## Introduction

Welcome, Python enthusiasts! If you’re looking to level up your Python skills and embark on a journey to master the art of long short-term memory (LSTM) using Python 3, you’re in the right place. In this blog post, we will explore LSTM, break it down step by step, provide you with detailed explanations, and, of course, share code and plots to make your learning experience as smooth as possible.

Whether you’re an 18-year-old student just getting started or a 30-year-old professional seeking to enhance your Python expertise, this guide is designed to cater to your needs. By the end of this journey, you’ll have the skills to implement LSTMs effectively and advance in your Python programming journey.

Let’s dive in!

## What is Long Short-Term Memory(LSTM)?

Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture used in machine learning and deep learning. It’s designed to process and make predictions on sequences of data, making it especially useful for tasks like time series forecasting, natural language processing, and speech recognition.

What sets LSTM apart from traditional RNNs is its ability to capture long-range dependencies and remember information from earlier time steps in a more effective way. This is achieved through a combination of specialized memory cells and gating mechanisms, allowing LSTMs to maintain and update information over extended sequences, making them highly valuable in a wide range of applications.

**Understanding Long Short-Term Memory (LSTM)**

Before we jump into coding, it’s crucial to understand what LSTM is and how it works. Think of LSTM as a specialized type of recurrent neural network (RNN) that’s well-suited for tasks involving sequences, like time series data or natural language processing.

LSTMs are exceptionally good at capturing long-range dependencies and patterns within data, making them a powerful tool in various domains, such as speech recognition, text generation, and financial predictions.

**Why Use Python for LSTM?**

Python has become the go-to language for data science, machine learning, and deep learning, thanks to its rich ecosystem of libraries and user-friendly syntax. Python 3 is the latest major version, offering improved performance and various enhancements over Python 2. By learning and working with Python 3, you’re ensuring that your skills remain relevant and future-proof.

Now, let’s get hands-on with LSTM using Python 3.

## How does Long Short-Term Memory(LSTM) works?

Long Short-Term Memory (LSTM) is a specialized type of recurrent neural network (RNN) architecture designed to overcome some of the limitations of traditional RNNs, such as the vanishing gradient problem, when dealing with sequences of data. LSTMs achieve this by introducing memory cells and gating mechanisms that enable them to capture and retain information over longer sequences effectively. Here’s how LSTMs work in deep learning:

**Sequence Input**: LSTMs are used for tasks involving sequential data. This can be time series data, natural language text, audio signals, or any other type of data that has a sequential nature.**Memory Cells**: LSTMs have memory cells that act as storage units. These cells can store information over time and decide what to keep or discard, depending on the context. This ability to store and retrieve information is what makes LSTMs suitable for long-range dependencies.**Gating Mechanisms**: LSTMs have three main gates:**Forget Gate**: This gate determines what information from the previous cell state should be forgotten or retained. It takes as input the previous cell state and the current input, and it produces a value between 0 and 1 for each element in the cell state. A value of 0 means “completely forget,” and 1 means “completely retain.”**Input Gate**: This gate decides what new information is relevant to add to the cell state. It takes into account the previous cell state and the current input and produces a candidate cell state.**Output Gate**: The output gate controls what information from the cell state should be used to produce the output of the LSTM. It filters the cell state and produces the output of the LSTM unit.

**Updating the Memory Cell**: The memory cell state is updated by combining the result of the forget gate (what to remove), the input gate (what to add), and the previous cell state. The new cell state is computed as a combination of these components.**Output**: The LSTM produces two outputs at each time step: the new cell state and the output hidden state. The cell state is used for the next time step, and the output hidden state is used for predictions or can be passed to subsequent layers in a deep neural network.**Training**: LSTMs are trained using backpropagation through time (BPTT). During training, the model’s parameters are updated to minimize a specified loss function, typically using an optimization algorithm like stochastic gradient descent (SGD).

The combination of these memory cells and gating mechanisms allows LSTMs to effectively capture and utilize information from long sequences, making them incredibly powerful in tasks like language modeling, machine translation, speech recognition, and time series forecasting. LSTMs have played a pivotal role in advancing the state of the art in many natural language processing and sequential data analysis tasks.

