Making a RNN model learn Arithmetic Operations

Making a RNN model learn Arithmetic Operations

Text Prediction using RNN.Given the phrase “34+17”, the model should predict the next word in the sequence “51”. The input and output is a sequence of characters which in turn an arithmetic expression of two numbers and its result. Thus our data is represented as a sequence of two words expression and result.

Problem:

Given the phrase “34+17”, the model should predict the next word in the sequence “51”. The input and output is a sequence of characters which in turn an arithmetic expression of two numbers and its result. Thus our data is represented as a sequence of two words expression and result.

Motivation:

As Recurrent Neural Networks(RNNs) are best suitable for processing sequential data, we are going to build a simple RNN model for solving this problem.

This can be implemented in 6 steps:

  1. Generating data
  2. Building model
  3. Vectoring, DE_vectoring data & remove the padding
  4. Creating dataset
  5. Training the model
  6. Predictions

Import necessary Libraries

Step 1: Generating data

We need to define a vocabulary with the required set of characters for the input and output strings. Thus the vocabulary consists of 0 to 9 digits, +, -, *, / and decimal(.) symbols.

The RNN model that we are building needs numeric values in tensors as an input. A suitable representation of this sequence of characters is one-hot encoded vectors. The dimension of the vector should be equal to the length of vocabulary, which is the total number of features. A dictionary needs to create to tokenize the characters into numeric values. Also, create another dictionary with indices as keys and corresponding characters as values that are used in later steps.

python rnn arithmetic operations

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