What is the difference between a RNN and LSTM in keras Explain with an example?

Recipe Objective

Difference between a RNN and LSTM? Explain with an example

RNN stands for *Recurrent Neural Networks* these are the first kind of neural network algorithm that can memorize or remember the previous inputs in memory.

It difficult to train RNN that requires long-term memorization meanwhile LSTM performs better in these kinds of datasets it has more additional special units that can hold information longer. LSTM includes a 'memory cell' that can maintain information in memory for long periods of time.

A Deep Dive into the Types of Neural Networks

Step 1- Importing Libraries

import keras from keras.models import Sequential from keras.layers import RNN, LSTM import numpy as np

Step 2- Creating the model.

We will create the sequential model with defining all the parameters

# define model where GRU is also output layer model_1 = Sequential() model_1.add(LSTM(1, input_shape=(20,1))) model_1.compile(optimizer='adam', loss='mse')

Step 3- Defining the sample array.

We will define the sample array to run through the model.

# input time steps y = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [9, 8, 7, 6, 5, 4, 3, 2, 1, 0], [11, 12, 13, 14, 15, 16, 17, 18, 19, 20], [21, 22, 23, 24, 25, 26, 27, 28, 29, 30], [31, 32, 33, 34, 35, 36, 37, 38, 39, 40]]).reshape((5,10,1)) # make and show prediction print(model_1.predict(y))

[[0.7465496 ]
 [0.38062745]
 [0.7608172 ]
 [0.76155245]
 [0.761592  ]]