In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.
In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).
In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files
function from the scikit-learn library:
train_files
, valid_files
, test_files
- numpy arrays containing file paths to imagestrain_targets
, valid_targets
, test_targets
- numpy arrays containing onehot-encoded classification labels dog_names
- list of string-valued dog breed names for translating labelsfrom sklearn.datasets import load_files
from keras.utils import np_utils
import numpy as np
from glob import glob
# define function to load train, test, and validation datasets
def load_dataset(path):
data = load_files(path)
dog_files = np.array(data['filenames'])
dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
return dog_files, dog_targets
# load train, test, and validation datasets
train_files, train_targets = load_dataset('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')
test_files, test_targets = load_dataset('dogImages/test')
# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]
# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files
.
import random
random.seed(8675309)
# load filenames in shuffled human dataset
human_files = np.array(glob("lfw/*/*"))
random.shuffle(human_files)
# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades
directory.
In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray)
# print number of faces detected in the image
print('Number of faces detected:', len(faces))
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale
function executes the classifier stored in face_cascade
and takes the grayscale image as a parameter.
In the above code, faces
is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x
and y
) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w
and h
) specify the width and height of the box.
We can use this procedure to write a function that returns True
if a human face is detected in an image and False
otherwise. This function, aptly named face_detector
, takes a string-valued file path to an image as input and appears in the code block below.
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
return len(faces) > 0
Question 1: Use the code cell below to test the performance of the face_detector
function.
human_files
have a detected human face? dog_files
have a detected human face?  You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short
and dog_files_short
.
Answer:
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.
## TODO: Test the performance of the face_detector algorithm
## on the images in human_files_short and dog_files_short.
human_faces_count = 0
dog_faces_count = 0
for single_image in human_files_short:
if face_detector(single_image) > 0:
human_faces_count += 1
for single_image in dog_files_short:
if face_detector(single_image) > 0:
dog_faces_count += 1
human_accuracy = (human_faces_count/len(human_files_short))*100
dog_accuracy = (dog_faces_count/len(dog_files_short))*100
print("Fraction of Human Accuracy: ", human_accuracy, " %")
print("Fraction of Dog Accuracy: ", dog_accuracy, " %")
Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?
Answer:
We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on each of the datasets.
Answer:
I think there are other more better approaches which can be used to detect faces with a not clear face. Example: FaceNet by Google
## (Optional) TODO: Report the performance of another
## face detection algorithm on the LFW dataset
### Feel free to use as many code cells as needed.
In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.
from keras.applications.resnet50 import ResNet50
# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')
When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape
$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$where nb_samples
corresponds to the total number of images (or samples), and rows
, columns
, and channels
correspond to the number of rows, columns, and channels for each image, respectively.
The path_to_tensor
function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape
The paths_to_tensor
function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape
Here, nb_samples
is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples
as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!
from keras.preprocessing import image
from tqdm import tqdm
def path_to_tensor(img_path):
# loads RGB image as PIL.Image.Image type
img = image.load_img(img_path, target_size=(224, 224))
# convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
x = image.img_to_array(img)
# convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
return np.expand_dims(x, axis=0)
def paths_to_tensor(img_paths):
list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
return np.vstack(list_of_tensors)
Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input
. If you're curious, you can check the code for preprocess_input
here.
Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict
method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels
function below.
By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.
from keras.applications.resnet50 import preprocess_input, decode_predictions
def ResNet50_predict_labels(img_path):
# returns prediction vector for image located at img_path
img = preprocess_input(path_to_tensor(img_path))
return np.argmax(ResNet50_model.predict(img))
While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua'
to 'Mexican hairless'
. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels
function above returns a value between 151 and 268 (inclusive).
We use these ideas to complete the dog_detector
function below, which returns True
if a dog is detected in an image (and False
if not).
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
prediction = ResNet50_predict_labels(img_path)
return ((prediction <= 268) & (prediction >= 151))
Question 3: Use the code cell below to test the performance of your dog_detector
function.
human_files_short
have a detected dog? dog_files_short
have a detected dog?Answer:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
human_count = 0
for single_image in human_files_short:
if dog_detector(single_image):
human_count += 1
human_percentage = (human_count/len(human_files_short))*100
dog_count = 0
for single_image in dog_files_short:
if(dog_detector(single_image)):
dog_count += 1
dog_percentage = (dog_count/len(dog_files_short))*100
print("Percentage with Human: ", human_percentage, " %")
print("Percentage with Dog: ", dog_percentage, " %");
Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.
Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.
We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.
| Brittany | Welsh Springer Spaniel |
|---|---|
It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).
| Curly-Coated Retriever | American Water Spaniel |
|---|---|
Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.
| Yellow Labrador | Chocolate Labrador | Black Labrador |
|---|---|---|
We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.
Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!
We rescale the images by dividing every pixel in every image by 255.
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
model.summary()
We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:
Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.
Answer:
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
model = Sequential()
### TODO: Define your architecture.
### The Logic is to keep on Convoluting and then Pooling three times (depth, width height).
### Each time with increasing number of filters (2X Multiplier)
### Finally flatten and add Relu and Softmax Activations.
### Add Conv2D Layer with Relu Activation method with input shape as the shape of images.
model.add(Conv2D(filters=20, kernel_size=2, padding='same', activation='relu', input_shape=(224, 224, 3)))
### Add Pooling to help the model extract more complex features and understand that
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=40, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Conv2D(filters=80, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
### Add flatten layer to convolutional part of the CNN into a 1D feature vector.
model.add(Flatten())
### Add relu function is used in the dense layer.
model.add(Dense(500, activation='relu'))
### An output layer with softmax activation to classify the output into 133 categories
model.add(Dense(133, activation='softmax'))
model.summary()
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
from keras.callbacks import ModelCheckpoint
### TODO: specify the number of epochs that you would like to use to train the model.
epochs = 16
### Do NOT modify the code below this line.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5',
verbose=1, save_best_only=True)
model.fit(train_tensors, train_targets,
validation_data=(valid_tensors, valid_targets),
epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)
model.load_weights('saved_models/weights.best.from_scratch.hdf5')
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]
# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']
The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))
VGG16_model.summary()
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5',
verbose=1, save_best_only=True)
VGG16_model.fit(train_VGG16, train_targets,
validation_data=(valid_VGG16, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')
Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]
# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
from extract_bottleneck_features import *
def VGG16_predict_breed(img_path):
# extract bottleneck features
bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
# obtain predicted vector
predicted_vector = VGG16_model.predict(bottleneck_feature)
# return dog breed that is predicted by the model
return dog_names[np.argmax(predicted_vector)]
You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.
In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:
The files are encoded as such:
Dog{network}Data.npz
where {network}
, in the above filename, can be one of VGG19
, Resnet50
, InceptionV3
, or Xception
. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/
folder in the repository.
In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:
bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']
### TODO: Obtain bottleneck features from another pre-trained CNN.
### Using Resnet50
bottleneck_features = np.load('bottleneck_features/DogResnet50Data.npz')
train_Resnet50 = bottleneck_features['train']
valid_Resnet50 = bottleneck_features['valid']
test_Resnet50 = bottleneck_features['test']
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
<your model's name>.summary()
Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.
Answer:
Why previous Model didn't work: Starting, we tried developing everything from scratch (Not good test accuracy)... Later, I checked, with previous model, the accuracy on test data was ≈38%, however with using ResNet50, I was able to bump up the test accuracy to ≈76%.
I found that ResNet was performing better (considering the tradeoff between accuracy and time of computation).
Also, I researched and found Resnet is very much suitable to be used (Link to few references):
Another plus point was use of TransferLearning, which is helpful with ResNet.
Once that step was done, then I moved further and tried to implement similar model as above, starting with GlobalAveragePooling2D. However, I had the curiosity to add multiple layers to make it more accurate, but the issue with that was for overfitting and the train accuracy would increase, but test accuracy would decrease... So I added Dropout layers to handle overfitting.
With each pair of layer, increasing the units to make more refined classification. But then, preventing from overfitting too...
After adjusting the parameters, I found this sufficient enough and also fast enough to proceed with. My last layer had to be a Softmax Function to map to 133 classes.
Edit: After submitting last time, (as this is a re-submit), I found that better architecture would be Xception, however I would prefer to stick with my current architecture because it is also suitable and I have already worked on the layers and parameters however I will try to implement Xception on my later projects.
### TODO: Define your architecture.
model = Sequential()
### Add Global Average as first layer.
model.add(GlobalAveragePooling2D(input_shape=train_Resnet50.shape[1:]))
### Adding dense layer and then Dropout layer to prevent from overfitting.
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.4))
### Add another set of layers with changed parameters and lesser dropout percentage.
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))
### Lastly, Softmax activation to classify the output into one of 133 categories
model.add(Dense(133, activation='softmax'))
model.summary()
### TODO: Compile the model.
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
### TODO: Train the model.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.Resnet50.hdf5',
verbose=1, save_best_only=True)
model.fit(train_Resnet50, train_targets,
validation_data=(valid_Resnet50, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
### TODO: Load the model weights with the best validation loss.
model.load_weights('saved_models/weights.best.Resnet50.hdf5')
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.
### TODO: Calculate classification accuracy on the test dataset.
# get index of predicted dog breed for each image in test set
model_predictions = [np.argmax(model.predict(np.expand_dims(feature, axis=0))) for feature in test_Resnet50]
# report test accuracy
test_accuracy = 100*np.sum(np.array(model_predictions)==np.argmax(test_targets, axis=1))/len(model_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Write a function that takes an image path as input and returns the dog breed (Affenpinscher
, Afghan_hound
, etc) that is predicted by your model.
Similar to the analogous function in Step 5, your function should have three steps:
dog_names
array defined in Step 0 of this notebook to return the corresponding breed.The functions to extract the bottleneck features can be found in extract_bottleneck_features.py
, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function
extract_{network}
where {network}
, in the above filename, should be one of VGG19
, Resnet50
, InceptionV3
, or Xception
.
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
def dogBreed(img):
bottleneck_features = extract_Resnet50(path_to_tensor(img))
predicted_vector = model.predict(bottleneck_features)
return dog_names[np.argmax(predicted_vector)]
Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,
You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector
and dog_detector
functions developed above. You are required to use your CNN from Step 5 to predict dog breed.
Some sample output for our algorithm is provided below, but feel free to design your own user experience!
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
def processImg(img):
breed = dogBreed(img)
if dog_detector(img):
print("Hi Doggo, " + str(breed))
elif face_detector(img):
print("Hello Fellow Human, " + str(breed))
else:
print("I'm confused");
imgFile = cv2.imread(img)
cv_rgb = cv2.cvtColor(imgFile, cv2.COLOR_BGR2RGB)
plt.imshow(cv_rgb)
plt.show()
In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?
Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.
Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.
Answer:
I think my Algorithm can be improved. Possibly these three things can be improved:
Not sure, but maybe this can improve...
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
for singleImg in sorted(glob("algoTest/*")):
processImg(singleImg)


"Making Healthy Cookies Because We Can!"
Hungry for a Snack... try some Seattle Salmon Cookies!
Pat and his wife wanted to find a way to get their kids to eat healthier. They told their kids no dessert until they eat all their food, but it just didn't work.
While fishing for Chinook salmon one day, Pat was thinking about how much his kids liked cookies, and how much he liked salmon; and that's when Seattle Salmon cookies were born!
Pat and Penelope spent the next 8 months and their life's savings creating the perfect receipe. The best part is their kids can't get enough!


Home
Georgia :: Locate The Law
Constitution
Constitution of the State of Georgia
(Georgia Secretary of State)
Colonial Charters, Grants and Related Documents: Georgia
(Yale Avalon Project)
includes the 1732 Charter of Georgia and the 1777 Constitution of Georgia
Executive
Georgia Administrative Rules and Regulations
(Georgia Secretary of State)
Georgia Attorney General Opinions
1994-
Georgia Executive Orders
2003-
Georgia Governor
Georgia Attorney General
Georgia Elections Division
Georgia Office of Administrative Hearings
Georgia Secretary of State
State Agencies/Authorities/Commission/Councils
Judicial
Georgia Supreme Court - Opinion and Summaries
(Georgia Courts)
Georgia Court of Appeals
Georgia Court of Appeals Opinions
(FindLaw)
U.S. Court of Appeals, 11th Circuit Opinions
(U.S. Courts)
November 1994-
U.S. Court of Appeals, 11th Circuit Opinions
(FindLaw)
December 1994-
U.S. District Court, Middle District of Georgia
U.S. District Court, Northern District of Georgia
U.S. District Court, Southern District of Georgia
Judicial Qualifications Commission Opinions
Georgia Supreme Court
- includes opinion summaries and rules
Judicial Branch of Georgia
Legislative
Georgia Code
(Lexis Nexis)
Georgia Bills
1995-
Georgia General Assembly
- composed of the
Senate
and
House of Representatives


Farhan Nasim Dipto
Chief Technology Officer
Designing | Coding
"Hi, I'm Farhan, in charge of the technical know-hows and the CTO at PixelHow.in. I can communicate in codes fluently, flawlessly projecting your designs to the screens in pixel perfection. I'm well versed in WordPress Designing, and front end web development. So what are you waiting for? let's talk!"


Your top picks of vegetables.
Your top picks of fruits.
Your top picks of nuts.
Your top picks of flowers.
Capsicum chinense
Zingiber officinale
Dioscorea alata
Phaseolus vulgaris


My name is Ayana, friends call me Biggy and I'm an aspiring developer, designer and creator. Currently I'm attending a programming bootcamp in Portland taking the PHP/Drupal track while also learning Javascript, Jquery, Angular, Ember, React, CSS, and Sass. The most valuable skill I developed here is my ability to problem solve-- to adapt, learn and excel when in unknown territory.
Being the zealous and communicative young-sponge that I am, a workplace that mirrors or accents those qualities is exceedingly important to me.
I feel I will be a strong asset to the right team and am looking forward to being apart of the collaboration and development of an evolutionary indusrty.


DTGov Dashboard
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BrickHack 2015
20 schools - 320 hackers - 24 hours
Thanks for making BrickHack 2015 a huge success! You can view pictures taken at the event below.
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| Path: | README |
| Last Update: | Thu Sep 07 12:03:56 Romance Daylight Time 2006 |
= Localization Simplified (LocalizationSimplified)
Localization Simplified plugin for Ruby on Rails. Really simple localization. Written by Jesper Rønn-Jensen ( justaddwater.dk/ )
The goal is to have a dead simple plugin for one-language (non-english) Rails applications. Many of the existing localization / internationalization plugins are too big for this and hard to get started with. Just dump this plugin in /vendor/plugins/, set your language and off you go.
Unlike the more advanced plugins, you don’t have to translate anything in your view files. Just use the standard Rails commands you’re used to.
The simple approach also makes limits. Make sure you understand them to decide if this plugin is right for you.
I use this plugin when creating new projects. Then later in the development process I can decide to change to a more advanced localization plugin (if necessary)
This plugin modifies the following most used helpers for Rails
The reason why I have not reordered the datetime_select fields or currency is that it will introduce an unnecessary dependency here (because functions have to be recoded).
Currently supported languages:
in init.rb, set your language. That’s it. Now your db connection is running UTF-8 and standard Rails output is localized.
If your view files contains text, you probably also want to save your files as UTF-8.
1) Just copy this plugin into your /vendor/lib folder 2) Choose your lang-file in init.rb (default is Danish because I am Danish)
A special note of WARNING: All files here are saved using UTF-8 encoding. It’s not required for working, I guess, but other encodings could bring you in trouble.
"onsdag d. 23 august 2006 12:38:22" (Danish)
Feel free to use, translate, modify and improve this code. Do send me translations, improvements, etc. I cannot promise to use it, but chances are that I will unless it bloats the code here completely or makes code harder to maintain.
I added FIXME notes in the code to indicate where I also could use help.
This plugin uses a few bits and pieces from other Rails plugins GLoc (rubyforge.org/projects/gloc/) and swe_rails (opensource.ki.se/swe_rails.html)
Created 2006-07-28 by Jesper Rønn-Jensen justaddwater.dk/


Table of contents Close window
Each subsidiary has to produce two times a year the number of merchant contracts signed and the number of active merchant contracts on the 30 of June and the 31 of December, dispatched on acquiring channel.

