In this blog post, we’ll go over how to build an LSTM from scratch in TensorFlow. We’ll go over the theory behind LSTMs and how they work before implementing one in TensorFlow.

Check out our video for more information:

## Introduction

In this tutorial, we’ll be using the LSTM API in TensorFlow to create a model for generating text. We’ll be training our model on a set of Shakespearean sonnets, and then using it to generate new sonnets.

This tutorial is divided into three parts:

1. Preprocessing the data

2. Building the model

3. Training and generation

## What is an LSTM?

LSTMs are a type of neural network that is specifically designed to deal with sequence data.

Sequence data is data where each example is a sequence of items, such as a series of words in a sentence, or a series of measurements over time.

LSTMs are very effective at remember long-term dependencies in sequence data, and have been used successfully on tasks such as language modeling, machine translation, and image captioning.

In this tutorial, we’ll learn how to implement an LSTM in TensorFlow. We’ll go through the process of building the network step-by-step, and then we’ll train it on some real-world data.

## Why use an LSTM?

Long short-term memory (LSTM) networks are a type of recurrent neural network capable of learning and remembering over long periods of time. This is due to their unique structure which consists of a cell state and gates that control the flow of information into and out of the cell.

LSTMs were first proposed in the paper “Long Short-Term Memory” by Sepp Hochreiter and Jürgen Schmidhuber in 1997. Since then, they have been widely used in many different fields such as speech recognition, natural language processing and time series analysis.

There are many reasons why you might want to use an LSTM in your project. For example, they are often used for:

– Sequence classification

– Time series prediction

– Language modeling

## How to implement an LSTM in TensorFlow

LSTMs are a type of RNN, or recurrent neural network, that are well-suited to modeling time series data. That is, data where the order in which it occurs is important. In this tutorial, we’ll see how to implement an LSTM in TensorFlow.

TensorFlow includes a library for working with RNNs called tf.contrib.rnn. To use it, we first need to import it:

import tensorflow as tf

from tensorflow.contrib import rnn

Now, let’s create someplaceholders for our input data and output labels:

x = tf.placeholder(tf.float32, [None, seq_length, input_dim])

y = tf.placeholder(tf.float32, [None, output_dim])

Notice that we’re using a 3-dimensional placeholder for our input data (x). This is because an LSTM takes in a sequence of input vectors, not just a single vector. The first dimension is for the batch size (which can be None if we’re not training in batches), the second dimension is for the sequence length (which can be variable), and the third dimension is for the input dimensionality (which must be specified).

Our output labels (y) can be 2-dimensional since we’re only predicting one thing at a time (whether or not there’s an anomaly). However, if we wanted to predict multiple things at once (for example, multiple stocks simultaneously), then we would need to use a 3-dimensional placeholder here as well.

2 Set some hyperparameters

learning_rate = 0.01 # The speed of convergence optmizer= ‘rmsprop’ cost function=’mean squared error’ number of units=’256′ activation=’relu’ batch size= ‘128’ epochs= ’10’ dropout keep probability:’0.5′ sequence length=’10’ input dimensionality=’3′ output dimensionality=’1′

## The training process

To train the network, we first need to define the training data. For this example, we’ll use a toy dataset consisting of sequences of integers. Each integer will be mapped to a vector of floats, and the sequence of vectors will be fed into the network. We can generate this dataset easily using TensorFlow’s built-in functions.

Next, we need to define the network architecture. For this example, we’ll use a basic LSTM cell with three hidden layers.

Finally, we need to define the training procedure. We’ll use a standard gradient descent optimizer, and train for 100 epochs.

With all of this setup in place, we’re now ready to train the network!

## Evaluating the results

Once you have trained your model, you will want to evaluate its performance on some unseen data. The standard way to do this is to split your data into a training set and a test set. The model is trained on the training set and then its performance is evaluated on the test set. This evaluation gives you an idea of how well the model will generalize to new data.

There are various metrics that you can use to evaluate your model. The most common metric for classification tasks is accuracy. This is simply the percentage of items that are correctly classified by the model. Another common metric is precision, which is the percentage of items that are correctly classified as positive examples. Recall is the percentage of positive examples that are correctly classified.

In addition to these metrics, you may also want to consider theArea Under Curve (AUC) metric. This metric measures the area under the receiver operating characteristic (ROC) curve. The ROC curve is a plot of true positive rate against false positive rate. The AUC metric gives you a single number that represents the overall performance of the model on the test set.

Once you have evaluated your model on the test set, you can then deploy it in production and start using it to make predictions on new data points.

## Conclusion

Thank you for reading! This concludes our tutorial on implementing an LSTM in TensorFlow. We hope this was helpful in understanding how to work with LSTMs in TensorFlow. If you have any questions or feedback, feel free to reach out in the comments below.

## Further reading

If you want to implement an LSTM in TensorFlow, we recommend you check out the tutorials on the website:

– [TensorFlow RNN Tutorial](https://www.tensorflow.org/tutorials/recurrent)

– [Building an LSTM from scratch in TensorFlow](http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/)

## References

-A.graves, “Generating Sequences With Recurrent Neural Networks”, arXiv:1308.0850

-I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative Adversarial Nets”, in Advances in Neural Information Processing Systems 27 (NIPS), 2014

-M. radiator and U. Shaikh, “Understanding LSTM Networks”, colah’s blog, 2015

I am a data scientist and I have been working with TensorFlow for a while. In this article, I will show you how to implement an LSTM in TensorFlow. Long short-term memory (LSTM) is a recurrent neural network (RNN) architecture that has been very successful in modeling long-term dependencies in data.

Keyword: Implementing an LSTM in TensorFlow