What are Recurrent layers in lasagne layers?

This recipe explains what are Recurrent layers in lasagne layers.

Recipe Objective - What are Recurrent layers in lasagne layers?

Layers to construct recurrent networks. Recurrent layers can be used similarly to feed-forward layers except that the

input shape is expected to be ("batch_size", "sequence_length", "num_inputs").

Links for the more related projects:-

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

Recurrent layers:-

1. CustomRecurrentLayer - A layer which implements a recurrent connection.

2. RecurrentLayer - Dense recurrent neural network (RNN) layer.

3. LSTMLayer - A long short-term memory (LSTM) layer.

4. GRULayer - Gated Recurrent Unit (GRU) Layer.

Example:-

from lasagne.layers import *
num_inputs, num_units, num_classes = 10, 12, 5
# By setting the first two dimensions as None, we are allowing them to vary
# They correspond to batch size and sequence length, so we will be able to
# feed in batches of varying size with sequences of varying length.
ly_inp = InputLayer((None, None, num_inputs))
# We can retrieve symbolic references to the input variable's shape
# we will later use in reshape layers.
batchsize, seqlen, _ = ly_inp.input_var.shape
ly_lstm = LSTMLayer(ly_inp, num_units=num_units)
# In order to connect a recurrent layer to a dense layer

ly_shp = ReshapeLayer(ly_lstm, (-1, num_units))
ly_dense = DenseLayer(ly_shp, num_units=num_classes)

# To reshape back to our original shape, we can use the symbolic shape
ly_out = ReshapeLayer(ly_dense, (batchsize, seqlen, num_classes))


import lasagne
no_batch, no_steps, no_in = (2, 3, 4)
no_hid = 5
ly_in = lasagne.layers.InputLayer((no_batch, no_steps, no_in))
ly_in_hid = lasagne.layers.DenseLayer(lasagne.layers.InputLayer((None, no_in)), no_hid)
ly_hid_hid = lasagne.layers.DenseLayer(lasagne.layers.InputLayer((None, no_hid)), no_hid)
ly_rec = lasagne.layers.CustomRecurrentLayer(ly_in, ly_in_hid, ly_hid_hid)

What Users are saying..

profile image

Jingwei Li

Graduate Research assistance at Stony Brook University
linkedin profile url

ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data.... Read More

Relevant Projects

Build a Text Classification Model with Attention Mechanism NLP
In this NLP Project, you will learn to build a multi class text classification model with attention mechanism.

Learn to Build an End-to-End Machine Learning Pipeline - Part 2
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, incorporating Hopsworks' feature store and Weights and Biases for model experimentation.

AWS MLOps Project for Gaussian Process Time Series Modeling
MLOps Project to Build and Deploy a Gaussian Process Time Series Model in Python on AWS

Build an End-to-End AWS SageMaker Classification Model
MLOps on AWS SageMaker -Learn to Build an End-to-End Classification Model on SageMaker to predict a patient’s cause of death.

Build a Customer Churn Prediction Model using Decision Trees
Develop a customer churn prediction model using decision tree machine learning algorithms and data science on streaming service data.

PyTorch Project to Build a GAN Model on MNIST Dataset
In this deep learning project, you will learn how to build a GAN Model on MNIST Dataset for generating new images of handwritten digits.

Time Series Forecasting with LSTM Neural Network Python
Deep Learning Project- Learn to apply deep learning paradigm to forecast univariate time series data.

Time Series Analysis with Facebook Prophet Python and Cesium
Time Series Analysis Project - Use the Facebook Prophet and Cesium Open Source Library for Time Series Forecasting in Python

Mastering A/B Testing: A Practical Guide for Production
In this A/B Testing for Machine Learning Project, you will gain hands-on experience in conducting A/B tests, analyzing statistical significance, and understanding the challenges of building a solution for A/B testing in a production environment.

NLP Project for Multi Class Text Classification using BERT Model
In this NLP Project, you will learn how to build a multi-class text classification model using using the pre-trained BERT model.