For each channel :
Number of merchant contracts signed ,
Number of active merchant contracts.
********************


xxxxx一二三xxxxxxxccchhhh
xxxxxxx
By Alex Martelli
Preface content
Part Introduction...
If you httpparty, you must party hard
What's the frequency, Kenneth?
Chapter content
Appendix content
Bibliography content
Colophon content


Black to play 32
b0-w0/7
pips Black=147 White=123
(CorrectAnswer= 0/0, ErrorScore= 0)
1. Bar/23 24/21 eq: -0.371 42.89% (G:10.02% B:0.60%) 57.11% (G:24.88% B:2.09%) 2. Bar/20 eq: -0.506 (-0.136) 39.93% (G: 9.33% B:0.64%) 60.07% (G:26.15% B:2.36%) 3. Bar/23 13/10 eq: -0.517 (-0.146) 39.26% (G: 9.58% B:0.57%) 60.74% (G:25.70% B:2.15%) 4. Bar/23 15/12 eq: -0.542 (-0.171) 38.54% (G: 8.61% B:0.48%) 61.46% (G:24.31% B:2.00%) 5. Bar/23 6/3 eq: -0.623 (-0.252) 39.65% (G: 9.73% B:0.77%) 60.35% (G:31.31% B:4.12%)


{# Main landing page. #} {% extends 'base/base.html' %} {% load socialaccount %} {% load static %} {% block stylesheets %} {% endblock %} {% block content %} {% include "base/navbar.html" %} {% if is_staff %}
Hey {{ username }}, you have permission to create new challenges!
We will be hosting a code jam every 2 months, each consisting of a different theme and allowing yourself to put your skills to the test. In comparison to other code jams available, you can request for your own teammates and team sizes. Meaning, if you prefer to go solo, you're still welcome to participate.
For our code jams, you're in for a chance to win real cash prizes.
(CAD) $150, $100 and $50 will be split amongst the winning teams.
To sign up for the our weekly coding challenges, please go to #challenge. Read up on the challenge and if you would like to participate all you'll need to do is react to the message.
Challenges are posted every Monday and If you would like to get pinged for new challenges; react to this message
{{ team.submission.description }}
{{ team_member.username }}
Coming Soon....
#} {#{{ challenge.short_desc }}
#} {#{{ challenge.short_desc }}
#} {#{{ challenge.short_desc }}
#} {#{{ challenge.short_desc }}
#} {#


Home Page
Search Bar
Spotify Song Success
Enter a Song and Artist Name:
Artist Name:
Track Name:
Song Title, Artist
Artist Stats
Artist Name:
{{artist_name}}
Track Name:
{{track_name}}
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List of Countries
Successful Countries
{{score_1}}
Unsuccessful Countries
{{score_0}}


MONEY TRAIN A film review by Peter Wels Copyright 1995 Peter Wels
Starring Wesley Snipes, Woody Harrelson Directed by Joseph Ruben Rating: MA Score: * (out of ****)
Cashing in on the success of WHITE MEN CAN'T JUMP and the failure of most of their solo vehicles (including THE COWBOY WAY and DROP ZONE), Wesley Snipes and Woody Harrelson have reteamed, hoping that they can strike up the same success.
This time out, the duo play foster brothers who are transit cops. Following several run-ins with their tyrannical boss (Robert Blake), who is obsessed with the "Money Train"--a train which carries all the subway's takings--and will let nothing stop it (now *there's* an original plot device), Harrelson's character decides to try and take off with the cash.
Along the way, both of them fall in love with the same woman--another transit cop who also hates her boss. This leads to the typical sibling rivalry crap that you usually find in a film of this standard ('you were always the better brother' etc. etc. etc.), and doesn't help to add anything to the film.
Given a better script, better performers in supporting roles and a shorter running time (the film almost went for two hours--or did it just feel like it?), this film might (and I mean *might*) have turned out okay, but as it is, it is stocked with the worst action film cliches, whose only purpose appears to be to pad the film out to its painfully long running time.
 He sounds like Wayne Newton, and appears to think the best way to appear evil is to have your eyes bulging as though they are about to burst out of their sockets!
But this is only one of MONEY TRAIN's problems. If you look at the script as if it was an action film, it lacks originality and suspense. But if you try to look at it as if it were a heist film, it is even worse. The final robbery sequence is almost trivialised and the characters definitely ain't Butch and Sundance.
The film isn't all bad though. Snipes and Harrelson, when they aren't using the dumb cliches that the script gives them, make an agreeable duo, and some of the stunt-work is indeed excellent. But good stunt-work cannot save a bottom-of-the-barrel action movie.
If you're a really undemanding action freak, there's a chance you'll like this. But if you're looking for something original, or suspenseful, try somewhere else--I doubt you'll find it here.
-- For info, comments and feedback, contact me via email at<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


|
Rendu
An OpenGL rendering engine for experimentations
|
Represent an OS window and its associated rendering context. More...
#include <Window.hpp>
Public Types | |
| enum | Action : uint { Action::None, Action::Quit, Action::Fullscreen, Action::Vsync } |
Public Member Functions | |
| Window (const std::string &name, RenderingConfig &config, bool escapeQuit=true, bool hidden=false) | |
| void | perform (Action action) |
| bool | nextFrame () |
| void | clean () |
| Window (const Window &)=delete | |
| Window & | operator= (const Window &)=delete |
| Window (Window &&)=delete | |
| Window & | operator= (Window &&)=delete |
Private Member Functions | |
| void | setupImGui () |
Private Attributes | |
| RenderingConfig & | _config |
| The window configuration. | |
| GLFWwindow * | _window = nullptr |
| Internal window handle. | |
| bool | _frameStarted = false |
| Has a frame been started. | |
| bool | _allowEscape = false |
| Can the window be closed by pressing escape. | |
Represent an OS window and its associated rendering context.
|
strong |
System actions that can be executed by the window.
| Enumerator | |
|---|---|
| None | Do nothing. |
| Quit | Quit the application. |
| Fullscreen | Switch the window from/to fullscreen mode. |
| Vsync | Switch the v-sync on/off. |
| Window::Window | ( | const std::string & | name, |
| RenderingConfig & | config, | ||
| bool | escapeQuit = true , |
||
| bool | hidden = false |
||
| ) |
Create a new window backed by an OpenGL context.
| name | the name of the window |
| config | the configuration to use (additional info will be added to it) |
| escapeQuit | allows the user to close the window by pressing escape |
| hidden | should the window be hidden (for preprocess for instance) |
|
delete |
Copy constructor.
|
delete |
Move constructor.
| void Window::clean | ( | ) |
Clean resources, delete the window and its context.
| bool Window::nextFrame | ( | ) |
Start registering GUI items.
Copy assignment.
Move assignment.
| void Window::perform | ( | Action | action | ) |
Execute an action related to the windowing system.
| action | the system action to perform |
|
private |
Setup ImGui with the proper callbacks and style.


#include <ThreadBase.h>
Definition at line 66 of file ThreadBase.h.
| CThreadBase::CThreadBase |
( |
| ) |
|
|
inline |
| virtual CThreadBase::~CThreadBase |
( |
| ) |
|
|
inlinevirtual |
| virtual void CThreadBase::_Start |
( |
| ) |
|
|
protectedpure virtual |
| virtual void CThreadBase::_Stop |
( |
| ) |
|
|
protectedpure virtual |
| int CThreadBase::_ThreadMethod |
( |
| ) |
|
|
inline |
| bool CThreadBase::GetExit |
( |
| ) |
|
|
inline |
| bool CThreadBase::IsRunning |
( |
| ) |
|
|
inline |
| void CThreadBase::Start |
( |
void * |
pParameter, |
|
|
ThreadMethodType |
pThreadMethod, |
|
|
int |
nKillThreadTimeout = 5000 |
|
) |
| |
|
inline |
| void CThreadBase::Start |
( |
int |
nKillThreadTimeout = 5000 | ) |
|
|
inline |
| void CThreadBase::Stop |
( |
| ) |
|
|
inline |
| virtual void CThreadBase::StopThreadCallback |
( |
| ) |
|
|
inlineprotectedvirtual |
| virtual int CThreadBase::ThreadMethod |
( |
| ) |
|
|
inlineprotectedvirtual |
| bool CThreadBase::m_bCompletelyDone |
| int CThreadBase::m_nKillThreadTimeout |
|
protected |
The documentation for this class was generated from the following file:


Overview
Retrieves the token portion of the OAuth 2.0 specification.
Request Arguments
| Name |
Type |
Description |
Required |
|
grant_type
|
String
|
Type of request. Available grant types are "password" and "refresh_token".
|
True
|
|
client_id
|
String
|
Used to identify the client. A client_id of "sugar" will automatically create an OAuth Key in the system that is used for "password" authentication. A client_id of "support_portal" will create an OAuth Key that will allow for portal authentication. Additional client_id's can be created by the administrator in Admin > OAuth Keys to allow for additional grant types. If the client secret is populated, it will be validated against the client id.
|
True
|
|
client_secret;
|
String
|
The clients secret key.
|
True
|
|
username
|
String
|
The username of the user authenticating to the system.
|
True
|
|
password
|
String
|
The plaintext password the user authenticating to the system.
|
True
|
|
platform
|
String
|
The platform type. Available types are "base", "mobile", and "portal".
|
True
|
Request for Password Grant Types
{
"grant_type":"password",
"client_id":"sugar",
"client_secret":"",
"username":"admin",
"password":"password",
"platform":"base"
}
Request for Refresh Token Grant Types
{
"grant_type":"refresh_token",
"refresh_token":"c1be5132-655b-1ca3-fb44-512e36709871",
"client_id":"sugar",
"client_secret":"",
}
Response Arguments
| Name |
Type |
Description |
|
access_token
|
String
|
The access token needed to authenticate for other methods.
|
|
expires_in
|
Integer
|
The length of time until access_token expires in seconds.
|
|
token_type
|
String
|
The token type. Currently only "bearer" is supported.
|
|
null|
|
|
The Oauth scope. Normally returned as null.
|
|
refresh_token
|
String
|
The token needed to extend the access_token expiration timeout.
|
|
refresh_expires_in
|
Integer
|
The length of time until refresh_token expires in seconds.
|
|
download_token
|
String
|
The token used to download images and files.
|
Response
{
"access_token":"802b64c0-5eac-8431-a541-5342d38ac527",
"expires_in":3600,
"token_type":"bearer",
"scope":null,
"refresh_token":"85053198-24b1-4521-b1a1-5342d382e0b7",
"refresh_expires_in":1209600,
"download_token":"8c9b5461-0d95-8d87-6084-5342d357b39e"
}
Change Log
| Version |
Change |
|
v10
|
Added /oauth2/token POST endpoint.
|


Let us know where you are in the process so we can help! Simply fill out the form below or visit
http://confirm.xula.edu
to update your status.
Woohoo—I am depositing soon. I will mail my deposit by:
My deposit is already in the mail!
I am still considering Xavier University of Louisiana.
But, before I send my deposit, can you help me with (select all that apply):
Information about my area of study
Paying for my investment in college
Campus life and activities
Scheduling a campus visit
Connecting with a coach
I have decided not to attend Xavier University of Louisiana. I plan to attend:
Submit


|
SideCar
|
#include <sys/types.h>
#include <netinet/in.h>
#include <arpa/inet.h>
#include <sstream>
#include "boost/shared_ptr.hpp"
#include "Logger/Formatters.h"
#include "Logger/Priority.h"
Go to the source code of this file.
Data Structures | |
| class | Logger::Writers::Writer |
| Abstract class that defines the interface for all objects that write log messages. More... | |
| class | Logger::Writers::File |
| class | Logger::Writers::RollingFile |
| class | Logger::Writers::Stream |
| class | Logger::Writers::Syslog |
| class | Logger::Writers::RemoteSyslog |
Namespaces | |
| Logger | |
| Namespace for the Log logging device and support classes. | |
| Logger::Writers | |
| Namespace for Log device writers. | |


Class SqlXmlReader
Provides a disposable wrapper for SQL XML data reader, which synchronizes the SQL connection disposal with its own life cycle.
Inheritance
Implements
Inherited Members
Namespace: MilestoneTG.TransientFaultHandling
Assembly: MilestoneTG.TransientFaultHandling.Data.dll
Syntax
public class SqlXmlReader : XmlReader, IDisposable
Constructors
SqlXmlReader(IDbConnection, XmlReader)
Initializes a new instance of the SqlXmlReader class that is associated with the specified SQL connection and the original XML reader.
Declaration
public SqlXmlReader(IDbConnection connection, XmlReader innerReader)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Data.IDbConnection | connection | The SQL connection that provides access to the XML data for this reader. |
| System.Xml.XmlReader | innerReader | The original XML reader that is to be wrapped by this instance. |
Properties
AttributeCount
Returns the number of attributes on the current node.
Declaration
public override int AttributeCount { get; }
Property Value
| Type | Description |
|---|---|
| System.Int32 |
Overrides
BaseURI
Returns the base Uniform Resource Identifier (URI) of the current node.
Declaration
public override string BaseURI { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
Overrides
Depth
Returns the depth of the current node in the XML document.
Declaration
public override int Depth { get; }
Property Value
| Type | Description |
|---|---|
| System.Int32 |
Overrides
EOF
Returns a value that indicates whether the reader is positioned at the end of the stream.
Declaration
public override bool EOF { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
Overrides
HasValue
Returns a value that indicates whether the current node can have a System.Xml.XmlReader.Value.
Declaration
public override bool HasValue { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
Overrides
IsEmptyElement
Returns a value that indicates whether the current node is an empty element.
Declaration
public override bool IsEmptyElement { get; }
Property Value
| Type | Description |
|---|---|
| System.Boolean |
Overrides
LocalName
Returns the local name of the current node.
Declaration
public override string LocalName { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
Overrides
NamespaceURI
Returns the namespace URI (as defined in the W3C Namespace specification) of the node on which the reader is positioned.
Declaration
public override string NamespaceURI { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
Overrides
NameTable
Returns the System.Xml.XmlNameTable that is associated with this implementation.
Declaration
public override XmlNameTable NameTable { get; }
Property Value
| Type | Description |
|---|---|
| System.Xml.XmlNameTable |
Overrides
NodeType
Returns the type of the current node.
Declaration
public override XmlNodeType NodeType { get; }
Property Value
| Type | Description |
|---|---|
| System.Xml.XmlNodeType |
Overrides
Prefix
Returns the namespace prefix that is associated with the current node.
Declaration
public override string Prefix { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
Overrides
ReadState
Returns the state of the reader.
Declaration
public override ReadState ReadState { get; }
Property Value
| Type | Description |
|---|---|
| System.Xml.ReadState |
Overrides
Value
Returns the text value of the current node.
Declaration
public override string Value { get; }
Property Value
| Type | Description |
|---|---|
| System.String |
Overrides
Methods
Close()
Closes both the original System.Xml.XmlReader and the associated SQL connection.
Declaration
public override void Close()
Overrides
GetAttribute(Int32)
Returns the value of the attribute that has the specified index.
Declaration
public override string GetAttribute(int i)
Parameters
| Type | Name | Description |
|---|---|---|
| System.Int32 | i | The index of the attribute. The index is zero-based. (The first attribute has an index of 0.) |
Returns
| Type | Description |
|---|---|
| System.String | The value of the specified attribute. This method does not move the reader. |
Overrides
GetAttribute(String)
Returns the value of the attribute that has the specified name.
Declaration
public override string GetAttribute(string name)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | name | The qualified name of the attribute. |
Returns
| Type | Description |
|---|---|
| System.String | The value of the specified attribute, or null if the attribute isn't found or its value is System.String.Empty. |
Overrides
GetAttribute(String, String)
Returns the value of the attribute that has the specified name and namespace URI.
Declaration
public override string GetAttribute(string name, string namespaceUri)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | name | The local name of the attribute. |
| System.String | namespaceUri | The namespace URI of the attribute. |
Returns
| Type | Description |
|---|---|
| System.String | The value of the specified attribute, or null if the attribute isn't found or its value is System.String.Empty. This method does not move the reader. |
Overrides
LookupNamespace(String)
Resolves a namespace prefix in the current element's scope.
Declaration
public override string LookupNamespace(string prefix)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | prefix | The prefix whose namespace URI you want to resolve. To match the default namespace, pass an empty string. |
Returns
| Type | Description |
|---|---|
| System.String | The namespace URI to which the prefix maps, or null if no matching prefix is found. |
Overrides
MoveToAttribute(String)
Moves to the attribute that has the specified name.
Declaration
public override bool MoveToAttribute(string name)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | name | The qualified name of the attribute. |
Returns
| Type | Description |
|---|---|
| System.Boolean | true if the attribute is found; otherwise, false. If false, the reader's position does not change. |
Overrides
MoveToAttribute(String, String)
Moves to the attribute that has the specified name and namespace URI.
Declaration
public override bool MoveToAttribute(string name, string ns)
Parameters
| Type | Name | Description |
|---|---|---|
| System.String | name | The local name of the attribute. |
| System.String | ns | The namespace URI of the attribute. |
Returns
| Type | Description |
|---|---|
| System.Boolean | true if the attribute is found; otherwise, false. If false, the reader's position does not change. |
Overrides
MoveToElement()
Moves to the element that contains the current attribute node.
Declaration
public override bool MoveToElement()
Returns
| Type | Description |
|---|---|
| System.Boolean | true if the reader is positioned on an attribute (in which case, the reader moves to the element that owns the attribute); false if the reader is not positioned on an attribute (in which case, the position of the reader does not change). |
Overrides
MoveToFirstAttribute()
Moves to the first attribute.
Declaration
public override bool MoveToFirstAttribute()
Returns
| Type | Description |
|---|---|
| System.Boolean | true if an attribute exists (in which case, the reader moves to the first attribute); otherwise, false (in which case, the position of the reader does not change). |
Overrides
MoveToNextAttribute()
Moves to the next attribute.
Declaration
public override bool MoveToNextAttribute()
Returns
| Type | Description |
|---|---|
| System.Boolean | true if there is a next attribute; false if there are no more attributes. |
Overrides
Read()
Reads the next node from the stream.
Declaration
public override bool Read()
Returns
| Type | Description |
|---|---|
| System.Boolean | true if the next node was read successfully; false if there are no more nodes to read. |
Overrides
ReadAttributeValue()
Parses the attribute value into one or more Text, EntityReference, or EndEntity nodes.
Declaration
public override bool ReadAttributeValue()
Returns
| Type | Description |
|---|---|
| System.Boolean | true if there are nodes to return, false if the reader is not positioned on an attribute node when the initial call is made or if all the attribute values have been read. An empty attribute such as misc="" returns true with a single node that has a value of System.String.Empty. |
Overrides
ResolveEntity()
Resolves the entity reference for EntityReference nodes.
Declaration
public override void ResolveEntity()


Client ID*
Client Secret*
Protocol*
https
http
Host (only hostname)
Scope*
Email*
Password*
Exec authenticate()?
Show User Data


Latest Release
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Next: Odd Number of Keyword Arguments, Previous: Unrecognized Keyword Arguments, Up: Argument Mismatch Detection
It is not permitted to supply a keyword argument to a function using a name that is not a symbol.
If this situation occurs in a safe call,
an error of type program-error must be signaled unless keyword argument checking is suppressed as described in Suppressing Keyword Argument Checking; and in an unsafe call the situation has undefined consequences.


Honzi
/
Personal Bests
/
Hitman
Absolution
PB
Achievements
11/47 (23.4042553%)
Disguises
20%
Techniques
13%
Weapons
39%
2 Demo (Hard)
PB
Any/All Cheats
0:45
Fire Axe Only
1:54
Infinite Ammo
1:17
Invisible
1:11
Silent Assassin
No Cheats
14:01


Work hard to be a real Data Scienctist
Bring to the table win-win survival strategies to ensure proactive domination. At the end of the day, going forward, a new normal that has evolved from generation X is on the runway heading towards a streamlined cloud solution. User generated content in real-time will have multiple touchpoints for offshoring.
Educated three months in SK careers center and worked two months at the connected cooperation.
GPA: 3.97
Apart from being a web developer, I enjoy most of my time being outdoors. In the winter, I am an avid skiier and novice ice climber. During the warmer months here in Colorado, I enjoy mountain biking, free climbing, and kayaking.
When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows, I am an aspiring chef, and I spend a large amount of my free time exploring the latest technolgy advancements in the front-end web development world.


