ICE 8 - AvniM/Big-Data-Analytics GitHub Wiki

Link to source code

Team Members:

Name: Avni Mehta, Class Id: 11

Name: (Trinadha) Raji Muppala, Class Id: 14

Task 1:

Compare Softmax with Logistic Regression

Task 2:

Write about TensorFlow Basics including placeholder and feed_dict.

TensorFlow: TensorFlow is a framework created by Google for creating Deep Learning models. Deep Learning is a category of machine learning models that use multi-layer neural networks. The idea of deep learning has been around since 1943. Everything in TensorFlow is based on creating a computational graph.

Placeholders: Along with variables, we have placeholders in tensorflow. A placeholder is simply a variable that we will assign data to at a later date. It allows us to create our operations and build our computation graph, without needing the data. In TensorFlow terminology, we then feed data into the graph through these placeholders.

Syntax: tf.placeholder(dtype, shape=None, name=None)

Example: a = tf.placeholder(tf.float32, shape=[3])

Feed Dict: While running tensorflow computation, you can supply data using feed_dict. In simple words, feed_dict feeds data to your model.

Example: For tf session "sess", sess.run(x, feed_dict=my_data_dict)

Task 3 Objective:

Implement softmax clasification for MNIST dataset. Report the accuracy. Show the tensorboard

Input:

MNIST dataset: a large database of handwritten digits that contains 60,000 training images and 10,000 testing images.

Code:

Have used the same code provided in the use case.

Output:

Accuracy is about 90%. This is not great because one of the CNN classifer has achieved about 99.79% accuracy for this dataset.

Cross-Entropy:

Histogram:

Distributions:

Graph:

Observation:

This is not great because one of the CNN classifer has achieved about 99.79% accuracy for MNIST dataset.

Contritbution of Team Members:

100% for both since both team members completed the assignment individually.