Friday, November 10, 2017

Convolutional Neural Networks - CNNs


Convolution: The primary purpose of Convolution in case of a CNNs is to extract features from the input image. Convolution preserves the spatial relationship between pixels by learning image features


In the computation above we slide the orange matrix over the original image (green) by 1 pixel (also called ‘stride’) and for every position, we compute element wise multiplication (between the two matrices) and add the multiplication outputs to get the final value which forms a single element of the output matrix (pink). Note that the 3×3 matrix “sees” only a part of the input image in each stride.

In CNNs terminology, the 3×3 matrix is called a ‘filter‘ or ‘kernel’ or ‘feature detector’ and the matrix formed by sliding the filter over the image and computing the dot product is called the ‘Convolved Feature’ or ‘Activation Map’ or the ‘Feature Map‘. It is important to note that filters acts as feature detectors from the original input image.


Saturday, November 04, 2017

Machine Learning - Model Evaluation Metrics


Confusion Matrix:




ROC (Receiver Operating Characteristics) and Area Under Curve (AUC)


ROC graphs are two-dimensional graphs in which tp rate (true positive rate or recall in above diagram) is plotted on the Y axis and fp rate is plotted on the X axis. An ROC graph depicts relative tradeoffs between benefits (true positives) and costs (false positives). More details: An introduction to ROC analysis by Tom Fawcett

For example, when you consider the results of a particular test in two populations, one population with a disease, the other population without the disease, you will rarely observe a perfect separation between the two groups. Indeed, the distribution of the test results will overlap, as shown in the following figure.


For every possible cut-off point or criterion value you select to discriminate between the two populations, there will be some cases with the disease correctly classified as positive (TP = True Positive fraction), but some cases with the disease will be classified negative (FN = False Negative fraction). On the other hand, some cases without the disease will be correctly classified as negative (TN = True Negative fraction), but some cases without the disease will be classified as positive (FP = False Positive fraction).

  • Sensitivity: probability that a test result will be positive when the disease is present (true positive rate, expressed as a percentage). 
  • Specificity: probability that a test result will be negative when the disease is not present (true negative rate, expressed as a percentage). 
  • Positive likelihood ratio: ratio between the probability of a positive test result given the presence of the disease and the probability of a positive test result given the absence of the disease, i.e. = True positive rate / False positive rate = Sensitivity / (1-Specificity)
  • Negative likelihood ratio: ratio between the probability of a negative test result given the presence of the disease and the probability of a negative test result given the absence of the disease, i.e. = False negative rate / True negative rate = (1-Sensitivity) / Specificity
  • Positive predictive value: probability that the disease is present when the test is positive (expressed as a percentage).
  • Negative predictive value: probability that the disease is not present when the test is negative (expressed as a percentage). 

In a Receiver Operating Characteristic (ROC) curve the true positive rate (Sensitivity) is plotted in function of the false positive rate (100-Specificity) for different cut-off points. Each point on the ROC curve represents a sensitivity/specificity pair corresponding to a particular decision threshold. A test with perfect discrimination (no overlap in the two distributions) has a ROC curve that passes through the upper left corner (100% sensitivity, 100% specificity). Therefore the closer the ROC curve is to the upper left corner, the higher the overall accuracy of the test - see next diagram.


The most important metric are the following:



Thursday, November 02, 2017

Deploy a Python app on Google Cloud

STEPS:
TUTORIALDIR=src/[YOUR_PROJECT_ID]/python_gae_quickstart-2017-11-01-23-03
git clone https://github.com/GoogleCloudPlatform/python-docs-samples $TUTORIALDIR
cd $TUTORIALDIR/appengine/standard/hello_world
dev_appserver.py $PWD
gcloud app deploy app.yaml --project [YOUR_PROJECT_ID]
The app runs on: https://[YOUR_PROJECT_ID].appspot.com/

Flask App on Google App Engine:

cd $TUTORIALDIR/appengine/standard/flask/tutorial
gcloud app deploy app.yaml --project [YOUR_PROJECT_ID]
Run the Flask app on: https://[YOUR_PROJECT_ID].appspot.com/form
Example: /src/tantal-183814/python_gae_quickstart-2017-11-01-23-03/appengine/standard/flask/tutorial

Using the Container Engine:

https://cloud.google.com/container-engine/docs/quickstart#optional_hello_app_code_review

This example makes use of a web app framework - a web application framework can simplify development by taking care of the details of the interface, letting you focus development effort on your applications features. App Engine includes a simple web application framework called webapp2 - a lightweight framework that allows you quickly build simple web applications for the Python 2.7 runtime.
webapp2 is compatible with the WSGI standard for Python web applications. You don't have to use webapp2 to write Python applications for App Engine. Other web application frameworks, such as Django, work with App Engine, and App Engine supports any Python code that uses the CGI standard. The webapp2 project, by Rodrigo Moraes, started as a fork of the App Engine webapp framework, which was used by the Python 2.5 runtime. webapp2 includes a number of features that make developing web applications easier, such as improved support for URI routing, session management and localization. The Python 2.7 runtime uses webapp2, and the project is maintained externally to App Engine. It is supported, but not maintained, by Google.
For more information about webapp2, see the official documentation.

RUNNING Django + setup a MySQL database instance on AppEngine: 
https://cloud.google.com/python/django/appengine#configure_the_database_settings

Wednesday, November 01, 2017

Google Cloud - Natural Language API


Apart from the Quickstart here:
https://cloud.google.com/natural-language/docs/

I used this code:
https://raw.githubusercontent.com/GoogleCloudPlatform/python-docs-samples/master/language/cloud-client/v1/quickstart.py

I had to do a few additional steps including:

Setting up Google Application Default Credentials - including setting the environment variable GOOGLE_APPLICATION_CREDENTIALS

Well described here:
https://developers.google.com/identity/protocols/application-default-credentials

Then I had to disable unwanted warnings based on the details here:
https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings