"]
features_path = config["features_path"]
labels_path = config["labels_path"]
results = config["results"]
classifier_path = config["classifier_path"]
train_path = config["train_path"]
num_classes = config["num_classes"]
classifier_path = config["classifier_path"]
# import features and labels
h5f_data = h5py.File(features_path, 'r')
h5f_label = h5py.File(labels_path, 'r')
features_string = h5f_data['dataset_1']
labels_string = h5f_label['dataset_1']
features = np.array(features_string)
labels = np.array(labels_string)
h5f_data.close()
h5f_label.close()
# verify the shape of features and labels
print("[INFO] features shape: {}".format(features.shape))
print("[INFO] labels shape: {}".format(labels.shape))
print("[INFO] training started...")
# split the training and testing data
(trainData, testData, trainLabels, testLabels) = train_test_split(np.array(features),
np.array(labels),
test_size=test_size,
random_state=seed)
print("[INFO] splitted train and test data...")
print("[INFO] train data : {}".format(trainData.shape))
print("[INFO] test data : {}".format(testData.shape))
print("[INFO] train labels: {}".format(trainLabels.shape))
print("[INFO] test labels : {}".format(testLabels.shape))
# use logistic regression as the model
print("[INFO] creating model...")
model = LogisticRegression(random_state=seed)
model.fit(trainData, trainLabels)
# use rank-1 and rank-5 predictions
print("[INFO] evaluating model...")
f = open(results, "w")
rank_1 = 0
rank_5 = 0
# loop over test data
for (label, features) in zip(testLabels, testData):
# predict the probability of each class label and
# take the top-5 class labels
predictions = model.predict_proba(np.atleast_2d(features))[0]
predictions = np.argsort(predictions)[::-1][:5]
# rank-1 prediction increment
if label == predictions[0]:
rank_1 += 1
# rank-5 prediction increment
if label in predictions:
rank_5 += 1
# convert accuracies to percentages
rank_1 = (rank_1 / float(len(testLabels))) * 100
rank_5 = (rank_5 / float(len(testLabels))) * 100
# write the accuracies to file
f.write("Rank-1: {:.2f}%\n".format(rank_1))
f.write("Rank-5: {:.2f}%\n\n".format(rank_5))
# evaluate the model of test data
preds = model.predict(testData)
# write the classification report to file
f.write("{}\n".format(classification_report(testLabels, preds)))
f.close()
# dump classifier to file
print("[INFO] saving model...")
pickle.dump(model, open(classifier_path, 'wb'))
# display the confusion matrix
print("[INFO] confusion matrix")
# get the list of training lables
labels = sorted(list(os.listdir(train_path)))
# plot the confusion matrix
cm = confusion_matrix(testLabels, preds)
sns.heatmap(cm,
annot=True,
cmap="Set2")
plt.show()
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How to run this script?
Open up a command prompt and go to the folder where you saved this file. Type python train.py. It will train the Logistic Regression classifier with the features and labels extracted and stored locally. Finally, it prints the RANK-1 and RANK-5 accuracies of the model on unseen test data. -
What this script does?
The features and labels extracted from your dataset are loaded. Logistic Regression model is created to train these features and labels. The trained model could then be used to predict the label of unseen images. I have added some code to visualize the confusion matrix of the trained model on unseen test data splitted using scikit-learn and seaborn.
Show me the numbers
The below tables shows the accuracies obtained for each pretrained model used to extract features from FLOWERS17 dataset using different configuration settings.
Result-1
- test_size : 0.10
- classifier : Logistic Regression
| Model | Rank-1 accuracy | Rank-5 accuracy |
|---|---|---|
| Xception | 97.06% | 99.26% |
| InceptionV3 | 96.32% | 99.26% |
| VGG16 | 85.29% | 98.53% |
| VGG19 | 88.24% | 99.26% |
| ResNet50 | 56.62% | 90.44% |
| MobileNet | 98.53% | 100.00% |
| Inception ResNetV2 |
91.91% | 98.53% |
Result-2
- test_size : 0.30
- classifier : Logistic Regression
| Model | Rank-1 accuracy | Rank-5 accuracy |
|---|---|---|
| Xception | 93.38% | 99.75% |
| InceptionV3 | 96.81% | 99.51% |
| VGG16 | 88.24% | 99.02% |
| VGG19 | 88.73% | 98.77% |
| ResNet50 | 59.80% | 86.52% |
| MobileNet | 96.32% | 99.75% |
| Inception ResNetV2 |
88.48% | 99.51% |
Notice how InceptionV3 outperforms the other Deep Neural Net architectures. This could mainly be due to the presence of 9 network-in-a-network modules codenamed as Inception modules which applies different convolutional filter sizes parallely to an input volume and concatenates the result at output. More details about this can be found in this astounding paper by C. Szegedy et al. - Going Deeper with Convolutions.
Thus, we have built our own Flower Species Recognition System using Deep Neural Nets and Keras. Our system is able to obtain much higher accuracies than state-of-the-art accuracies (which mostly used hand-crafted features for shape, color and texture representations) in this FLOWERS17 dataset. Using this procedure, you could use these pretrained models for your own image dataset and reduce the time consumed to construct a deep neural net from scratch.
Testing on new images
To test on new flower images, we need to have some test images in dataset/test folder. Please use this script to make predictions on unseen test images.
Issues and Workarounds
(Updated on 10/06/2018)
-
Intel MKL FATAL ERROR: Cannot load mkl_intel_thread.dll
When I installed Anaconda with Python 3.6 to enable TensorFlow on Windows 10, I got this error. Tried googling the error and this link gave me a working solution. -
no such layer: flatten or custom
This error is the common one found when trying to add Flatten() in any of the model’s .py file. Please update Keras to the latest version. Completely uninstall Keras and reinstall it using pip. Now, get into the similar directory shown below where you have installed Anaconda.
1
C:\deeplearning\anaconda2\Lib\site-packages\keras\applications\
Inside this directory, you can see all the pre-trained models .py file. If you use InceptionV3 as the model, then open inception_v3.py.
Don’t forget to add the below code on top where imports are written.
1
from ..layers import Flatten
Next, go to the place where you can find the final Dense() layer. Normally, we need to perform Flatten() before the last fully connected layer. This is because the final Dense() layer has the number of classes in ImageNet challenge which is typically 1000. We could take these 1000 activations as (1, 1000) feature vector for a single image. But taking features from intermediate layers makes our classifier learn better.
This is how I inserted Flatten() layer to get features from InceptionV3 model. Notice that I have set include_top as False. This gave me a feature vector of size (1, 131072).
1
2
3
4
5
6
7
8
9
10
11
12
13
14
...
...
if include_top:
# Classification block
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dense(classes, activation='softmax', name='predictions')(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
elif pooling == 'max':
x = GlobalMaxPooling2D()(x)
x = Flatten(name='custom')(x)
...
...
- couldn’t see Dense() layer in “model_name”.py applications folder
This issue is seen in Keras 2.2.0 version update. Keras has externalized the applications module to “keras_applications” from where all the pre-trained models are getting imported. To make changes to the pre-trained_model.py file, simply go to the below directory where you will find all the pre-trained models “.py” files.
1
"python_installation_directory" -> "Lib" -> "site-packages" -> "keras_applications"
In case if you want to keep the previous Keras version, simply do the following two commands.
1
2
pip uninstall keras
pip install keras==2.1.2
References
- Keras - Official Documentation
- Building powerful image classification models using very little data
- A Comprehensive guide to Fine-tuning Deep Learning Models in Keras (Part I)
- A Comprehensive guide to Fine-tuning Deep Learning Models in Keras (Part II)
- 17 Category Flower Dataset
- 102 Category Flower Dataset
In case if you found something useful to add to this article or you found a bug in the code or would like to improve some points mentioned, feel free to write it down in the comments. Hope you found something useful here.


