Create a custom layer that changes the shape using lasagne.

In this recipe, we will see how to create custom layers and a layer that changes the shape using lasagne.

Recipe Objective - How to create a custom layer and a layer that changes the shape using lasagne?

To execute a custom layer in Lasagne, we have to write a Python class that subclasses "Layer" and

implements one method: get_output_for().

Links for the more related projects:-

/projects/data-science-projects/deep-learning-projects
/projects/data-science-projects/neural-network-projects

A Simple Layer:-

import lasagne

class DoubleLayer(lasagne.layers.Layer):
  def get_output_for(self, input, **kwargs):
    return 2 * input

A layer that changes the shape:-

class SumLayer(lasagne.layers.Layer):
  def get_output_for(self, input, **kwargs):
    return input.sum(axis=-1)

  def get_output_shape_for(self, input_shape):
    return input_shape[:-1]

What Users are saying..

profile image

Ray han

Tech Leader | Stanford / Yale University
linkedin profile url

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

Relevant Projects

PyCaret Project to Build and Deploy an ML App using Streamlit
In this PyCaret Project, you will build a customer segmentation model with PyCaret and deploy the machine learning application using Streamlit.

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.

Build CI/CD Pipeline for Machine Learning Projects using Jenkins
In this project, you will learn how to create a CI/CD pipeline for a search engine application using Jenkins.

Skip Gram Model Python Implementation for Word Embeddings
Skip-Gram Model word2vec Example -Learn how to implement the skip gram algorithm in NLP for word embeddings on a set of documents.

Stock Price Prediction Project using LSTM and RNN
Learn how to predict stock prices using RNN and LSTM models. Understand deep learning concepts and apply them to real-world financial data for accurate forecasting.

Build an optimal End-to-End MLOps Pipeline and Deploy on GCP
Learn how to build and deploy an end-to-end optimal MLOps Pipeline for Loan Eligibility Prediction Model in Python on GCP

Learn How to Build a Logistic Regression Model in PyTorch
In this Machine Learning Project, you will learn how to build a simple logistic regression model in PyTorch for customer churn prediction.

Expedia Hotel Recommendations Data Science Project
In this data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.

AWS MLOps Project for ARCH and GARCH Time Series Models
Build and deploy ARCH and GARCH time series forecasting models in Python on AWS .

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.