Intent Classification using LSTM

Building a Deep Learning Model for Intent Classification

Intent Classification using LSTM                                             Photo by Jakub Jezovic 🦔 on Dribbble

What is intent?

Intent is the aim behind a specific action or set of actions.

Aim

To develop a Deep Learning Model for Intent Classification using Python programming Language and Keras on Cainvas Platform.

Prerequisites

Before getting started, you should have a good understanding of:

  1. Python programming language
  2. Keras — Deep learning library

Dataset

The CSV file that we are going to use can be directly downloaded through this link: https://cainvas-static.s3.amazonaws.com/media/user_data/vomchaithany/train.csv

Get the data file

output:

--2021-07-11 14:09:45--  https://cainvas-static.s3.amazonaws.com/media/user_data/vomchaithany/train.csv
Resolving cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)... 52.219.66.48
Connecting to cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)|52.219.66.48|:443... connected.
HTTP request sent, awaiting response... 304 Not Modified
File ‘train.csv’ not modified on server. Omitting download.

Import required libraries

Load the data

output:

Load Data
Load Data

Let’s shuffle the data

output:

The rows got shuffled
The rows got shuffled

output:

((7929,), (7929, 4))

output:

(\'In NH will it be chillier in West York at noon\', array([0, 1, 0, 0]))

Preprocessing

output:

((7929,), (7929, 4))

output:

((5729, 4000), (2200, 4000))

output:

(array([[   0,    0,    0, ...,    5,   11,   32],
        [   0,    0,    0, ...,  350,  977,  614],
        [   0,    0,    0, ...,    9, 4070, 2134],
        ...,
        [   0,    0,    0, ...,    4, 5446,  493],
        [   0,    0,    0, ...,  503,  562,  281],
        [   0,    0,    0, ...,   29,    4, 5447]], dtype=int32),
 array([[   0,    0,    0, ...,    5,   11,   32],
        [   0,    0,    0, ...,  350,  977,  614],
        [   0,    0,    0, ...,    9, 4070, 2134],
        ...,
        [   0,    0,    0, ...,   97,  195,  265],
        [   0,    0,    0, ...,   72,    5,   95],
        [   0,    0,    0, ..., 2294,   19,   10]], dtype=int32))

Build and Train the Model

output:

Model: "sequential_9"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_9 (Embedding)      (None, 4000, 32)          240704    
_________________________________________________________________
lstm_9 (LSTM)                (None, 10)                1720      
_________________________________________________________________
dropout_27 (Dropout)         (None, 10)                0         
_________________________________________________________________
dense_27 (Dense)             (None, 800)               8800      
_________________________________________________________________
dropout_28 (Dropout)         (None, 800)               0         
_________________________________________________________________
dense_28 (Dense)             (None, 200)               160200    
_________________________________________________________________
dropout_29 (Dropout)         (None, 200)               0         
_________________________________________________________________
dense_29 (Dense)             (None, 4)                 804       
=================================================================
Total params: 412,228
Trainable params: 412,228
Non-trainable params: 0
_________________________________________________________________

output:

Epoch 1/5
180/180 [==============================] - 19s 106ms/step - loss: 0.3748 - accuracy: 0.8284 - val_loss: 0.0190 - val_accuracy: 0.9945
Epoch 2/5
180/180 [==============================] - 19s 103ms/step - loss: 0.0857 - accuracy: 0.9672 - val_loss: 0.0049 - val_accuracy: 0.9991
Epoch 3/5
180/180 [==============================] - 19s 103ms/step - loss: 0.0665 - accuracy: 0.9738 - val_loss: 0.0033 - val_accuracy: 0.9995
Epoch 4/5
180/180 [==============================] - 19s 103ms/step - loss: 0.0512 - accuracy: 0.9808 - val_loss: 0.0024 - val_accuracy: 0.9991
Epoch 5/5
180/180 [==============================] - 19s 103ms/step - loss: 0.0444 - accuracy: 0.9831 - val_loss: 8.6375e-04 - val_accuracy: 0.9995

Save the model

Plots

Loss vs Validation Loss

output:

Intent Classification using LSTM: Loss vs Validation Loss
Loss vs Validation Loss

Accuracy vs Validation Accuracy

output:

Intent Classification using LSTM: Accuracy vs Validation Accuracy
Accuracy vs Validation Accuracy

Loss and Accuracy

output:

69/69 [==============================] - 3s 41ms/step - loss: 8.6375e-04 - accuracy: 0.9995
[0.0008637462160550058, 0.9995454549789429]

Custom Predictions

output:

\'BookRestaurant\'

output:

\'GetWeather\'

output:

\'RateBook\'

output:

\'PlayMusic\'

Cainvas Gallery: https://cainvas.ai-tech.systems/gallery/

Conclusion

We’ve trained our LSTM model on the Intent Classification dataset and got an accuracy of 99%.

Notebook Link: Here

Credit: Om Chaithanya V

Also Read: Neural Style Transfer

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