What is the difference between a RNN and LSTM in keras Explain with an example?
This recipe explains what is the difference between a RNN and LSTM in keras 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
Table of Contents
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 ]]
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