Robert Talbert, Grand Valley State University
27 May 2015
Flipped learning is a pedagogical approach in which direct instruction moves from the group learning space to the individual learning space, and the resulting group space is transformed into a dynamic, interactive learning environment where the educator guides students as they apply concepts and engage creatively in the subject matter.
Think/pair/share: What's good? What could go wrong and why?
Setting clear expectations
Giving student choices
Giving clear, action-oriented learning objectives
Providing rich set of resources and tools
Making learning iterative and feedback-based
Is minimal
Gives immediate results
Explicity tied to learning objectives
Incentivizes risk-taking
Guided Practice is a potential model for pre-formative assessment
Robert Talbert, Associate Professor of Mathematics
Grand Valley State University, Allendale, Michigan USA
Presentation: rtalbert.org/pearsontoronto


Class ArucoObject
Describes the shared properties of all the ArUco objects. Trackers, Creators and Calibrators use this interface.
Inheritance
Namespace: ArucoUnity.Objects
Assembly: Assembly-CSharp.dll
Syntax
[ExecuteInEditMode]
public abstract class ArucoObject : MonoBehaviour
Fields
| Improve this Doc View SourcemetersToPixels300ppp
Declaration
protected const float metersToPixels300ppp = 11811.0234F
Field Value
| Type | Description |
|---|---|
| System.Single |
Properties
| Improve this Doc View SourceArucoHashCode
Declaration
public int ArucoHashCode { get; protected set; }
Property Value
| Type | Description |
|---|---|
| System.Int32 |
Dictionary
Gets or sets the dictionary to use.
Declaration
public Aruco.Dictionary Dictionary { get; set; }
Property Value
| Type | Description |
|---|---|
| Aruco.Dictionary |
DisplayInEditor
Declaration
public bool DisplayInEditor { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
MarkerBorderBits
Gets or sets the number of bits in marker borders (default: 1). Used by Creators.
Declaration
public uint MarkerBorderBits { get; set; }
Property Value
| Type | Description |
|---|---|
| System.UInt32 |
MarkerSideLength
Gets or sets the side length of each marker. In pixels for Creators. In meters for Trackers and Calibrators.
Declaration
public float MarkerSideLength { get; set; }
Property Value
| Type | Description |
|---|---|
| System.Single |
Methods
| Improve this Doc View SourceAwake()
Calls UpdateProperties().
Declaration
protected virtual void Awake()
Draw()
Returns the image of the ArUco object. In editor it should returns null and no exception if the object is incorrectly configured.
Declaration
public abstract Cv.Mat Draw()
Returns
| Type | Description |
|---|---|
| Cv.Mat | The image of the ArUco object. |
GenerateName()
Returns a generated name depending on the value of the properties.
Declaration
public abstract string GenerateName()
Returns
| Type | Description |
|---|---|
| System.String |
GetGameObjectScale()
Gets the scale to MarkerSideLength length.
Declaration
public abstract Vector3 GetGameObjectScale()
Returns
| Type | Description |
|---|---|
| UnityEngine.Vector3 |
GetInPixels(Single)
Returns a value as an int, with a conversion from meters to pixels if the property value is less than 10.
Declaration
protected int GetInPixels(float propertyValue)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Single | propertyValue |
Returns
| Type | Description |
|---|---|
| System.Int32 |
OnPropertyUpdated()
Calls UpdateProperties() and the PropertyUpdated event.
Declaration
protected void OnPropertyUpdated()
OnPropertyUpdating()
Calls the event PropertyUpdating.
Declaration
protected void OnPropertyUpdating()
OnValidate()
Calls OnPropertyUpdated() in editor mode.
Declaration
protected virtual void OnValidate()
UpdateArucoHashCode()
Updates the ArUco hash code of the object.
Declaration
protected abstract void UpdateArucoHashCode()
UpdateProperties()
Initializes the properties and calls the UpdateArucoHashCode() method.
Declaration
protected virtual void UpdateProperties()
Events
| Improve this Doc View SourcePropertyUpdated
Executed after a property has been updated.
Declaration
public event Action<ArucoObject> PropertyUpdated
Event Type
| Type | Description |
|---|---|
| System.Action<ArucoObject> |
PropertyUpdating
Executed before a property is going to be updated.
Declaration
public event Action<ArucoObject> PropertyUpdating
Event Type
| Type | Description |
|---|---|
| System.Action<ArucoObject> |


?Sabatia
stellarti
Pursh
Det.-rev.
M
Thiv
(B)
2001
7if
Cj
PLANTS
OF
THE
BAHAMAS
Sabatia
stellarla
Pursh
Andros:
east
end
of
airport
runway,
Andros
Town,
Edge
of
pineland
in
open,
Flowers
pink
to
white,
S.R.Hill
No.
2997
May
24,
1975
Herbarium
FAIRCHILD
TROPICAL
GARDEN
1624749
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Today's TODO list! February 16th 2018 Take vitamins. Go to work. Advance with globants bootcamp exercises. Advance with my Android project coding. Feed the cat


In the administration portal, online help is available in the language the user has set in his profile. The same applies to the FAQs that can be opened on a marketplace.
The online help and FAQ files are provided after installation in a separate .war file contained in the oscm-install-pack.zip file:
<install_pack_dir>/domains/bes_domain/oscm-portal-help.war.
You can update the existing online help and FAQ files and/or provide the files in another language:
- Extract the content of the oscm-portal-help.war file to a separate directory on your file system.
You see the following directory structure:
The languages provided after installation are English (en), German (de), and Japanese (ja). You can update the texts in their respective directories. The procedure is the same as when providing the texts in a different language (see below).
- Copy the help/en directory and name it help/<ISO code>.
Copy the faq/en directory and name it faq/<ISO code>.
The ISO language code denotes the language for which you want to provide translations. For example, add a cs directory to the help as well as to the faq directory. Click here for the list of supported language codes.
The directory structure now looks as follows:
- Translate the HTML files.
Be aware of the following:
- Make sure not to change any style sheet or icon.
- Do not remove any files that you do not translate.
- In the HTML files, make sure not to enter tags or invalid HTML code that may affect the page layout.
- Use a separate test environment for testing your translations.
- Images used in the online help topics can be translated as follows:
- Create the image with an image drawing tool.
- Save the image with the file name referenced in the related HTML file to the Shared/_images directory.
- Check the size of the image in the HTML topic and adapt it, if required.
- Create a new oscm-portal-help.war file containing the additional subdirectories for the new language.
- Undeploy and deploy or redeploy the customized oscm-portal-help.war file to the bes-domain domain on your application server. Refer to the Installation Guide for details.
By deploying the file, existing HTML files are overwritten.
Note: You are responsible for keeping the directory structures and files consistent!


