MACHINE LEARNING RECIPES
DATA CLEANING PYTHON
DATA MUNGING
PANDAS CHEATSHEET
ALL TAGS
What is the difference between a GRU and LSTM Explain with an example?
What is the difference between a GRU and LSTM Explain with an example?
This recipe explains what is the difference between a GRU and LSTM Explain with an example
Recipe Objective
Difference between a GRU and LSTM. Explaining with an example.
The key difference between GRU and LSTM is that GRU's bag has two gates that are reset and update while LSTM has three gates that are input, output, forget. GRU is less complex than LSTM because it has less number of gates.
If the dataset is small then GRU is preferred otherwise LSTM for the larger dataset.
GRU exposes the complete memory and hidden layers but LSTM doesn't.
Step 1- Importing Libraries
import keras
from keras.models import Sequential
from keras.layers import GRU, LSTM
import numpy as np
Step 2- Defining two different models
We will define two different models and Add a GRU layer in one model and an LSTM layer in the other model.
# define model where GRU is also output layer
model_1 = Sequential()
model_1.add(GRU(1, input_shape=(20,1)))
model_1.compile(optimizer='adam', loss='mse')
# define model where LSTM is also output layer
model_2 = Sequential()
model_2.add(LSTM(1, input_shape=(50,1)))
model_2.compile(optimizer='adam', loss='mse')
Step 3- Define a sample array.
We will define a sample array to run in both models.
# 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))
[[6.1044526e-01] [4.0416101e-01] [1.4171210e-02] [1.2617696e-04] [8.3446486e-07]]
# 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_2.predict(y))
[[-1.9881524e-02] [-5.2695298e-01] [-3.5639611e-04] [-3.7144428e-06] [-2.5736982e-08]]
Relevant Projects
Predict Employee Computer Access Needs in Python
Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.
Choosing the right Time Series Forecasting Methods
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.
Machine Learning project for Retail Price Optimization
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.
Customer Market Basket Analysis using Apriori and Fpgrowth algorithms
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.
Mercari Price Suggestion Challenge Data Science Project
Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.
Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.
Solving Multiple Classification use cases Using H2O
In this project, we are going to talk about H2O and functionality in terms of building Machine Learning models.
Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.
Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.
Resume parsing with Machine learning - NLP with Python OCR and Spacy
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.