# Keras: A Beginner’s Guide

In this tutorial, I will demonstrate how to create a simple feed-forward neural network using Keras and TensorFlow in Python. We will use the Keras library, which is a high-level library for deep learning, and TensorFlow as the backend. The tutorial will cover the basics of neural networks and how they work. We will then build a neural network to predict whether John will play basketball based on weather conditions. The code for this tutorial can be found on my GitHub.

## Table of Contents: Keras: A Beginner’s Guide – Medium

- Introduction to Keras and Deep Learning
- Understanding Neural Networks and their Applications
- Implementing a Feedforward Neural Network in Keras
- Training and Testing a Neural Network for Classification
- Full Python Script

### Introduction to Keras and Deep Learning

Deep learning is a subfield of machine learning that uses artificial neural networks to learn from data. Artificial neural networks are inspired by the human brain, and they can be used to solve a wide variety of problems, including image recognition, natural language processing, and speech recognition.

In this tutorial, we will learn how to use Keras to create a simple feed-forward neural network to predict whether or not someone will play basketball based on the weather conditions. We will use the TensorFlow backend for Keras, which is a popular deep learning library.

Prerequisites

Before we begin, let’s talk about TensorFlow and Keras. TensorFlow is an open-source library that uses large mathematical computations, popular for its ability to run either on CPUs or GPUs. Therefore, it makes an efficient library to perform machine learning and deep learning algorithms. Keras is a high-level library made for deep learning using separate libraries. Recently, it adopted TensorFlow as its latest release due to popularity, built on top of TensorFlow to make it easier to develop applications for deep learning faster and easier.

For this tutorial, we will need:

Python 3.6 or later

TensorFlow 2.0 or later

Keras 2.3 or later

Numpy 1.16 or later

Matplotlib 3.1 or later

Installation

To install TensorFlow, Keras, and Numpy, you can use the following commands:

pip install tensorflow

pip install keras

pip install numpy

To install Matplotlib, you can use the following command:

pip install matplotlib

If you are using Windows, you may need to install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019.

References

The following resources may be helpful:

Keras documentation

TensorFlow documentation

Numpy documentation

Matplotlib documentation

scikit-learn documentation

pandas documentation

seaborn documentation

Conclusion

In this tutorial, we learned how to use Keras to create a simple feed-forward neural network to predict whether or not someone will play basketball based on the weather conditions.

```
# Import the necessary libraries
import tensorflow as tf
from tensorflow.keras import models, layers
# Create the model
model = models.Sequential()
model.add(layers.Dense(10, activation='relu', input_shape=(4,)))
model.add(layers.Dense(10, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(X_train, y_train, epochs=10)
# Evaluate the model
model.evaluate(X_test, y_test)
# Save the model
model.save('my_model.h5')
```

### Understanding Neural Networks and their Applications

Neural networks are a subfield of machine learning inspired by the human brain. They consist of layers of interconnected nodes, or neurons, that can process information and learn from data. The input layer of a neural network receives data, which is then passed through the hidden layers to the output layer. Each layer of neurons performs a specific transformation on the data, and the output layer produces the final result.

Neural networks are used for a wide range of tasks, including image recognition, natural language processing, and speech recognition. They have been shown to be very effective in solving complex problems that are difficult for traditional machine learning algorithms to handle.

Some of the most common applications of neural networks include:

1) Image recognition: Neural networks can be trained to identify objects in images. This is useful for tasks such as self-driving cars, facial recognition, and medical imaging.

2) Natural language processing: Neural networks can be used to understand and generate human language. This is useful for tasks such as machine translation, spam filtering, and sentiment analysis. 3) Speech recognition: Neural networks can be used to recognize spoken words. This is useful for tasks such as voice control, dictation, and customer service.

```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# Create a neural network
model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(784,)))
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
# Compile the neural network
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Load the MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Reshape the data
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
# One-hot encode the labels
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
# Train the neural network
model.fit(x_train, y_train, epochs=10)
# Evaluate the neural network
model.evaluate(x_test, y_test)
# Save the neural network
model.save('my_model.h5')
```

### Implementing a Feedforward Neural Network in Keras

```
# Import the necessary libraries
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
# Create a Sequential model
model = Sequential()
# Add a Dense layer with 10 units and a ReLU activation function
model.add(Dense(10, activation='relu'))
# Add a Dense layer with 1 unit and a sigmoid activation function
model.add(Dense(1, activation='sigmoid'))
# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# Train the model
model.fit(X_train, y_train, epochs=10)
# Evaluate the model
model.evaluate(X_test, y_test)
# Save the model
model.save('my_model.h5')
```

Implementing a Feedforward Neural Network in Keras

In this tutorial, we will demonstrate how to create a simple feedforward neural network using Keras and TensorFlow in Python. If you are new to neural networks, I recommend watching this YouTube video by Siraj Raval, which shows how to create a simple feedforward neural network from scratch using NumPy and Python.

Before we begin, let’s talk about TensorFlow and Keras. TensorFlow is an open-source library used for large mathematical computations and is popular for its ability to run on either CPUs or GPUs. Therefore, it makes it an efficient library to perform machine learning and deep learning algorithms. Keras is a high-level library made for deep learning that uses separate libraries. Recently, it adopted TensorFlow as its backend in the latest release due to its popularity.

Built on top of TensorFlow, Keras makes it easier to develop deep learning applications faster and easier.

Prerequisites:

You must have Python 3.6 or later.

TensorFlow 2.0 installed. Refer to the following links to download and install it:

Windows

Mac

Linux

Jupyter Notebook

Numpy

Matplotlib

Now that we have all the necessary prerequisites installed, let’s import the required modules.

### Training and Testing a Neural Network for Classification

In this section, we will train and test a simple feedforward neural network for classification using Keras and TensorFlow. We will use the sklearn.datasets.load_iris() dataset, which contains 150 samples of iris flowers, each with four features (sepal length, sepal width, petal length, and petal width) and one label (iris species).

First, we need to import the necessary libraries.

“`

import tensorflow as tf

from tensorflow.keras import Sequential

from tensorflow.keras.layers import Dense, Activation

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

“`

Next, we load the iris dataset and split it into training and testing sets.

“`

iris = load_iris()

X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.25)

“`

We then standardize the data using the StandardScaler class.

“`

scaler = StandardScaler()

X_train = scaler.fit_transform(X_train)

X_test = scaler.transform(X_test)

“`

Now we can create our neural network. We will use a simple feedforward network with one hidden layer.

“`

model = Sequential()

model.add(Dense(10, activation=’tanh’, input_shape=(4,)))

model.add(Dense(3, activation=’softmax’))

“`

We then compile the model using the Adam optimizer and the sparse_categorical_crossentropy loss function.

“`

model.compile(optimizer=’adam’,

loss=’sparse_categorical_crossentropy’,

metrics=[‘accuracy’])

“`

Finally, we train the model using the fit() method.

“`

model.fit(X_train, y_train, epochs=100)

“`

We can then evaluate the model on the test set using the evaluate() method.

“`

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

print(‘Test loss:’, score[0])

print(‘Test accuracy:’, score[1])

“`

The output of the above code should be something like the following:

“`

Test loss: 0.0727899962079298

Test accuracy: 0.9666666388580322

“`

This shows that our model is able to achieve a high level of accuracy on the test set.

### Full Python Script Mentioned Earlier of Keras: A Beginner’s Guide

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