**Setting Up Your Environment**

Before we start coding, you need to set up your environment. We recommend using Jupyter Notebook for this tutorial, as it offers an interactive and convenient way to work with code and visualize results. Ensure you have Python 3 installed, and install the required libraries, such as TensorFlow and Matplotlib.

```
# Install TensorFlow
pip install tensorflow
# Install numpy, pandas, Matplotlib for
pip install numpy pandas matplotlib
```

**Importing Libraries**

To get started, let’s import the necessary libraries. We’ll use TensorFlow for building our LSTM model and Matplotlib for data visualization.

```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
```

**Choosing a Dataset**

For our LSTM adventure, we need a dataset to work with. We’ll use a sample dataset to demonstrate the power of LSTM in time series forecasting. Let’s consider a hypothetical dataset of daily temperature records.

```
# Sample dataset
temperature_data = np.array([20, 21, 22, 24, 27, 30, 32, 33, 35, 34, 32, 29, 25, 22, 21, 20, 18, 17, 16, 15])
```

In this dataset, we have daily temperature records over a 20-day period. Our goal is to predict the temperature for the next day based on the previous days’ data. LSTMs are perfect for this type of time series forecasting task.

**Data Preprocessing**

Before feeding the data into our LSTM model, we need to preprocess it. This involves splitting the data into training and testing sets, normalizing it, and shaping it to be suitable for LSTM input.

```
# Splitting the data into training and testing sets
train_size = int(len(temperature_data) * 0.67)
train_data, test_data = temperature_data[0:train_size], temperature_data[train_size:]
# Normalize the data
train_data = (train_data - np.min(train_data)) / (np.max(train_data) - np.min(train_data))
test_data = (test_data - np.min(test_data)) / (np.max(test_data) - np.min(test_data))
# Function to create sequences from the data
def create_sequences(data, seq_length):
sequences = []
for i in range(len(data) - seq_length):
sequence = data[i:i+seq_length]
sequences.append(sequence)
return np.array(sequences)
# Choose a sequence length (e.g., 5 days)
seq_length = 5
X_train = create_sequences(train_data, seq_length)
y_train = train_data[seq_length:]
X_test = create_sequences(test_data, seq_length)
y_test = test_data[seq_length:]
```

**Building the LSTM Model**

Now, it’s time to create our LSTM model. We’ll build a simple sequential model with one LSTM layer and one dense output layer.

```
model = Sequential()
# LSTM layer with 50 units
model.add(LSTM(50, input_shape=(seq_length, 1)))
# Output layer
model.add(Dense(1))
# Compile the model
model.compile(loss='mean_squared_error', optimizer='adam')
# Print a summary of the model
model.summary()
```

```
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm (LSTM) (None, 50) 10400
dense (Dense) (None, 1) 51
=================================================================
Total params: 10451 (40.82 KB)
Trainable params: 10451 (40.82 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
```

