How to use LSTM using TF learn

This recipe helps you use LSTM using TF learn

Recipe Objective

This recipe explains how to use LSTM using TFLayer.

LSTM

Its syntax is: tflearn.layers.recurrent.lstm (incoming, n_units, activation='linear', inner_activation='softmax', dropout=None, bias=True, weights_init=None, forget_bias=1.0, return_seq=False, return_state=False, initial_state=None, dynamic=False, trainable=True, restore=True, reuse=False, scope=None, name='LSTM')

Comprehensive List of Computer Vision Project Ideas for Data Enthusiasts

def LSTM(incoming, n_units, activation='linear ', inner_activation='softmax', dropout=None, bias=True, weights_init=None, forget_bias=2.0, return_seq=False, return_state=False, initial_state=None, dynamic=False, trainable=True, restore=True, reuse=False, scope=None, name="LSTM"):

    c = BasicLSTMCell(n_units, activation=activation, inner_activation=inner_activation, forget_bias=forget_bias, bias=bias, weights_init=weights_init, trainable=trainable, restore=restore, reuse=reuse)

    n = _rnn_template(incoming, cell=cell, dropout=dropout, return_seq=return_seq, return_state=return_state, initial_state=initial_state, dynamic=dynamic, scope=scope, name=name)

    return n

What Users are saying..

profile image

Ameeruddin Mohammed

ETL (Abintio) developer at IBM
linkedin profile url

I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good... Read More

Relevant Projects

Build a Churn Prediction Model using Ensemble Learning
Learn how to build ensemble machine learning models like Random Forest, Adaboost, and Gradient Boosting for Customer Churn Prediction using Python

Learn How to Build a Linear Regression Model in PyTorch
In this Machine Learning Project, you will learn how to build a simple linear regression model in PyTorch to predict the number of days subscribed.

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.

Multilabel Classification Project for Predicting Shipment Modes
Multilabel Classification Project to build a machine learning model that predicts the appropriate mode of transport for each shipment, using a transport dataset with 2000 unique products. The project explores and compares four different approaches to multilabel classification, including naive independent models, classifier chains, natively multilabel models, and multilabel to multiclass approaches.

Tensorflow Transfer Learning Model for Image Classification
Image Classification Project - Build an Image Classification Model on a Dataset of T-Shirt Images for Binary Classification

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.

Image Segmentation using Mask R-CNN with Tensorflow
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.

Hands-On Approach to Causal Inference in Machine Learning
In this Machine Learning Project, you will learn to implement various causal inference techniques in Python to determine, how effective the sprinkler is in making the grass wet.

Personalized Medicine: Redefining Cancer Treatment
In this Personalized Medicine Machine Learning Project you will learn to classify genetic mutations on the basis of medical literature into 9 classes.

Medical Image Segmentation Deep Learning Project
In this deep learning project, you will learn to implement Unet++ models for medical image segmentation to detect and classify colorectal polyps.