Python with deep learning

0 comments

Assignment : Deep Learning

In this assignment, you’ll need amazon_review_500.csv for this assignment. This csv file has two columns as follows. The label column provides polarity sentiment, either positive or negative

label

text

2

I must admit that I’m addicted to “Version 2.0…

1

I think it’s such a shame that an enormous tal…

2

The Sunsout No Room at The Inn Puzzle has oddl…

Q1: Train a CNN classification model

Create a function sentiment_cnn( ) to detect sentiment as follows:

the input parameter is the full filename path to amazon_review_500.csv convert the text into padded sequences of numbers (see Exercise 5.2)

hold 20% of the data for testing

carefully select hyperparameters: max number of words for embedding layer, input sentence length, filters, the number of filters, batch size, and epoch etc. create a CNN model with the training data

print out accuracy, precision, recall calculated from testing data.

Your precision_macro, recall_macro, and accurracy should be all about 70%.

If your result is much lower than that (e.g. below 67%), you need to tune the hyperparameters

Also note that the label in the dataset is either 1 or 2. Your binary prediction out of CNN is either 0 or 1. Conversion is needed in order to compare predictions with actual labels

This function has no return. Besides your code, also provide a pdf document showing the following

How you choose the hyperparameters

Screenshots of model trainning history

Testing accuracy, precision, recall

Q2 Improve the performance of CNN model

Create a function improved_sentiment_cnn( ) to detect sentiment with improved accuracy

You still need to train a CNN model

You can apply diļ¬€erent techniques, e.g.

map words to pretrained word vectors

e.g. from Google

(https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?

usp=sharing

(https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?

usp=sharing)) or

e.g. from spacy package (https://spacy.io/usage/vectors-similarity (https://spacy.io/usage/vectors-similarity))

e.g. create your own pretrained word vectors using other review documents you can find

add additional features etc.

Your taraget is to improve the accuracy by about 5% from the model you created in Q1.

For fair comparison, make sure you use the same datasets for training/testing.

This function has no return. Please provide a pdf document showing the following

Screenshots of model trainning history

Testing accuracy, precision, recall

Your analysis about

what technique contributes to the performance improvement why this technique is useful

About the Author

Follow me


{"email":"Email address invalid","url":"Website address invalid","required":"Required field missing"}