package syntactic
This package is used to describe syntax for LL(1) languages.
To use the package, mix-in the scallion.syntactic.Syntaxes trait.
object MySyntaxes extends Syntaxes { // Type of tokens. type Token = MyToken // Type of token kinds. type Kind = MyKind // Define the token kind of tokens. override def getKind(token: Token): Kind = ... // Then define your syntax using combinators. lazy val mySyntax = ... }
- Source
- package.scala
- Grouped
- Alphabetic
- By Inheritance
- syntactic
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Type Members
-
trait
Enumeration extends AnyRef
Provides enumeration capabilites to syntaxes.
-
trait
Operators extends AnyRef
Contains utilities to write syntaxes with infix, prefix and postfix operators.
Contains utilities to write syntaxes with infix, prefix and postfix operators. Expected to be mixed-in to
Syntaxes
.Example
The following example shows how to build a syntax for a language with unary operators and infix operators of different priority levels.
// Syntax for basic expressions, which are our operands. lazy val basic: Syntax[Expr] = number | open.skip ~ value ~ close.skip // Syntax for postfix application of an operator. lazy val postfixed: Syntax[Expr] = postfixes(basic, fac)({ // Indicates how to apply the operator. case (e, op) => UnaryExpr(op, e) }, { // Optionally, indicates how to unapply an operator. // This is only used for pretty printing. case UnaryExpr(op, e) => (e, op) }) // Syntax for infix application of various operators. lazy val value: Syntax[Expr] = recursive { // In this case, our operands are postfixed basic expressions. operators(postfixed)( // Indicates the various operators and their associativity. // First level in multiplication and division, which are // left associative and bind the strongest. times | div is LeftAssociative, // On the next priority level are addition and subtraction, // which are also left associative. plus | minus is LeftAssociative )({ // Indicates how to apply a binary operator. case (l, op, r) => BinaryExpr(op, l, r) }, { // Optionally, indicates how to unapply a binary operator. // This is only used for pretty printing. case BinaryExpr(op, l, r) => (l, op, r) }) }
-
implicit
class
PairDecorator[A] extends AnyRef
Adds an
~
methods to build pairs. -
trait
PrettyPrinting extends AnyRef
Provides pretty printing capabilites to syntaxes.
- trait Syntaxes extends AnyRef
-
case class
~[+A, +B](_1: A, _2: B) extends Product with Serializable
Simply a pair.
Simply a pair.
Can be used in infix position in pattern matching.
- _1
First element.
- _2
Second element.
Overview
Scallion is a library for easily writing lexers, parsers and pretty printers in Scala. See the package scallion for more information.


{%extends "registration/base.html"%} {% load bootstrap4 %} {% block content %}
© 2020 Neighborhood Watch SigIn Form.


David Tremont, Model maker, Weta Workshop, New Zealand
Even being part of a distinguished organization does not take away normal dreams and passions, and thus in defying his father, Alan takes Tin-Tin and the rest of the team on an adventure they believe in enough to risk all with International Rescue. For Alan and Tin-Tin the challenges are great, but the personal gains will become far greater. This is another unique adventure with International Rescue, addressing a few of their personal issues only lightly hinted at in the TV series and revealing a more serious side of this dedicated family.
Alan Tracy, the youngest of the Tracy sons, has always worked to distinguish himself as a member of the International Rescue team. Now his goals are put to the test as he faces challenges that cause him to evaluate his role in the organization, and at the same time, strengthen his relationship with the love of his life, Tin-Tin. Can Alan maintain his balance with home, job, romance, and race car driving? And what about the mysterious golden aircraft that seems to have appeared out of nowhere and captured the team’s attention?
Daria (D.H.) Brooks, author of A Legacy of the Pacific
For his entire life, Alan Tracy has been the 'baby brother' of the Tracy clan. In Danger Zone, he is forced to take on a more mature role on the valiant International Rescue team. In the process, a series of exciting events give Alan pause to ponder his future as a rescue hero and race car driver. Will the future he dreams of also include Tin-Tin, IR's talented engineer...?


Some Button Styles
By: Blake Tarter
Secondary
Call
Ghost
Disabled
Call To Action
Ghost
Secondary
Disabled
Call To Action
Ghost


I'm a post-doctoral reasearcher at the Institut Jean Nicod (ENS). Please contact me at keny.chatain (at) ens (dot) psl (dot) eu.
The questions that haunt me:
-
Semantics of pronouns and anaphors:
Under what conditions can a noun phrase serve as a pronoun's antecedent? What is the division of labour between grammatical constraints and extra-grammatical constraints (e.g. pragmatics, processing, etc.) in pronoun reference and resolution? What drives the existential/universal ambiguity in donkey pronouns? Can we derive context-change potentials of elements from their truth-conditions only?
-
Semantics of plurality, distributivity and cumulativity:
What is the complete inventory of operators and combinators needed to provide an adequate semantics for pluralities? What is the origin of homogeneity in plural?
-
Implicature calculation:
Why is implicature calculation sometimes sensitive to contextual factors, sometimes blind to them? Why are our best generalization about implicature calculation so computationally expensive? Can our implicature calculation algorithm be grounded in deeper principles of language (e.g. cell identification, Bayesian reasoning, Gricean maxims, etc.)?
$news$
How I pronounce my name: [keˈnih ʃaˈtɛ̃]
If it's easier for you, feel free to anglicize it. Here's one way to do so: [ˈkhɛnɪ (t)ʃəˈtɛɪn]


This service allows you to modify and control the controller's (UUT) default IO behavior. You reference an IO control by ID.
To delete, right-click the object in the configuration tree and select Delete from the shortcut menu.
To delete multiple items, right-click the object in the configuration tree and select Delete Multiple Items from the shortcut menu.
Mode defines the I/O control type. The values are application specific. The usual values are:
Mode mode defines the type of I/O control. The values are application specific. The usual values are:
Not Supported.


Leganés hires Mexican coach Javier Aguirre
MADRID (AP) - Leganés hired Mexican coach Javier Aguirre on Monday to try to lift the Madrid club out of last place in the Spanish league.
Leganés made the announcement a day after it lost to Eibar 2-1 at home and dropped six points from safety.
Aguirre replaces Luis Cembranos, who had taken over as an interim coach after Mauricio Pellegrino left last month.
Aguirre, a former Mexico player, previously coached Spanish clubs Osasuna, Atlético Madrid, Espanyol and Zaragoza. He also coached the national teams of México, Japan and Egypt. His last job had been with Egypt, which he coached until July.
In México, he played for América and Guadalajara, and coached Atlante and Pachuca.
The 60-year-old Aguirre is one of more than 40 people awaiting a ruling in a match-fixing trial involving a Spanish league game between Levante and Zaragoza at the end of the 2010-11 season, when Aguirre coached Zaragoza.
Aguirre denied any wrongdoing when he recently testified before a judge.
Leganés has five points from a victory and two draws in its first 12 league games. Its next match is at third-place Real Sociedad on Friday.
More AP soccer https://apnews.com/Soccer and https://twitter.com/AP_Sports
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


Qualcomm expects fast 5G adoption
by David MannersYesterday, at its analyst day, Qualcomm said there will be between 175 million and 225 million 5G smartphones in 2020, more than 450 million in 2021 and over 750 million in 2022..
5G adoption will be faster than 4G, says Qualcomm, because of demand from China.
Qualcomm is expected to have an integrated 5G modem in 2020.
Qualcomm sees its growth coming not only from wireless chips but from 5G-associated markets like automotive, computing and IoT.
The Serviceable Available Market (SAM) which the company is addressing is worth $65 billion in 2019, and will be worth $100 billion in 2022, said Qualcomm.