We provide an easy solution to boaters worldwide. RentABoat.com is a boat rental online marketplace connecting boat owners to boat renters. Potential renters can search boats listed by private boat owners, boat rental facilities and charter companies from around the world. They can view boat details, availability, rates, past renter experiences, and star ratings. As a boat owner, you're in complete control. You set the rate, your availability and your requirements. Thousands of boaters have listed and rented their boats successfully on RentABoat.com. List your boat for free or rent a boat though our network and you too will see why we are so successful!
RentABoat.com is a sub-corporation of Hilderbrandt Industries. For over 50 years the Hilderbrandt family has owned and operated marine related businesses. RentABoat.com's main operations are handled from their marina, Beaver Dam Boat Basin in Brookhaven, New York. Beaver Dam Boat Basin is a company that specializes in boat rentals, jet ski rentals, boat services, storage, and slip leasing.
President and CEO of RentABoat.com, Kenny Hilderbrandt, was born and raised in New York on Long Island. Surrounded by water he became a passionate boater and thought, “Why not rent boats out of Beaver Dam Boat Basin?” In 2004, Kenny started the boat rental division of his marina using www.boatrenting.com as his domain name. After receiving phones calls from individuals around the world looking to rent a boat, he realized there was a big market for him to connect boat renters to quality boat owners.
While brainstorming how to create a boat rental site that would connect boat owners to boat renters, Kenny faced a few obstacles. Back in 2006 the term peer-to-peer was just starting to become known. Kenny initially targeted boat renting facilities to list their rental boats on his site. He built a system that would rate both boat owner and boat renter by their rental experience. This allowed boat owners to become comfortable with the idea of renting their boat to potential renters. Another obstacle Kenny faced was making boatrenting.com cost effective for everyone involved. He designed a system that did not cost boat owners any up-front financial investment. Kenny was opposed to charging advertising fees and believed in simply collecting a commission on each boat rented from RentABoat.com.
He developed a boat rental clearing house and marketplace. Kenny wanted boat owners to have an online presence that they could have full control of. His system allows boat owners to upload photos, describe their boats, set rates and control their own reservation calendar while reaching millions of potential boat renters from around the world
The system allows for online reservations and sends an email to the boat owner when a reservation is made. Boat owners decide who rents their boat and when, and they can decline any reservation as they please.
Boat owners set the price, availability, deposits, fees and can temporarily deactivate their boats for service, seasons, or holidays. Boat owners can add users, add new boats and edit their profile to attract potential renters. Kenny also created a number of online reporting tools which allow boat owners to print rental reports, rental agreements and a renter’s boat rental history report.
Since 2004, we developed a new site with improved features called RentABoat.com. Since BoatRenting.com forwards to RentABoat.com, we will still happily answer to both names. Not only do we own the number 1 boat rental domain name, BoatRenting.com but we also own RentABoat.com, JetSkiRentals.com, JetSkiRenting.com, and YachtRenting.com. Why does this help your boat rental services? Having boat rentals, and rent a boat in our domain name impacts our website's success in nearly every area, including search engine optimizations (SEO), such as Google and social media marketing (SMM). RentAboat.com is a win-win for both boat renters and boat owners.
RentABoat.com is an online boat rental service dedicated to connecting boat owners/boat renting facility with individuals looking to rent a boat. As all boat owners know, boating is expensive. RentABoat.com offers all types of boaters the opportunity to earn extra income and make their watercrafts profitable. Chartering your boat will not only allow you to support these costs but will allow others to share your passion for boating.
We have over 750 boats in over 300 different locations, reaching most of the major US boating markets. With locations in Bahamas, Mexico, Croatia, Greece and beyond, RentABoat.com has grown to offer boat renting services all over the world. Want to try it out? Sign into our demo listing.You can try all of our features by using our DEMO facility. To sign in, use DEMO as a username and DEMO as the password. If you have a website, we can add a link that directs potential renters to your website. Are you interested in using our Display Ad method? Sign in using DemoAd as a username and DemoAd as the password to view a demo display ad listing.
Renting a boat is easy with our services. Within seconds, renters can view rates, availability, current photos and reserve a boat. Are you interested in renting a boat? Find your dream boat in just a few clicks. You don’t have to be a member to search for boats.


© Centre for Applied AI @ MQ
Weekly Presentations Schedule
| Date | Presenter | Topic |
|---|---|---|
| Friday, 10-03-2023 | Anthony Shaw | Data and AI at a Planetary Scale |
| Friday, 03-03-2023 | Habiba H. | Responsible AI: Ensuring Ethical and Safe AI Systems |
| Friday, 24-02-2023 | Greg Baker | Some Methodologies for Getting the Best Outputs from ChatGPT |
| Friday, 17-02-2023 | Nasrin Shabani | Basics of Graph and Graph mining (including demo) |
| Friday, 03-02-2023 | Matineh Pooshideh | Basics of video processing (including demo) |
| Friday, 27-01-2023 | Helia Farhood | Basics of image processing (including demo) |
| Friday, 20-01-2023 | Amin Beheshti | ChatGPT: Towards Transforming the Way Businesses Operate |


Intro / Solo:
| Am | C | D |
| Am | C | D |
1. Verse:
I hurt myself today, to see if I still feel
I focus on the pain, the only thing thats real
The needle tears a hole, the old familiar sting
| C | D | G | |
| Try to kill it all away, but I rem | ember | every | thing |
Chorus:
| Am | F | C | G |
| What Have I be | come, | my sweetest | friend |
| Am | F | C | G |
| Everyone I | know, goes a | way in the | end |
| Am | F | C | G | |
| And | you could have it | all, | my empire of | dirt |
| Am | F | G | Am |
| I will let you | down, | I will make you | hurt |
2. Verse:
I wear this crown of thorns, upon my liars chair
Beneath the stains of time, the feeling disappears
| C | D | G | |
| You are someone else, | I am | still right | here |
Chorus:
| Am | F | C | G |
| What Have I be | come, | my sweetest | friend |
| Am | F | C | G |
| Everyone I | know, goes a | way in the | end |
| Am | F | C | G | |
| And | you could have it | all, | my empire of | dirt |
| Am | F | C | G |
| I will let you | down, | I will make you | hurt |
| Am | F | C | G | |
| If | I could start ag | ain, | a million miles aw | ay |
| Am | F | G | Am |
| I would keep myself, | I would | find a way | . |
Outro:
| Am | C | D |
| Am | C | D |


John Doe
It Can Make You Feel
Happier
Exercise has been shown to improve your mood and decrease feelings of depression, anxiety and stress
It produces changes in the parts of the brain that regulate stress and anxiety.
Jane Doe
It Can Help With
Weight Loss
Some studies have shown that inactivity is a major factor in weight gain and obesity.
To understand the effect of exercise on weight reduction, it is important to understand the relationship between exercise and energy expenditure.
John Doe
It Is Good for Your
Muscles and Bones
As people age, they tend to lose muscle mass and function, which can lead to injuries and disabilities. Practicing regular physical activity is essential to reducing muscle loss and maintaining strength as you age.


| 059:009 Khan | : |
And those who, before them, had homes (in Al- Madinah) and had adopted the Faith, love those who emigrate to them, and have no jealousy in their breasts for that which they have been given (from the booty of Bani An-Nadir), and give them (emigrants) preference over themselves, even though they were in need of that. And whosoever is saved from his own covetousness, such are they who will be the successful. |
| 059:009 Maulana | : |
And those who made their abode in the City and in faith before them love those who have fled to them, and find in their hearts no need of what they are given, and prefer (them) before themselves, though poverty may afflict them. And whoever is saved from the niggardliness of his soul, these it is that are the successful. |
| 059:009 Pickthal | : |
Those who entered the city and the faith before them love those who flee unto them for refuge, and find in their breasts no need for that which hath been given them, but prefer (the fugitives) above themselves though poverty become their lot. And whoso is saved from his own avarice - such are they who are successful. |
| 059:009 Rashad | : |
As for those who provided them with a home and a refuge, and were believers before them, they love those who immigrated to them, and find no hesitation in their hearts in helping them. In fact, they readily give them priority over themselves, even when they themselves need what they give away. Indeed, those who overcome their natural stinginess are the successful ones. |
| 059:009 Sarwar | : |
Those who established a community center and embraced the faith before the arrival of the immigrants love those who have come to their town. They are not jealous of what is given to the immigrants. They give preference to them over themselves - even concerning the things that they themselves urgently need. Whoever controls his greed will have everlasting happiness. |
| 059:009 Shakir | : |
And those who made their abode in the city and in the faith before them love those who have fled to them, and do not find in their hearts a need of what they are given, and prefer (them) before themselves though poverty may afflict them, and whoever is preserved from the niggardliness of his soul, these it is that are the successful ones. |
| 059:009 Sherali | : |
And for those who had established their home in this City and had accepted the Faith before them. They love those who come to them for refuge, and find not in their breasts any desire for that which is given them (the Refugees), but give preference to the Refugees above themselves, even though poverty be their own lot. Whoso is rid of the covetousness of his own soul - it is these who will be successful; |
| 059:009 Yusufali | : |
But those who before them, had homes (in Medina) and had adopted the Faith,- show their affection to such as came to them for refuge, and entertain no desire in their hearts for things given to the (latter), but give them preference over themselves, even though poverty was their (own lot). And those saved from the covetousness of their own souls,- they are the ones that achieve prosperity. |
Concepts/Themes Covered: أركان الإسلام > (محمد (صلى الله عليه وسلم > هجرته ومنزلة المهاجرين Pillars of Islam> The Blessed Muhammad(PBUH)> His Immigration and the Immigrants Rank الجهاد > نتائج الحرب > الغنائم والأنفال Jihad> The War Results> The Spoils and the Boots الجهاد > الهجرة > ثواب المهاجرين Jihad> The Immigration> The Immigrants' Reward الجهاد > الهجرة > هجرة الأنصار Jihad> The Immigration> Al-Ansar Immigration العمل > العمل الطالح > الفلاح والسعادة Action(Work)> Bad Deeds>Success and Happiness الإنسان والعلاقات الأخلاقية > الأخلاق الحميدة > الإيثار Man and The Moral Relations> Good Morals>Unselfishness الإنسان والعلاقات الأخلاقية > الأخلاق الذميمة > البخل Man and The Moral Relations>The Dispraised Morals>Stinginess
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


