Thursday, 12 November 2020

Data structures using Python: Coils 3.0.1

1) Follow Hari's python datastructure library on GitHub

2) Install

pip install pycoils

3) License

Apache2 License

4) New Features

11 November 2020:

Bit vector data structure

5) List of data structures

Stack using python list
Queue using python list
Heap (Min & Max) using python list.
Binary Search Tree with link inversion traversal
SplayTree -do-
SeperateChainHashTable (3 types of chaining using LinkedList, SplayTree, BinarySearchTree)
DisjointSetWithUnion (uses uptree nodes and path compression)
Bit Vector

6) Examples and usage

Refer: pycoils/examples package

Saturday, 30 November 2019

Deep Learning | Tensor flow | GPU (cuda/cuDNN) Vs CPU w and w/o EarlyStopping

In the previous post we built a neural net and tuned its hyper parameters. The hyper parameters were tuned using GridSearchCV. Here we look at modifying training the same network on GPU and compare that with training on CPU. 

Tools used
Tensorflow-gpu: 2.0.0
Keras version 2.3.1
Pandas version 0.25.3
Scikitlearn version 0.21.3

Nvidia GTX 960M 2GB
Intel i7 6700HQ 16 GB

Now, the number of epochs needed was tuned separately last time. Loss/accuracy Vs epochs curves raises the question of whether those many epochs are needed for this network and data. This has an impact on the time needed to train the network. Given the low end GPU the numbers are as expected.

Could we train the network in a lower duration with an acceptable loss of accuracy?

Early stopping is used here to answer this question. Results are shown below. Instead of going through 55 epochs, it decides to stop when the loss cannot be minimised beyond a certain point around 17-27 epochs. Early stopping parameters used is shown below.

Hyper parameter grid is small to begin with

Results (2 parameters and 3 values each)

Wall time Accuracy learning rate momentum
CPU 1 min 28 sec 0.978685 0.03 0.43
CPU Early Stopping 35.8 sec 0.973357 0.024 0.39
GPU 9 min 1 sec 0.978685 0.024 0.41
GPU Early Stopping 2 min 54 sec 0.971580 0.024 0.41

Results (2 parameters 5 values each)

Parameter grid is modified with additional ranges.

Wall time Accuracy learning rate momentum
CPU Early Stopping 1 min 35 sec 0.9822 0.03 0.39
GPU Early Stopping 9 min 37 sec 0.9751 0.033 0.41