Add templated panels to your layout.
Documentation on Github
Created with care and love. Bruno Correia


Gunman in US school shooting dies as police hunt motive
by AFPLOS ANGELES: The teenager who gunned down classmates at a California high school before shooting himself died Friday, leaving detectives baffled over the motive of a Boy Scout with no obvious alarm bells in his background.
Nathaniel Berhow died in a hospital one day after shooting himself in the head at the end of a 16th birthday rampage that left two fellow students dead.
Berhow had been in critical condition since police officers storming the campus found him lying among the wounded.
The Los Angeles sheriff’s department confirmed Berhow died shortly after 3:30 pm on Friday.
The puzzle of what led him to open fire at Saugus High School, a short commute from Los Angeles, deepened as a picture emerged of a quiet yet sociable teen who was familiar with guns but had no history of troubled behavior.
“We know it was a planned attack, it was deliberate… The motive? We don’t have it yet,” Los Angeles County Sheriff Alex Villanueva told a news conference.
The victims – a boy and a girl died while another three students were wounded- appeared to have been chosen at random, he said.
“There’s no relationships that we can tell (between) the suspect and the victims,” Villanueva added, scotching rumors that an ex-girlfriend had been his target.
There was also no history of the teen being bullied.
The FBI earlier said there is no evidence that the suspect had any ideological motives or acted on behalf of any organisation. No manifesto or suicide note has been found.
Deepening the riddle, Berhow’s late father had owned six registered guns – but none of them matched the .45 caliber semiautomatic the suspect used.
His proficiency with the weapon during his short, deadly ambush suggested he had been firing guns for some time.
“In 16 seconds he cleared a malfunction and was able to shoot five people and himself, so he seemed very familiar with firing the weapon,” said Villanueva.
Jared Axen, 33, a next door neighbor, described Berhow as an introvert who was usually quiet and reserved but who had friends and could readily be engaged in conversation.
“I would see him go back and forth between scouting. I’d see him do a lot of sports-related activities,” he told the Los Angeles Times.
School records show Berhow was a keen middle-distance track runner.
Aidan Soto, a fellow scout and student at Saugus, described the suspect as a “good kid,” and the last person he would expect to carry out a mass shooting.
“The younger scouts really looked up to him. He was there when they needed him with anything. I’m bewildered and looking for answers.”
Although Berhow had no evident history of violence, his family life over the past few years had been difficult.
His father, divorced from his mother not long before, died of a heart attack in December 2017.
The father had reportedly suffered from alcohol-related problems, and was at one point suspected of domestic violence but never prosecuted for lack of evidence.
Nonetheless, the father and son were seemingly close.
“He would tell me that he missed his father and that he loved him,” Axen said.
The father was a keen hunter whom neighbors would see manufacturing ammunition in his garage. Investigators said they found additional arms at the home, beyond the six registered weapons.
Berhow would sometimes accompany his father on hunting trips, according to Axen – accounting for the deadly accuracy of his shooting.
He also “knew how many rounds he had,” noted Villanueva, the sheriff. The teen gunman saved his final bullet for himself.
The students slain by Berhow were identified as Gracie Anne Muehlberger, 15, and a 14-year-old boy.
Messages on Muehlberger’s Instagram page read “I miss you so much already love,” and “It’s so sad that another innocent person has to die.”
Another wounded teenager was able to return home Friday morning, and two girls — both operated on for gunshot wounds – should be able to leave the hospital in a few days, say medics.
Tearful prayer vigils were held Thursday night, and all schools in the district remained closed Friday in a mark of respect, Deputy Superintendent Mike Kuhlman said.


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{% extends "page.html" %} {% block main %}
Tour Wode.AI
Jupyter Notebook 101
Tour NLP Server
Tour Deep Learning Server
Python Quick Demo
R Quick Demo
Julia Quick Demo
Have Some Fun!


seg=LOGICAL2SEG(x,v)
seg=LOGICAL2SEG(x,v) given a vector of indices or a logical index vector v, this function will return segments in x defined by those indices.


Jiaxing, director of the triangle and choke his throat, east facing the sea, south of the Qian Tang River, north of Taihu, west of Tianmu water, the Grande Canale straight through the territory. Since ancient times in the river, sea, lake, river, and the intersection, Shanghai, Hangzhou, Suzhou, lake and other places are far less than 100, seize the good fortune of nature, world of aura, on a diet, from ancient to modern times, especially pay attention to the birth of many well-known snacks and food. Such as Jiaxing dumplings, lake crab, Trapaceae, Duck in Brown Sauce Wen Hu, steamed pork with rice flour, cake Gorgon, Bazhen cake, Egg Preserved in Rice Wine, one of the most representative when the number of Jiaxing dumplings.


| testApp {shinytest} | R Documentation |
Run tests for a Shiny application
testApp( appDir = ".", testnames = NULL, quiet = FALSE, compareImages = TRUE, interactive = is_interactive(), suffix = NULL )
appDir |
Path to directory containing a Shiny app (e.g. |
testnames |
Test script(s) to run. The .R extension of the filename is
optional. For example, |
quiet |
Should output be suppressed? This is useful for automated testing. |
compareImages |
Should screenshots be compared? It can be useful to set
this to |
interactive |
If there are any differences between current results and expected results, provide an interactive graphical viewer that shows the changes and allows the user to accept or reject the changes. |
suffix |
An optional suffix for the expected results directory. For
example, if the suffix is |
snapshotCompare()
and snapshotUpdate()
if
you want to compare or update snapshots after testing. In most cases, the
user is prompted to do these tasks interactively, but there are also times
where it is useful to call these functions from the console.


clustalw2fasta [options] <alignment file>
This program converts a multiple alignment in clustalw format into a FASTA file.
The name of a file containing one or more aligned sequences in Clustalw format.
Writes FASTA formatted sequences to standard output.
The program outputs each of the sequences in the multiple alignment.
| Option | Parameter | Description | Default Behavior |
|---|---|---|---|
| Input/Output | |||
| -consensus | threshold | Rather than printing each of the sequences to the FASTA file, the program prints their consensus, with gaps (hyphens) inserted whenever the consensus character occurs in less than threshold% of the sequences. | Output the sequences as normal. |
| -nogap | Do not include gap characters in the output. | Include the gap characters. | |
| -seqorder | seqorder filename | Output the sequences in the order given in the seqorder filename file. This file must contain a superset of the sequences in the Clustalw alignment file. | Output the sequences in the same order that they appear in the input. |


A detailed look at why StrongLifts & Starting Strength aren't great beginner programs, and how to fix them - lvysaur's Beginner 4-4-8 Program
I sort of shit on SL and SS a lot here, and I've found myself explaining why repeatedly, so I figured I'd make an effortpost with critique/solutions.
StrongLifts and Starting Strength might be the most often-recommended programs on this subreddit, and they’re pretty good at introducing new lifters to the gym. However, if you plan on using them to gain strength or muscle mass, they’re poor options.
Many of you might say you’ve ran SL and SS and got fine results, but that’s not really what makes a good program for a new lifter. New lifters can see results from doing damn near anything. A good beginner program gets you results efficiently. So, here’s why I believe the base SL and SS programs are suboptimal, and how you should tweak them. To keep things simple, I’ll be focusing my critiques on SL, but the majority carries over to SS as well.
1: Lack of frequency
This one is especially funny since SL is often referred to as a high-frequency program. I disagree with that label. Over a two-week period, you hit your pecs, biceps, and shoulders (the muscles most guys care about for aesthetics) only three times. Elevated muscle protein synthesis, the driver of hypertrophy (1), lasts roughly 36 hours after training (2). Since you should keep protein synthesis elevated as much as possible, averaging 112 hours between muscle stimuli as SL does is far from optimal. Even while controlling for volume and intensity, high frequency generally beats out low frequency for both muscle and strength development (3).
Fix: Bench and Overhead Press every workout.
2: Lack of volume
Increase in muscle mass trends logarithmically over time, meaning beginner lifters can gain the most muscle in the least amount of time. This is important even if you’re only interested in strength, because increasing your muscle mass is the best way to increase your strength (4). Researchers recommend a minimum of ten weekly sets for each muscle to see good results (8)- SL gives you 7.5 for pecs, shoulders, and biceps, and 1.5 for hamstrings. This means increasing sets (5) and reps per set (6) are the best ways to maximize both aesthetic and strength progress.
Fix: Do more sets and more reps when possible.
3: Lack of bicep involvement
Rows and cleans alone aren’t great at growing biceps. Your biceps are only secondary muscles involved in the movements, meaning they won’t really be taken anywhere close to failure. Since number of sets near muscular failure is the primary factor when inspiring protein synthesis (6), your bicep development will be subpar if you don’t fix this.
Fix: Ideally, add Chin-Ups. Not only do they hit the biceps hard, but the extra back work will balance out your increased benching. If you can’t do Chin-Ups, do 30° Bent-Over Rows with supinated grip, which shifts emphasis to the biceps (7).
4: Poopoo lower body programming
If you’re mainly interested in aesthetics, SL is probably a bit squat-heavy for you. If you’re mainly interested in strength, 1x5 deadlifts three times every two weeks is a bad plan. Taking the time to warm up and load the bar, only to perform one serious set is not an efficient way to spend your time. The idea that you should only do 1 set because CNS fatigue is silly. SL is made for beginners using light weight. CNS fatigue is not an issue for them. The idea that you should only do 1 set to avoid form breakdown is flawed as well. The programs are designed to eventually push you to form breakdown regardless of how many sets you’ve performed, so that’s inevitable. On top of all of this, Deadlifts are really the only exercise in the program that hit your hamstrings (no, Squats don’t to a significant degree [9])- why give them a measly 3 total sets every two weeks?
Fix: Squat less, Deadlift more. The increased leg work from Deadlifts will make up for decreasing Squats.
5: No periodization
This one’s simple: Not varying your rep ranges is sub-optimal for both strength and hypertrophy progress (10). SL sacrifices this great strategy in the name of simplicity. I think you can handle a little variation.
Fix: Vary your rep ranges.
6: Boring start
If you’re healthy and just getting into lifting, it can take you a considerable amount of time to get to challenging weights following the typical SL progression scheme. Going so long without being challenged will have new lifters quitting before they even start touching real weights.
Fix: Use infrequent AMRAP (as many reps as possible) sets to determine progression speed.
Applying all the changes I suggested, you get something like this:
lvysaur’s Beginner 4-4-8 Program
The weight progression is a tiny bit more complicated, but I think most are willing to put in a few minutes of extra thought in exchange for better progress. Since your 8 rep weight should be ~90% of your 4 rep weight, your 4 rep weight would begin at 50lb if you want to start with the bar for 8 rep sets.
Here are some questions I anticipate:
Why 4 sets?
To respect time constraints. Do 5 if you want.
I can’t do Chin-Ups?
30° Bent-Over Rows with supinated grip.
I can do more than 4/8 Chin-Ups?
Weighted Chin-Ups.
How much do I deload after failure?
No change here. 10% as a beginner.
How do I progress?
Smallest incremental jumps possible between workouts to meet the weekly goal.
Why alternate Squats and Deadlift days?
Deadlifting after Squatting sucks, and doing both on the same day would take a lot of time. Do both every day if you want. I also didn’t want to suggest something like 2 sets of each lift every day because that would become inefficient when you factor in warming up, loading the bar, waiting for a rack, etc.
Why not alternate starting Overhead Press vs starting Bench?
Doing so would introduce fluctuating levels of fatigue to the lifts, which I don’t think is appropriate %RM based linear progression program. It can definitely work if you used RPE to autoregulate your weights, but I don’t think beginners have the experience necessary to do so.
I opted to start Bench because more people care about Bench. Start Overhead Press if you want.
Sources – thanks /u/gnuckols
-
https://www.unm.edu/~lkravitz/Article folder/musclesgrowLK.html
-
http://strengtheory.com/powerlifters-should-train-more-like-bodybuilders/
-
http://breakingmuscle.com/strength-conditioning/pull-up-vs-chin-up-a-comparison-and-analysis
-
http://www.tandfonline.com/doi/full/10.1080/02640414.2016.1210197
-
http://strengtheory.com/in-defense-of-program-hoppers-dup-revisited/


The DFS method performs a depth--first search [clr98] of a graph. A depth--first search visits vertices that are further to the source before visiting vertices that are closer away. In this context `distance' is defined as the number of edges in the shortest path from the source vertex.
Figure 1. Pseudocode for the DFS algorithm
DFS(G) { For each v in V, { color[v]=white; pred[u]=NULL } time=0; For each u in V If (color[u]=white) DFSVISIT(u) } DFSVISIT(u) { color[u]=gray; d[u] = ++time; For each v in Adj(u) do If (color[v] = white) { pred[v] = u; DFSVISIT(v); } color[u] = black; f[u]=++time; }
| Next | ||
| Bibliography |


dark-sky
Crystal implementation of the DarkSky weather API
Based primarily the Go implementation.
Installation
- Add the dependency to your
shard.yml
:
dependencies:
dark-sky:
github: dscottboggs/dark-sky
- Run
shards install
Usage
require "dark-sky"
ENV["darksky_api_key"] = "your-api-key-here"
weather = DarkSky::Forecast.new(longitude: -58.47028, latitude: -34.52959)
weather.retreive # current weather
weathe.at(Time.now + 1.day) # weather tomorrow.
Contributing
- Fork it (<https://github.com/dscottboggs/dark-sky/fork>)
- Create your feature branch (
git checkout -b my-new-feature
) - Commit your changes (
git commit -am 'Add some feature'
) - Push to the branch (
git push origin my-new-feature
) - Create a new Pull Request
Contributors
- D. Scott Boggs - creator and maintainer


Welcome to ClubHub, a platform to streamline communication between college campus organizations and college students
Get started by logging in below!


If you precise inputs appInstanceId and operations, it automatically calls the api /scopedSearch so the results are only the users having one of the specified operations for the specified app instance
appInstanceId
operations
/scopedSearch
{{ user | json }}


Empowering you and your community with easy to use and personalized rights when you need them. It's time to take your safety in your hands.
We give you the resources you need, through a simple to use app, to show you the stept that you could take in a tight situation.
Give back to your community by becoming a partner of PocketLaw. If you're a lawyer and can give proper, legal feedback, contact us!
CEO
Biz Wiz
Lead Developer
Android Developer
Developer
Back-End Developer
Business Development Specialist
Recruiting Specialist


Deuteronomy 1:14
And ye answered me, and said, The thing which thou hast spoken is good for us to do.


Note: Most of the enumeration scripts will do post exploitation gathering too if run as Administrator/root!
Linux
https://github.com/mubix/post-exploitation/wiki/Linux-Post-Exploitation-Command-List
Windows
http://www.handgrep.se/repository/cheatsheets/postexploitation/WindowsPost-Exploitation.pdf
https://github.com/mubix/post-exploitation/wiki/windows


Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Romor Atlantic Limited
Legal Name:
Romor Atlantic Limited
Operating Name:
ROMOR Atlantic Limited
Mailing Address:
41 Martha AveMount Uniacke Industrial Park
MOUNT UNIACKE, Nova Scotia
B0N 1Z0
Location Address:
41 Martha AveMount Uniacke Industrial Park
MOUNT UNIACKE, Nova Scotia
B0N 1Z0
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Company Profile
NATURE OF BUSINESS
ROMOR Atlantic Limited is manufacturers' sales representative and
distributor for oceanographic, geophysical, defence and offshore
oil & gas equipment, instrumentation and supplies. We are Canada's leading provider of complete intergration of equipment and services to the marine industry. We offer sales, ocean instrumentation and technology consulting, training, maintenance logistics for our products and service to the Canadian marketplace. Our vendors are industry leaders from the United States, Europe and Canada.
Our major clients include Bedford Institute of Oceanography,
Geological Survey of Canada, Department of National Defence of
Canada, Vemco Limited, Satlantic Inc., Canadian Border Services and major survey and geophysical companies.
COMPANY HISTORY
The company began in 1984 as the east coast office of Romor
Equipment, an Ontario-based manufacturers' sales representative.
In 1992 Mr. Darrin Verge purchased the business and the copany was incorporated as ROMOR Atlantic Limited in 1994. In 2007 the company expanded into a 4800 square foot facility and has plans to increase its staff to provide added value for our existing and future clients.
COMPANY MANAGEMENT
Born and raised in Nova Scotia, Mr. Verge graduated from
Toronto's DeVry Institute of Technology in 1983 with a diploma in
Electronic Technology. Under Mr. Verge's ownership, annual
revenue from company sales has steadily increased from 1992.
TECHNOLOGY FOCUS
We supply Canadian businesses and research institutions with
high quality OEM oceanographic and geophysical equipment and
supplies. ROMOR Atlantic Limited's major applications for the
client base include ocean mapping, seismic profiling, underwater
acoustics, pressure measurements, underwater video, subsea
cable, control umbilical, connectors, cable and pipe tracking,
vessel motion monitoring, noise measurement, positioning and
navigation.
Our Products Include:
Flotation products
Underwater Acoustics/ Modems/ Releases
ROVs
Underwater cables and umbilicals
Thermal graphic recorders
Underwater electrical connectors
Profiling Sonars Systems
Pressure and Depth Sensors
Quality Machining
Underwater Lights, Pan & Tilts, Cameras
Vessel Motion Monitoring
Pipe and cable tracking
Acoustic Tracking
Hydrographic Survey Tools
Hydrophones, Towed & Bottom Mounted
DGPS
In addition to high quality products, ROMOR Atlantic Limited's
manufacturers provide excellent service, warranties and timely
responses to client inquiries. We also lease and rent many of
the instruments that we sell.
Innovative Features
Because the fields of oceanography and geophysics are driven by
advancements in research, they are rapidly changing and
difficult to predict. ROMOR Atlantic Limited has a reputation
for keeping abreast of these changes in the field and in
individual client needs.
From ROMOR Atlantic Limited you can expect a broad selection of
top-quality products and services, timely follow-up and accurate
up-to-date information concerning advances in technology and how
they might affect you.
Competitive Position
Located in the Burnside Industrial Park, with a 4800 square foot office and warehouse, in Dartmouth, Nova Scotia, Romor Atlantic Limited is close to Dalhousie University and the Bedford
Institute of Oceanography; institutions whose names are
respected around the world. We have access to accurate
information, resources and contacts in the oceanographic and
geophysical fields.
ROMOR Atlantic Limited is the exclusive Atlantic Canadian sales
representative for more than 15 internationally known
manufacturers of oceanographic and geophysical equipment.
COMPANY STRATEGY
Having established a solid sales base, ROMOR Atlantic Limited
works with companies integrating systems comprising of products
from several manufacturers. To provide sales, ocean instrumentation and technology consulting, training, maintenance logistics for our products and service to the Canadian marketplace.
Full profile
Full profile
Contact information
Company description
Product / Service / Licensing
on subsurface moorings.
Differential available. (LCD)Liquid crystal displays with
digital interfaces. Operates on rechargeable batteries or power
adapter.
underwater acoustic systems for side-scan/sub-bottom profiling
and underwater acoustic telemetry. We also offer altimeters and
and echo ranging systems and underwater acoustic relocation
devices.
applications, geophysical exploration, offshore oil and gas
exploration and other applications. Ability to design and
custom build cables and/or umbilicals to customer requirements.
and data acquisition and display units.
shell, metal shell, custom coax and geophysical connectors
including miniature rubber molded connectors to heavy duty air
gun connectors. All connectors are designed for rugged
applications and environments.
fully digital image processing. Sonar processors run
multidiscipline survey systems allowing the addition of other
valuable sensors, providing supplemental data to the sonar
imagery.
stage sensors, portable standards, meteorological measurement
systems and fiber optic systems.
wet/dry surface applications to full ocean depth deployments.
All products are pressure tested to the rated depth. Products
range from camera control units to lights.
and cable survey. Products are flexible and adaptable--custom
built solutions available.
providing location, tracking and survey data about the pipeline
or cable's relative position, achieving burial depths that have
previously been unattainable. Can be deployed on a wide range
of subsea vehicles due to it compact size, ease of installation
and use.
complete underwater landscape with significant accuracy. When
combined with other sensors, provides data on underwater terrain
to support numerous survey tasks.
research and military applications. With superior bandwidth,
lower distortion and considerable durability, our products exceed
the demands of the market. We offer down-hole arrays, bottom
cable hydrophone arrays, high-speed towed arrays and data
acquisition systems.
Market profile
Alliances:
- Financial
- Sales/Marketing
- Technology
Geographic markets:
Actively pursuing:
- United Kingdom
- United States
Sector information
Classification codes
Total plant size:
Distribution of employees:
2 administration, 1 management, 1 sales, 1 technical, 1 engineeringThird-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
Products, services and licensing
Product / Service / Licensing
on subsurface moorings.
Differential available. (LCD)Liquid crystal displays with
digital interfaces. Operates on rechargeable batteries or power
adapter.
underwater acoustic systems for side-scan/sub-bottom profiling
and underwater acoustic telemetry. We also offer altimeters and
and echo ranging systems and underwater acoustic relocation
devices.
applications, geophysical exploration, offshore oil and gas
exploration and other applications. Ability to design and
custom build cables and/or umbilicals to customer requirements.
and data acquisition and display units.
shell, metal shell, custom coax and geophysical connectors
including miniature rubber molded connectors to heavy duty air
gun connectors. All connectors are designed for rugged
applications and environments.
fully digital image processing. Sonar processors run
multidiscipline survey systems allowing the addition of other
valuable sensors, providing supplemental data to the sonar
imagery.
stage sensors, portable standards, meteorological measurement
systems and fiber optic systems.
wet/dry surface applications to full ocean depth deployments.
All products are pressure tested to the rated depth. Products
range from camera control units to lights.
and cable survey. Products are flexible and adaptable--custom
built solutions available.
providing location, tracking and survey data about the pipeline
or cable's relative position, achieving burial depths that have
previously been unattainable. Can be deployed on a wide range
of subsea vehicles due to it compact size, ease of installation
and use.
complete underwater landscape with significant accuracy. When
combined with other sensors, provides data on underwater terrain
to support numerous survey tasks.
research and military applications. With superior bandwidth,
lower distortion and considerable durability, our products exceed
the demands of the market. We offer down-hole arrays, bottom
cable hydrophone arrays, high-speed towed arrays and data
acquisition systems.
Market
Market profile
Alliances:
- Financial
- Sales/Marketing
- Technology
Geographic markets:
Actively pursuing:
- United Kingdom
- United States


--- title: Changed Functions version: "5.5" tags: - "5.5" - changed-functions ---
null
as
an argument to reset the error handler.
null
, set_error_handler() and
set_exception_handler() now return the previous error
or exception handler, respectively.
depth
parameter.
timezone
argument, and
IntlCalendar objects for the
calendar
argument. Furthermore, if the time zone
is omitted and the calendar
doesn't specify a time
zone, PHP's default time zone from
date_default_timezone_get() is now used instead of the
default ICU time zone.