**Training the Model**

Let’s train our model using the training data. We’ll set the number of epochs and batch size to control the training process.

```
# Train the model
model.fit(X_train, y_train, epochs=100, batch_size=1, verbose=2)
```

```
Epoch 1/100
8/8 - 1s - loss: 0.5463 - 1s/epoch - 145ms/step
Epoch 2/100
8/8 - 0s - loss: 0.3353 - 16ms/epoch - 2ms/step
Epoch 3/100
8/8 - 0s - loss: 0.1972 - 16ms/epoch - 2ms/step
Epoch 4/100
8/8 - 0s - loss: 0.1310 - 16ms/epoch - 2ms/step
Epoch 5/100
8/8 - 0s - loss: 0.1035 - 28ms/epoch - 4ms/step
Epoch 6/100
8/8 - 0s - loss: 0.0984 - 16ms/epoch - 2ms/step
Epoch 7/100
8/8 - 0s - loss: 0.0966 - 16ms/epoch - 2ms/step
Epoch 8/100
8/8 - 0s - loss: 0.0884 - 31ms/epoch - 4ms/step
Epoch 9/100
8/8 - 0s - loss: 0.0809 - 31ms/epoch - 4ms/step
Epoch 10/100
8/8 - 0s - loss: 0.0909 - 29ms/epoch - 4ms/step
Epoch 11/100
8/8 - 0s - loss: 0.0804 - 16ms/epoch - 2ms/step
Epoch 12/100
8/8 - 0s - loss: 0.0743 - 16ms/epoch - 2ms/step
Epoch 13/100
8/8 - 0s - loss: 0.0754 - 16ms/epoch - 2ms/step
Epoch 14/100
8/8 - 0s - loss: 0.0695 - 31ms/epoch - 4ms/step
Epoch 15/100
8/8 - 0s - loss: 0.0641 - 31ms/epoch - 4ms/step
Epoch 16/100
8/8 - 0s - loss: 0.0620 - 32ms/epoch - 4ms/step
Epoch 17/100
8/8 - 0s - loss: 0.0574 - 31ms/epoch - 4ms/step
Epoch 18/100
8/8 - 0s - loss: 0.0563 - 16ms/epoch - 2ms/step
Epoch 19/100
8/8 - 0s - loss: 0.0534 - 31ms/epoch - 4ms/step
Epoch 20/100
8/8 - 0s - loss: 0.0515 - 32ms/epoch - 4ms/step
Epoch 21/100
8/8 - 0s - loss: 0.0484 - 16ms/epoch - 2ms/step
Epoch 22/100
8/8 - 0s - loss: 0.0463 - 16ms/epoch - 2ms/step
Epoch 23/100
8/8 - 0s - loss: 0.0475 - 31ms/epoch - 4ms/step
Epoch 24/100
8/8 - 0s - loss: 0.0412 - 16ms/epoch - 2ms/step
Epoch 25/100
8/8 - 0s - loss: 0.0386 - 32ms/epoch - 4ms/step
Epoch 26/100
8/8 - 0s - loss: 0.0417 - 16ms/epoch - 2ms/step
Epoch 27/100
8/8 - 0s - loss: 0.0348 - 31ms/epoch - 4ms/step
Epoch 28/100
8/8 - 0s - loss: 0.0330 - 16ms/epoch - 2ms/step
Epoch 29/100
8/8 - 0s - loss: 0.0329 - 32ms/epoch - 4ms/step
Epoch 30/100
8/8 - 0s - loss: 0.0313 - 16ms/epoch - 2ms/step
Epoch 31/100
8/8 - 0s - loss: 0.0304 - 32ms/epoch - 4ms/step
Epoch 32/100
8/8 - 0s - loss: 0.0308 - 15ms/epoch - 2ms/step
Epoch 33/100
8/8 - 0s - loss: 0.0290 - 23ms/epoch - 3ms/step
Epoch 34/100
8/8 - 0s - loss: 0.0356 - 8ms/epoch - 1ms/step
Epoch 35/100
8/8 - 0s - loss: 0.0268 - 31ms/epoch - 4ms/step
Epoch 36/100
8/8 - 0s - loss: 0.0296 - 16ms/epoch - 2ms/step
Epoch 37/100
8/8 - 0s - loss: 0.0282 - 16ms/epoch - 2ms/step
Epoch 38/100
8/8 - 0s - loss: 0.0277 - 16ms/epoch - 2ms/step
Epoch 39/100
8/8 - 0s - loss: 0.0272 - 16ms/epoch - 2ms/step
Epoch 40/100
8/8 - 0s - loss: 0.0266 - 17ms/epoch - 2ms/step
Epoch 41/100
8/8 - 0s - loss: 0.0266 - 15ms/epoch - 2ms/step
Epoch 42/100
8/8 - 0s - loss: 0.0270 - 32ms/epoch - 4ms/step
Epoch 43/100
8/8 - 0s - loss: 0.0286 - 19ms/epoch - 2ms/step
Epoch 44/100
8/8 - 0s - loss: 0.0326 - 16ms/epoch - 2ms/step
Epoch 45/100
8/8 - 0s - loss: 0.0265 - 33ms/epoch - 4ms/step
Epoch 46/100
8/8 - 0s - loss: 0.0255 - 14ms/epoch - 2ms/step
Epoch 47/100
8/8 - 0s - loss: 0.0260 - 25ms/epoch - 3ms/step
Epoch 48/100
8/8 - 0s - loss: 0.0246 - 23ms/epoch - 3ms/step
Epoch 49/100
8/8 - 0s - loss: 0.0238 - 28ms/epoch - 3ms/step