Circuit Design
Your laboratory partner made a mess while designing the circuit boards for your experiment! Help them fix the issue by making sure that no connection crosses any other.
Solve this problem by clicking here.
Google Foobar: a Post Mortem
So, what was Google Foobar about? I still don't know. I will describe the problems I encountered, the techniques I used and what I learned. I will also discuss a few conjectures I have about it.
Turing's sunflower
In a letter to the zoologist J.Z. Young, British mathematician Alan Turing announced that his theory of embriology could explain, among other things, the connection between leaf arrangements and Fibonacci numbers.
Havel-Hakimi
A sequence of integers \(d_1,\dots,d_n\) is called graphical if there exists a graph \(G\) with it as its degree sequence. A theorem by Erdős and Gallai characterizes which sequences are graphical, but gives no algorithm to explicitly construct such a graph. Can you construct it?
Who killed the Duke of Densmore?
One day Sherlock Holmes received the visit of his friend Watson, who was asked to investigate on a murder that had gone unsolved for more than ten years.
Four-coloring a Dodecahedron
By the Four color theorem, the faces of a dodecahedron are four-colorable; that is, one can pick a color among four for each face in such a way that no two adjacent faces share the same color. But can you find such a coloration?


Projects
About
Contacts
Welcome to my Personal Portfolio Webpage
My Project One
My Project Two
My Project Three
My Project Four
My Github
My Facebook
My Twitter


I have looked into possible improvements to our CI/CD process and release workflow. I borrowed ideas from Software Configuration Management Patterns by Stephen Berczuk. In this post, I document suggestions and action items for improved CI/CD process.
At Pearson, my team's (CEET) current test suit takes over 2 hours to run. Since it is taking long time, we have a risk of not using the full test suite frequent. It is important to define most critical functionality and implement smoke tests. Smoke tests are not exhaustive tests, it guarantees that most critical functionality is working after development. In addition, during daily testing we can execute our full test suite.
Pre-checkin test suite
Full test suite
We use 'development' branch for active development. Therefore it make sense for us to integrate automated tests configured on that branch. I'm envisioning following workflow the 'development' branch.
When a developer done with a task, he/she go through pre-checkin workflow and submit a pull request. Then reviewer, review and merge the pull request for the 'development' branch. Then jenkins (or other tool) kicks off automated tests on the development branch. In addition, everyday around 7am (subject to change) jenkins kicks off daily testing process. We go through our full test suite during daily testing cycle. Furthermore, jenkins notifies developers on failure in automated testing or daily testing process.
We need to maintain active development during busy BTS (Back to school) season. Therefore we do our minor releases on a different release branch as shown in following diagram. We keep development on 'development' branch until we are ready for the release. When we are ready for the release, we create a release branch. Anything related to release happens on the this branch where as we can keep active development on the 'development' branch. On a later date, if we decide changes we did on the release branch is important for the next release, we merge them back to the ‘development’ branch.
We use separate branch for each minor release, trivial releases on minor release branch.
After running all of our testing at staging, will merge release branch with master so that master is always production ready.
As developers, it is our responsibility to make sure all our pull requests are conflicts free with the 'development' branch and pass all tests.


This page resets the simple MyJSON.com storage for this cookbook example to a single geojson feature to fix the inevitable Shlemiel problem with this simple design. This approach keeps this example as simple and understandable as possible.


I was working on one of the challenges during Foundations. It was an easy one and I was fairly certain with my code. I kept reading the challenge, revised my code 20 times but could not for the life of me, figure out what's wrong. I got frustrated and I got up, went for a walk, had something to eat and agot back to the challenge a couple of hours later. I did my process from beginning to end, still the same result. I resorted to looking at other people's code. And the second I opened up someone's repository, I realised what was wrong with my code. I was missing one word. ONE WORD. And when I fixed it, voila! My code works. It wasn't my solution that was wrong, it was just a typo. It feels very frustrating having to go through all that just because of a tiny mistake. But now, I will never forget that one word whenever I nees to use that code.
I wouldn't call it an elegant way but it is something that I am happy with. I was working on one of the challenges during Foundations and I got stuck with the solution. I went over it again and again. I was on the verge of giving up and was just about to ask my fellow cohort members on Slack. I was typing my question and explaining what I'm trying to do when it clicked. After I typed my explaination, I got the solution. I used the rubber duck method unexpectedly! Rubber ducky method is when you use a rubber ducky (or any inanimate object), and explain to it what you're trying to do. You usually understand more when you explain it yourself to someone else, or in this case a rubber ducky.
 Some of them are:
I find that I tend to use a few of these more than the others. I think my most use methos is reading error messages. I find that it is the easiest and fastest way to get to the problem. But this depends on how big the problem is. Next is Googling. With every question I have, I always run to Google. 90% of the time I get what I'm looking for through forums and articles. When I get stuck, I always tend to do the rubber ducky method. Most of the time, by the time I repeat what my problem is, I realise what the solution is. I admit I try to really hold off asking for help from peers and coaches, not because I do not want to ask for help but because I want to exhaust all my resources first before asking for help. I want to be able to solve the problem myself. I am still struggling with pseudocode. It doesn't really come naturally to me but I will definitely be working on it. Overall, problem solving still feels overwhelming to me at times but so far it has been bearable.


DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Press release
The first round of successful bids to the government’s new £2 million scholarship scheme for teachers were announced today by ministers.
The National Scholarship Fund for teachers, announced earlier in the year, was set up to help existing teachers in England to develop their skills and help them deepen their subject knowledge.
The Coalition Government firmly believes that the quality of teachers and their professional development are enormously important. That is why, as set out in the Schools White Paper, the Government is committed to developing a strong culture of professional development where more teachers acquire postgraduate qualifications and are supported to progress further academically.
Almost 2,000 teachers applied for the scheme, offering scholarships worth up to £3,500 each. The Department for Education today has confirmed the successful applications:
Schools Minister Nick Gibb said:
These scholarships have been awarded to teachers who have the potential to make a significant contribution to the country’s intellectual heritage through the acquisition of deeper subject knowledge.
Nothing is more important for raising standards in our schools than ensuring that we have more great teachers. These scholarships, alongside our other reforms to improve teacher recruitment and training, will elevate the status of the teaching profession.
The scheme is open to all qualified teachers. The maximum scholarship value is £3,500 but the value of each award varies depending on the type of activity funded. Around £2 million has been allocated for 2011/12 and we expect similar funding in the future.
Applications in respect of the three priority subjects were received for a wide variety of activities ranging from a Masters in Applied Linguistics to attendance at a Summer School. SEN activity included MEd in Autism, MA in Special and Inclusive Education, and PG Diploma in Specific Learning Difficulties Dyslexia.
The scholarships are awarded where applications are judged to be of sufficient merit. The first round of the scholarship scheme was awarded based on the following criteria:
Brian Lamb, Chair of the Lamb Inquiry and Achievement for All, said:
It is crucial that children with SEN, who have some of the most complex needs and challenges, are taught by teachers with the expertise and professional skills to match this challenge. This scholarship programme is vital if we are to have the well-trained and expert SEN teachers of the future. I was hugely impressed by the quality and commitment of the scholarship applicants. This programme can make a profound difference to the outcomes for children with SEN.
Ian McNeilly, Director of the National Association for the Teaching of English (NATE), said:
This prestigious national scholarship scheme will prove of great value to those teachers who have shown the initiative and commitment to undertake high-level professional development.
Professor Hilary Povey, Professor of Mathematics Education at Sheffield Hallam University, said:
It was encouraging to see mathematics teachers’ thirst for further study of their subject and how best to teach it. I was delighted to be part of the process.
The National Scholarship Fund for teachers is administered by the Training and Development Agency for Teachers (TDA). The next round of applications for the scholarship funding is due to open in spring 2012. Further details can be found on the Department for Education’s website .
| Region | English | Maths | Science | SEN< | Total (by Region) |
| East Midlands | 10 | 6 | 1 | 27 | 44 |
| East of England | 17 | 14 | 6 | 30 | 67 |
| London | 16 | 12 | 8 | 52 | 88 |
| North East | 2 | 2 | 1 | 16 | 21 |
| North West | 27 | 9 | 15 | 86 | 137 |
| South East | 29 | 10 | 9 | 62 | 110 |
| South West | 12 | 7 | 6 | 42 | 67 |
| West Midland | 13 | 4 | 3 | 44 | 64 |
| Yorkshire & Humber | 17 | 15 | 9 | 32 | 73 |
| Total (by type) | 143 | 79 | 58 | 391 | 671 |
Jennifer Alderson, from Newquay Tretherras School in Cornwall, will use the scholarship to fund her final dissertation at Huddersfield University. She said:
It’s a wonderful thing to be able to focus on teaching and to be provided with help to support such an important part of children’s learning. My research will particularly focus on how children can improve their response to long-answer questions in the new GCSE syllabus.
Natasha Imran, from Kingsway Park High School in Rochdale, is studying for an MSc in STEM Education at Manchester Met University. She said:
I’m really happy to receive some support that will have a very positive impact on my career. I am a chemist and work in a school with a high number of pupils with English as an additional language and Special Educational Needs, so aim to focus my work on them.
Helen Clark, from Emmanuel Holcombe CE Primary in Bury, Lancashire, plans to undertake a Masters degree in mathematics with Edge Hill University. She said:
I am very happy to learn that I have received a scholarship. It will enable me to look at the progress being made by more able children in mathematics and particularly at their ability with mental maths and problem solving.
Christopher Rollinson, from Royston Meadstead Primary School in Barnsley, applied for the Mathematics Specialist Teacher Programme at Sheffield Hallam University. He said:
The scholarship will allow me to direct my teaching, as a numeracy specialist and assistant head, towards improving numeracy teaching and learning across the school, and to raising standards.
Theresa Adams, from Middle Park Primary School in Eltham, London, is studying for Education MA with Literacy and Learning; Literacy and Creativity, at University of Greenwich. She said:
Wonderful news. I will be able to pass on the knowledge gained to other teachers, pupils and student teachers.
Siobhan Clegg, from Frankley Community High School in Birmingham, is studying difficulties in literacy development at The Open University. She said:
Brilliant! Very excited. Super. Fabulous.
Joanna Duffy, from Almondsbury Primary School in Bristol, applied for a postgraduate diploma in Specific Learning Difficulties at Bath Spa University. She said:
The course that I have applied for will enable me to carry out dyslexia assessment which will be invaluable to the school and their pupils. It will also provide parents with more valid information and guidance. I am very, very pleased to hear that I will have a scholarship to support my studies.
Brendan Anderson, from the Behaviour Support Team of Plymouth City Council, will use his scholarship to support his international Masters degree studies. He said:
Big changes are taking place which will involve more locality working. The course will give me more of a professional stance working alongside others and colleagues with Masters degrees which is great news for me.
From April 2012, the Teaching Agency, a new executive agency, will replace, amongst other bodies, the Training and Development Agency for Schools (TDA). It will be responsible for ensuring the supply of high-quality teachers and training, and for teacher regulation. This is subject to getting all necessary approvals, including Parliamentary approval of related legislation. More details are available on the arms length bodies section of the Department for Education’s website.
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 12 December 2011


Guidance
We’re asking women teachers to register for leadership coaching.
Register your interest in being coached , so we know you are taking part.
We are inviting women teachers at all stages of their career to develop their leadership skills through coaching. Coaching is at no cost to participants.
Our ambition is to have 1,000 aspiring women leaders supported through coaching. The scheme is at no cost to you. 73.8% of all classroom teachers are female; only 62% of headteachers are female. This data comes from the School Workforce in England: November 2015 statistical first release .
The leadership coaching pledge is one of the measures announced by government to support women in teaching.
This information is for women teachers who would like to get leadership coaching. If you would like to become a coach, please read our information on Women Leading in Education: become a coach .
Coaching is a one-to-one approach to professional development. It is non-directive and focuses on supporting the individual to find ways to develop their skills. It usually lasts for a short period and focuses on specific skills and goals.
Coaching can:
The most important thing is to be satisfied that the coach is right for you and will give you the support you need to move forward in your goals.
The coach profile includes details of their professional development, training and experience of coaching. Coaches also use a variety of styles and approaches that may or may not suit you so it is worth checking this area specifically.
Important points to consider are:
Coaching should be designed to suit your precise needs in terms of structure, content, style of delivery and overall duration. There is a great deal of flexibility in coaching and it depends on your needs, personal preference and learning style.
Coaching can be face-to-face, email, telephone, video based, digital or a mixture of these. For some people, having the right coach outweighs any geographic obstacles; for others it’s essential to be able to meet face-to-face regularly.
Check the coach profile for details of their professional development, training and experience of coaching someone like you. Coaches also use a variety of styles and approaches that may or may not suit you so it is worth checking about this area specifically.
The most important thing is to be satisfied that they are right for you and will give you the right support to enable you to move forward in your goals.
We ask coaches to sign up to the principles of good practice for coaches so that they:
Once you’ve registered your interest in being coached you should:
Once the coaching is complete, we suggest you and your coach complete an evaluation ( MS Word Document , 22.4KB ) . This is confidential between you and your coach.
You’ll also need to complete our participant feedback survey . This will help us to demonstrate that the coaching pledge is making a difference, meeting the needs of women teachers and increasing the number of women leaders in education.


Now we need to setup the media call. If you have a
webcam or mic, click Allow
to permit
the video or audio to be sent to your friend.
Don't worry, if you don't have a webcam or just
don't want to use it, you can still play endgame
(but you'll need to explicitly allow or
deny access regardless, or else the game
cannot continue).
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The pattern analysis works on activities which have to be generated before a host graph (e.g. parsed source code) can be analyzed.
We use a story diagram interpreter for the analysis, which will work on activities containing story patterns.
Such activities will be generated in the following manner:
Before we generate the classes, methods and its contained activities, we transform all Set Nodes into Set Fragments by wrapping them into a new, temporary set fragment.
For each specified pattern a new PatternAnnotation
EClass will be generated which will be used for annotations of the pattern when the story diagram is interpreted.
For each non-abstract pattern a new PatternEngine
EClass will be generated. This class contains the activities that will be interpreted to annotate the specified pattern on a host graph.
In the following all generated methods with its contained activity will be described.
Signature: annotate(EObject context, boolean searchAdditionals, PatternEngine this): AnnotationSet
The contained activity is the entry point to annotate the pattern. The following figure represents a general overview over the annotation activity.
Figure: General overview of the pattern annotation process
Signature: findAdditionals(ASGAnnotation annotation, PatternEngine this): void
This method and its contained activity will be generated when any additional element has been specified.


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