This tutorial will teach you how to create an LSTM model in TensorFlow. You will learn how to use the Long Short Term Memory architecture to build a model that can learn and make predictions from data.

Check out this video for more information:

## Introduction to LSTMs

LSTM (Long Short-Term Memory) is a type of recurrent neural network that is capable of learning long-term dependencies. In other words, it can remember information for long periods of time.

Traditional neural networks are not able to do this because they forget information as they learn new information. This is known as the “short-term memory” problem.

LSTMs solve this problem by using a special type of cell that can remember information for long periods of time. This cell is called a “memory cell.”

Memory cells are connected to each other in a chain. This allows them to pass information from one cell to the next.

Each memory cell has three gates: an input gate, an output gate, and a forget gate. The gates control how information flows into and out of the cell.

The input gate controls how much information from the current input flows into the cell.

The output gate controls how much information from the cell flows out to the output.

The forget gate controls how much information from the previous input is forgotten by the cell.

## What are LSTMs?

LSTMs, or Long Short-Term Memory networks, are a type of recurrent neural network capable of learning long-term dependencies. They were introduced by Hochreiter & Schmidhuber (1997), and were designed to address the vanishing gradient problem that can occur when training traditional RNNs. LSTMs have been shown to outperform vanilla RNNs on a wide variety of tasks, and are now a standard component of many state-of-the-art models.

LSTMs work by incorporating a “forget” gate and an “output” gate into each unit of the network. The forget gate decides how much information from the previous time step to keep, while the output gate decides how much information from the current unit to output. This allows the network to effectively store and retrieve information over long periods of time.

## LSTM Use Cases

LSTMs were originally designed to learn sequential data. Since then, they’ve been used for a variety of tasks, including:

-Text classification

-Language translation

-Sequence to sequence prediction

-Video classification

-Music generation

## LSTM Implementation in TensorFlow

This article will provide an overview of Long Short-Term Memory (LSTM) networks and implementation in TensorFlow. LSTM networks are a type of recurrent neural network, capable of learning complex sequences. They have been successful in a variety of tasks, including:

– Language modeling

– Speech recognition

– Machine translation

– Handwriting recognition

LSTMs are designed to avoid the long-term dependency problem, and remember information for long periods of time. The forgetting gate allows the network to selectively forget certain parts of the input, while the input gate controls what new information is added to the memory.

## Creating the LSTM Model

In this section, we will create our Long Short-Term Memory (LSTM) model in TensorFlow. We will first import the required libraries, define some helper functions, and then create our LSTM model.

We will start by importing the required libraries. We will need the following libraries for this section:

-tensorflow

-numpy

-pandas

-matplotlib

-sklearn

We will also need to import some helper functions from the previous section. We have created a file called helper_functions.py that contains these functions. To import these functions, we will add the following lines of code at the beginning of our file:

import tensorflow as tf

import numpy as np

## Training the LSTM Model

In this section, we will train our LSTM model on the training data. We will need to import the following packages:

-tensorflow

-numpy

-keras

We will also need to specify the number of time steps and neurons in our LSTM model:

model = Sequential()

model.add(LSTM(neurons, input_shape=(time_steps, 1)))

model.add(Dense(1))

model.compile(loss=’mean_squared_error’, optimizer=’adam’)

model.fit(x_train, y_train, epochs=5, batch_size=1, verbose=1)

After training the model, we can evaluate its performance on the test data:

score = model.evaluate(x_test, y_test, verbose=0)

print(‘Test MSE:’, score)

## Evaluating the LSTM Model

In this section, we will evaluate our LSTM model. We will make predictions on unseen data and compare those to the known labels. In order to do this, we will use a confusion matrix and classification report.

The classification report shows us the accuracy, recall, f1-score, and support for each class. The accuracy is the number of correctly classified samples divided by the total number of samples. The recall is the number of correctly classified samples divided by the total number of samples in that class. The f1-score is a measure of how well the model can predict a class. It takes into account both the precision and recall. The support is the number of samples in that class.

The confusion matrix is a table that can help us visualize how well our model is performing. Each row in the table represents the actual class and each column represents the predicted class. The numbers in each cell represent how many times a sample was predicted to be in that class. Ideally, we would want all of the numbers in the diagonal to be high and all of other numbers to be low.

## Conclusion

As a final observation, we have seen how to build an LSTM model in TensorFlow. We have input our data into the model, trained it and made predictions with it. We have also seen how to save our trained model so that we can use it again in the future.

## Further Reading

If you want to learn more about LSTM’s in TensorFlow, we suggest these great resources:

– [Official TensorFlow LSTM tutorial](https://www.tensorflow.org/tutorials/sequences/recurrent_quickstart)

– [Great blog post on LSTMs](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

– [Another great blog post on LSTMs](https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-LSTMs/)

Keyword: LSTM Example in TensorFlow