Epoch 50/100
8/8 - 0s - loss: 0.0263 - 21ms/epoch - 3ms/step
Epoch 51/100
8/8 - 0s - loss: 0.0236 - 16ms/epoch - 2ms/step
Epoch 52/100
8/8 - 0s - loss: 0.0244 - 16ms/epoch - 2ms/step
Epoch 53/100
8/8 - 0s - loss: 0.0237 - 16ms/epoch - 2ms/step
Epoch 54/100
8/8 - 0s - loss: 0.0254 - 16ms/epoch - 2ms/step
Epoch 55/100
8/8 - 0s - loss: 0.0233 - 31ms/epoch - 4ms/step
Epoch 56/100
8/8 - 0s - loss: 0.0230 - 32ms/epoch - 4ms/step
Epoch 57/100
8/8 - 0s - loss: 0.0236 - 16ms/epoch - 2ms/step
Epoch 58/100
8/8 - 0s - loss: 0.0239 - 16ms/epoch - 2ms/step
Epoch 59/100
8/8 - 0s - loss: 0.0260 - 41ms/epoch - 5ms/step
Epoch 60/100
8/8 - 0s - loss: 0.0217 - 22ms/epoch - 3ms/step
Epoch 61/100
8/8 - 0s - loss: 0.0195 - 16ms/epoch - 2ms/step
Epoch 62/100
8/8 - 0s - loss: 0.0216 - 15ms/epoch - 2ms/step
Epoch 63/100
8/8 - 0s - loss: 0.0208 - 32ms/epoch - 4ms/step
Epoch 64/100
8/8 - 0s - loss: 0.0200 - 31ms/epoch - 4ms/step
Epoch 65/100
8/8 - 0s - loss: 0.0198 - 31ms/epoch - 4ms/step
Epoch 66/100
8/8 - 0s - loss: 0.0190 - 22ms/epoch - 3ms/step
Epoch 67/100
8/8 - 0s - loss: 0.0186 - 10ms/epoch - 1ms/step
Epoch 68/100
8/8 - 0s - loss: 0.0188 - 33ms/epoch - 4ms/step
Epoch 69/100
8/8 - 0s - loss: 0.0185 - 15ms/epoch - 2ms/step
Epoch 70/100
8/8 - 0s - loss: 0.0175 - 31ms/epoch - 4ms/step
Epoch 71/100
8/8 - 0s - loss: 0.0193 - 16ms/epoch - 2ms/step
Epoch 72/100
8/8 - 0s - loss: 0.0181 - 32ms/epoch - 4ms/step
Epoch 73/100
8/8 - 0s - loss: 0.0171 - 16ms/epoch - 2ms/step
Epoch 74/100
8/8 - 0s - loss: 0.0164 - 31ms/epoch - 4ms/step
Epoch 75/100
8/8 - 0s - loss: 0.0168 - 32ms/epoch - 4ms/step
Epoch 76/100
8/8 - 0s - loss: 0.0159 - 31ms/epoch - 4ms/step
Epoch 77/100
8/8 - 0s - loss: 0.0155 - 33ms/epoch - 4ms/step
Epoch 78/100
8/8 - 0s - loss: 0.0153 - 16ms/epoch - 2ms/step
Epoch 79/100
8/8 - 0s - loss: 0.0158 - 15ms/epoch - 2ms/step
Epoch 80/100
8/8 - 0s - loss: 0.0181 - 31ms/epoch - 4ms/step
Epoch 81/100
8/8 - 0s - loss: 0.0135 - 16ms/epoch - 2ms/step
Epoch 82/100
8/8 - 0s - loss: 0.0142 - 31ms/epoch - 4ms/step
Epoch 83/100
8/8 - 0s - loss: 0.0129 - 19ms/epoch - 2ms/step
Epoch 84/100
8/8 - 0s - loss: 0.0128 - 16ms/epoch - 2ms/step
Epoch 85/100
8/8 - 0s - loss: 0.0123 - 31ms/epoch - 4ms/step
Epoch 86/100
8/8 - 0s - loss: 0.0155 - 32ms/epoch - 4ms/step
Epoch 87/100
8/8 - 0s - loss: 0.0120 - 31ms/epoch - 4ms/step
Epoch 88/100
8/8 - 0s - loss: 0.0129 - 31ms/epoch - 4ms/step
Epoch 89/100
8/8 - 0s - loss: 0.0104 - 32ms/epoch - 4ms/step
Epoch 90/100
8/8 - 0s - loss: 0.0155 - 16ms/epoch - 2ms/step
Epoch 91/100
8/8 - 0s - loss: 0.0099 - 16ms/epoch - 2ms/step
Epoch 92/100
8/8 - 0s - loss: 0.0101 - 16ms/epoch - 2ms/step
Epoch 93/100
8/8 - 0s - loss: 0.0132 - 31ms/epoch - 4ms/step
Epoch 94/100
8/8 - 0s - loss: 0.0119 - 31ms/epoch - 4ms/step
Epoch 95/100
8/8 - 0s - loss: 0.0113 - 16ms/epoch - 2ms/step
Epoch 96/100
8/8 - 0s - loss: 0.0096 - 31ms/epoch - 4ms/step
Epoch 97/100
8/8 - 0s - loss: 0.0083 - 22ms/epoch - 3ms/step
Epoch 98/100
8/8 - 0s - loss: 0.0091 - 25ms/epoch - 3ms/step
Epoch 99/100
8/8 - 0s - loss: 0.0084 - 16ms/epoch - 2ms/step
Epoch 100/100
8/8 - 0s - loss: 0.0078 - 31ms/epoch - 4ms/step
1/1 [==============================] - 0s 283ms/step
1/1 [==============================] - 0s 16ms/step
```