SRTPerformanceData
public struct SRTPerformanceData
The SRTPerformanceData represents the SRT’s performance statistics. This struct is wrapper for an CBytePerfMon.
-
The time since the UDT entity is started, in milliseconds.
Declaration
Swift
public let msTimeStamp: Int64
-
The total number of sent data packets, including retransmissions.
Declaration
Swift
public let pktSentTotal: Int64
-
The total number of received packets.
Declaration
Swift
public let pktRecvTotal: Int64
-
The total number of lost packets (sender side)
Declaration
Swift
public let pktSndLossTotal: Int32
-
The total number of lost packets (receiver side)
Declaration
Swift
public let pktRcvLossTotal: Int32
-
The total number of retransmitted packets
Declaration
Swift
public let pktRetransTotal: Int32
-
The total number of sent ACK packets
Declaration
Swift
public let pktSentACKTotal: Int32
-
The total number of received ACK packets
Declaration
Swift
public let pktRecvACKTotal: Int32
-
The total number of sent NAK packets
Declaration
Swift
public let pktSentNAKTotal: Int32
-
The total number of received NAK packets
Declaration
Swift
public let pktRecvNAKTotal: Int32
-
The total time duration when UDT is sending data (idle time exclusive)
Declaration
Swift
public let usSndDurationTotal: Int64
-
The number of too-late-to-send dropped packets
Declaration
Swift
public let pktSndDropTotal: Int32
-
The number of too-late-to play missing packets
Declaration
Swift
public let pktRcvDropTotal: Int32
-
The number of undecrypted packets
Declaration
Swift
public let pktRcvUndecryptTotal: Int32
-
The total number of sent data bytes, including retransmissions
Declaration
Swift
public let byteSentTotal: UInt64
-
The total number of received bytes
Declaration
Swift
public let byteRecvTotal: UInt64
-
The total number of lost bytes
Declaration
Swift
public let byteRcvLossTotal: UInt64
-
The total number of retransmitted bytes
Declaration
Swift
public let byteRetransTotal: UInt64
-
The number of too-late-to-send dropped bytes
Declaration
Swift
public let byteSndDropTotal: UInt64
-
The number of too-late-to play missing bytes (estimate based on average packet size)
Declaration
Swift
public let byteRcvDropTotal: UInt64
-
The number of undecrypted bytes
Declaration
Swift
public let byteRcvUndecryptTotal: UInt64
-
The number of sent data packets, including retransmissions
Declaration
Swift
public let pktSent: Int64
-
The number of received packets
Declaration
Swift
public let pktRecv: Int64
-
The number of lost packets (sender side)
Declaration
Swift
public let pktSndLoss: Int32
-
The number of lost packets (receiver side)
Declaration
Swift
public let pktRcvLoss: Int32
-
The number of retransmitted packets
Declaration
Swift
public let pktRetrans: Int32
-
The number of retransmitted packets received
Declaration
Swift
public let pktRcvRetrans: Int32
-
The number of sent ACK packets
Declaration
Swift
public let pktSentACK: Int32
-
The number of received ACK packets
Declaration
Swift
public let pktRecvACK: Int32
-
The number of sent NAK packets
Declaration
Swift
public let pktSentNAK: Int32
-
The number of received NAK packets
Declaration
Swift
public let pktRecvNAK: Int32
-
The sending rate in Mb/s
Declaration
Swift
public let mbpsSendRate: Double
-
The receiving rate in Mb/s
Declaration
Swift
public let mbpsRecvRate: Double
-
The busy sending time (i.e., idle time exclusive)
Declaration
Swift
public let usSndDuration: Int64
-
The size of order discrepancy in received sequences
Declaration
Swift
public let pktReorderDistance: Int32
-
The average time of packet delay for belated packets (packets with sequence past the ACK)
Declaration
Swift
public let pktRcvAvgBelatedTime: Double
-
The number of received AND IGNORED packets due to having come too late
Declaration
Swift
public let pktRcvBelated: Int64
-
The number of too-late-to-send dropped packets
Declaration
Swift
public let pktSndDrop: Int32
-
The number of too-late-to play missing packets
Declaration
Swift
public let pktRcvDrop: Int32
-
The number of undecrypted packets
Declaration
Swift
public let pktRcvUndecrypt: Int32
-
The number of sent data bytes, including retransmissions
Declaration
Swift
public let byteSent: UInt64
-
The number of received bytes
Declaration
Swift
public let byteRecv: UInt64
-
The number of retransmitted bytes
Declaration
Swift
public let byteRcvLoss: UInt64
-
The number of retransmitted bytes
Declaration
Swift
public let byteRetrans: UInt64
-
The number of too-late-to-send dropped bytes
Declaration
Swift
public let byteSndDrop: UInt64
-
The number of too-late-to play missing bytes (estimate based on average packet size)
Declaration
Swift
public let byteRcvDrop: UInt64
-
The number of undecrypted bytes
Declaration
Swift
public let byteRcvUndecrypt: UInt64
-
The packet sending period, in microseconds
Declaration
Swift
public let usPktSndPeriod: Double
-
The flow window size, in number of packets
Declaration
Swift
public let pktFlowWindow: Int32
-
The congestion window size, in number of packets
Declaration
Swift
public let pktCongestionWindow: Int32
-
The number of packets on flight
Declaration
Swift
public let pktFlightSize: Int32
-
The RTT, in milliseconds
Declaration
Swift
public let msRTT: Double
-
The estimated bandwidth, in Mb/s
Declaration
Swift
public let mbpsBandwidth: Double
-
The available UDT sender buffer size
Declaration
Swift
public let byteAvailSndBuf: Int32
-
The available UDT receiver buffer size
Declaration
Swift
public let byteAvailRcvBuf: Int32
-
The transmit Bandwidth ceiling (Mbps)
Declaration
Swift
public let mbpsMaxBW: Double
-
The MTU
Declaration
Swift
public let byteMSS: Int32
-
The UnACKed packets in UDT sender
Declaration
Swift
public let pktSndBuf: Int32
-
The UnACKed bytes in UDT sender
Declaration
Swift
public let byteSndBuf: Int32
-
The UnACKed timespan (msec) of UDT sender
Declaration
Swift
public let msSndBuf: Int32
-
Timestamp-based Packet Delivery Delay
Declaration
Swift
public let msSndTsbPdDelay: Int32
-
Undelivered packets in UDT receiver
Declaration
Swift
public let pktRcvBuf: Int32
-
The undelivered bytes of UDT receiver
Declaration
Swift
public let byteRcvBuf: Int32
-
The undelivered timespan (msec) of UDT receiver
Declaration
Swift
public let msRcvBuf: Int32
-
The Timestamp-based Packet Delivery Delay
Declaration
Swift
public let msRcvTsbPdDelay: Int32
-
The number of control packets supplied by packet filter
Declaration
Swift
public let pktSndFilterExtraTotal: Int32
-
The number of control packets received and not supplied back
Declaration
Swift
public let pktRcvFilterExtraTotal: Int32
-
The number of packets that the filter supplied extra (e.g. FEC rebuilt)
Declaration
Swift
public let pktRcvFilterSupplyTotal: Int32
-
The number of packet loss not coverable by filter
Declaration
Swift
public let pktRcvFilterLossTotal: Int32
-
The number of control packets supplied by packet filter
Declaration
Swift
public let pktSndFilterExtra: Int32
-
The number of control packets received and not supplied back
Declaration
Swift
public let pktRcvFilterExtra: Int32
-
The number of packets that the filter supplied extra (e.g. FEC rebuilt)
Declaration
Swift
public let pktRcvFilterSupply: Int32
-
The number of packet loss not coverable by filter
Declaration
Swift
public let pktRcvFilterLoss: Int32
-
The packet reorder tolerance value
Declaration
Swift
public let pktReorderTolerance: Int32


By the end of this module students will be able to:
Describe the scale of global greenhouse gas emissions.
Compare the quantity of emissions between individuals, corporations, and nation states.
Model the greenhouse effect with varying levels of greenhouse gases.
Create data visualizations of both quantitative and qualitative data related to greenhouse gas emissions.
Evaluate strategies for reducing emissions using quantitative tools.
Discuss the consequences of failing to cut greenhouse gas emissions.
Propose actions to mitigate emissions in your personal life, institution, and nation


Basic Page with JavaScript
Must be run by a browser so that JavaScript is executed and manipulates the page content.


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NoiseReduction
public class NoiseReduction: Filter
Noise Reduction
Seealso
CINoiseReduction-
A reference to the core image filter.
Declaration
Swift
public let filter: CIFilter
-
Creates a new CINoiseReduction filter
Declaration
Swift
public init(inputImage: CIImage, inputNoiseLevel: CGFloat = 0.02, inputSharpness: CGFloat = 0.4)
Parameters
inputImage
The image to use as an input image. For filters that also use a background image, this is the foreground image.
inputNoiseLevel
The amount of noise reduction. The larger the value, the more noise reduction.
inputSharpness
The sharpness of the final image. The larger the value, the sharper the result.


Before Change
// Results
img_sec_mean = np.mean(img_secs)
img_sec_conf = 1.96 * np.std(img_secs)
print("Img/sec per %s: %.1f +-%.1f" % (device, img_sec_mean, img_sec_conf))
print("Total img/sec on %d %s(s): %.1f +-%.1f" %
(num_workers, device, num_workers * img_sec_mean,
num_workers * img_sec_conf))
After Change
// Results
img_sec_mean = np.mean(img_secs)
img_sec_conf = 1.96 * np.std(img_secs)
print(f"Img/sec per {device}: {img_sec_mean:.1f} +-{img_sec_conf:.1f}")
print("Total img/sec on %d %s(s): %.1f +-%.1f" %
(num_workers, device, num_workers * img_sec_mean,
num_workers * img_sec_conf))


{% extends "base.html" %} {% import "_forms.html" as forms %} {% block content %}
Resend Email Confirmation
Resend SMS Confirmation


{% extends "GeneralApp/base.html" %} {% block title %}GeneralApp | Email sent{% endblock %} {% block navbar %}
We've emailed you instructions for resetting your password.
If an account exists with the email you entered, you should receive them shortly.
If you don't receive an email, please make sure you've entered the address you registered with, and check your spam folder.


Practical javascript course
Projects
In the following buttons you will find the projects made in JS by @Wiljo
Project Geometric Figures
Project Discounts
Project Discounts 2
Project Average-median-mode


const ( // CredType is the string that represents X509 credential type CredType = "X509" )
Client represents a client that will load/store an Idemix credential
type Client interface { NewX509Identity(name string, creds []credential.Credential) Identity GetCSP() bccsp.BCCSP }
Credential represents a X509 credential. Implements Credential interface
type Credential struct {
// contains filtered or unexported fields
}
func NewCredential(certFile, keyFile string, c Client) *Credential
NewCredential is constructor for X509 Credential
func (cred *Credential) CreateToken(req *http.Request, reqBody []byte) (string, error)
CreateToken creates token based on this X509 credential
func (cred *Credential) EnrollmentID() (string, error)
EnrollmentID returns enrollment ID of this X509 credential
func (cred *Credential) Load() error
Load loads the certificate and key from the location specified by certFile attribute using the BCCSP of the client. The private key is loaded from the location specified by the keyFile attribute, if the private key is not found in the keystore managed by BCCSP
func (cred *Credential) RevokeSelf() (*api.RevocationResponse, error)
RevokeSelf revokes this X509 credential
func (cred *Credential) SetVal(val interface{}) error
SetVal sets *Signer for this X509 credential
func (cred *Credential) Store() error
Store stores the certificate associated with this X509 credential to the location specified by certFile attribute
func (cred *Credential) Type() string
Type returns X509
func (cred *Credential) Val() (interface{}, error)
Val returns *Signer associated with this X509 credential
Identity represents an identity
type Identity interface { Revoke(req *api.RevocationRequest) (*api.RevocationResponse, error) }
Signer represents a signer Each identity may have multiple signers, currently one ecert and multiple tcerts
type Signer struct {
// contains filtered or unexported fields
}
func NewSigner(key bccsp.Key, cert []byte) (*Signer, error)
NewSigner is constructor for Signer
func (s *Signer) Attributes() (*attrmgr.Attributes, error)
Attributes returns the attributes that are in the certificate
func (s *Signer) Cert() []byte
Cert returns the cert bytes of this signer
func (s *Signer) GetName() string
GetName returns common name that is retrieved from the Subject of the certificate associated with this signer
func (s *Signer) GetX509Cert() *x509.Certificate
GetX509Cert returns the X509 certificate for this signer
func (s *Signer) Key() bccsp.Key
Key returns the key bytes of this signer


---
layout: post
title: watercolour from Sketchbooks detail
date: '2018-01-26T11:30:37+01:00'
categories:
- Sketchbooks
picture_url: https://film-Storyboards.tumblr.com/post/170156124196/watercolour-from-Sketchbooks-detail
---
Watercolour — From Sketchbooks (detail)


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Australian gets four months in jail over drunken Bali rampage
DENPASAR, Nov 12 — An Australian tourist who fly-kicked a motorcyclist and assaulted a man in his own home during a drunken rampage was jailed for four months today.
The ruling comes after Nicholas Carr’s antics were caught in a viral video that saw him carry out a campaign of destruction in Seminyak, a popular tourist area on the Indonesian holiday island.
“The defendant Nicholas Carr is found guilty and is sentenced to four months” in jail, presiding judge Soebandi, who goes by one name, told the Denpasar District Court.
A lawyer for Carr, charged with assault and property damage, said the 26-year-old would not appeal the ruling.
He is expected to be released next month because of time already served.
In August, Carr ran barefoot on to a street and shouted expletives before the apprentice builder slammed into the bonnet of a moving car and then fly-kicked an unsuspecting motorcycle rider.
The biker, who was thrown from the moving scooter, sustained minor injuries — later the pair embraced during a court hearing as Carr apologised to the victim.
Carr also shattered a convenience store’s glass door before stealing a motorcycle.
Later, he broke into a house where he assaulted the sleeping homeowner, leaving him with injuries, police said earlier.
He was eventually caught by locals and police and taken to hospital.
Pictures that circulated on social media showed at the time showed Carr bloodied and bruised, and trussed with hosepipe and rope.
Shortly after his arrest, Carr apologised and admitted drinking more than 10 small bottles of vodka as well as other alcohol.
After a string of embarrassing incidents by tourists, Bali officials recently warned that boorish visitors may be kicked off the island, which attracts millions annually to its palm-fringed beaches, colourful nightlife and ancient temples.
Australian professional rugby league player David Fifita returned home this week after he was briefly arrested in Bali for assaulting a nightclub security guard.
Several days after Carr’s arrest, a Czech couple who were slammed for disrespecting a Balinese temple took part in a ritual purification ceremony. — AFP


Land Use Map Clipper
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Street Food
We don't play with food
When I was a small boy, my father told me
never to recommend a church or woman
to anyone. And i have found it wise never to
recommend a restaurant either. Something
always goes wrong with the cheese souffle
Eat together
There is something profoundly satisfying
about sharing a meal. Eating together,
breaking bread together, is one of the
oldest and most fundamentally unifying of
human experiences
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There is something profoundly satisfying about sharing a meal. Eating together, breaking bread together, is one of the oldest and most fundamentally unifying of human experiences
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There is something profoundly satisfying
about sharing a meal. Eating together,
breaking bread together, is one of the
oldest and most fundamentally unifying of
human experiences