**Evaluating the Model**

Now that our model is trained, we need to evaluate its performance. We’ll do this by making predictions on the test data and visualizing the results.

```
# Predictions
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)
# Inverse transform the predictions to get them in the original scale
train_predict = (train_predict * (np.max(train_data) - np.min(train_data))) + np.min(train_data)
test_predict = (test_predict * (np.max(test_data) - np.min(test_data))) + np.min(test_data)
# Plot the results
plt.figure(figsize=(12, 6))
plt.plot(temperature_data, label='Actual Data')
plt.plot(range(seq_length, train_size), train_predict, label='Training Predictions')
plt.plot(range(train_size + seq_length, len(temperature_data)), test_predict, label='Testing Predictions')
plt.legend()
plt.title('Temperature Forecasting with LSTM')
plt.xlabel('Days')
plt.ylabel('Temperature')
plt.show()
```

**Conclusion**

Congratulations! You’ve just unlocked the potential of Long Short-Term Memory (LSTM) using Python 3. In this extensive guide, we’ve covered the fundamental concepts of LSTM, set up the environment, preprocessed data, built an LSTM model, and evaluated its performance using a sample time series dataset.

We hope this journey has been informative and fun. Python is a versatile language, and LSTMs are just one of the many exciting tools in your Python toolkit. With your newfound knowledge, you’re well on your way to becoming a Python pro.

Keep experimenting with different datasets and parameters to gain more experience. Python 3 offers limitless possibilities for your journey in data science, machine learning, and deep learning.

Stay curious, keep coding, and continue on your path to Python mastery! If you have any questions or need further clarification, feel free to ask.

Also, check out our other playlist Rasa Chatbot, Internet of things, Docker, Python Programming, Machine Learning, MQTT, Tech News, ESP-IDF etc.

Become a member of our social family on youtube here.

Stay tuned and Happy Learning. ✌🏻😃

Happy coding! ❤️🔥