【図解で学ぶ】入門者向けシンプルAIの解説【Keras入門】

In this tutorial, we will be discussing the most simple form of artificial intelligence and how to understand it using Keras, a popular deep learning library in Python. This tutorial is beginner-friendly and assumes no prior knowledge of deep learning or artificial intelligence.

What is Artificial Intelligence?

Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and act like humans. AI encompasses various subfields such as machine learning, deep learning, neural networks, and more. In this tutorial, we will be focusing on a simple form of AI implemented using neural networks.

What is Keras?

Keras is an open-source deep learning library written in Python that provides a simple and efficient way to build and train neural networks. It is built on top of other deep learning frameworks like TensorFlow and Theano, allowing for fast prototyping and experimentation. Keras is known for its user-friendly API and flexibility, making it a popular choice for beginners and experts alike.

Getting Started with Keras

Before we delve into the details of creating a simple AI using Keras, you will need to have Python installed on your machine. You can download Python from the official website and install it following the instructions provided.

Once you have Python installed, you can install Keras using pip, a package manager for Python. Open a terminal or command prompt and run the following command:

pip install keras

This will download and install Keras and its dependencies on your machine. Once the installation is complete, you can start experimenting with building neural networks in Keras.

Creating a Simple AI with Keras

To create a simple AI using Keras, we will build a neural network to classify images of handwritten digits (0-9). This is a common beginner project in deep learning and a good starting point to understand how neural networks work.

First, let’s import the necessary libraries:

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Flatten
from keras.datasets import mnist
from keras.utils import to_categorical

Next, we will load the MNIST dataset, which contains 60,000 training images and 10,000 testing images of handwritten digits:

(x_train, y_train), (x_test, y_test) = mnist.load_data()

We will preprocess the data by reshaping the images and normalizing the pixel values to be between 0 and 1:

x_train = x_train.reshape(x_train.shape[0], 28, 28, 1) / 255.0
x_test = x_test.reshape(x_test.shape[0], 28, 28, 1) / 255.0

We will also one-hot encode the labels:

y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

Now, let’s build our neural network with a simple architecture using Keras:

model = Sequential([
    Flatten(input_shape=(28, 28, 1)),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

We have created a sequential model with a single input layer (Flatten), a hidden layer with 128 neurons and ReLU activation function, and an output layer with 10 neurons and softmax activation function.

Next, we will compile the model with an optimizer, loss function, and evaluation metric:

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Now, we can train our model on the training data:

model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))

Finally, we can evaluate the model on the testing data to see how well it performs:

loss, accuracy = model.evaluate(x_test, y_test)
print(f'Test accuracy: {accuracy}')

Congratulations! You have successfully built and trained a simple AI using Keras. This is just the beginning of your journey into the world of artificial intelligence and deep learning. From here, you can explore more complex neural network architectures, datasets, and problems to solve.

Conclusion

In this tutorial, we have covered the most simple form of artificial intelligence using Keras. We have discussed what AI is, introduced Keras as a deep learning library, and walked through a step-by-step guide to create a simple neural network for image classification. By following this tutorial, you should now have a basic understanding of how neural networks work and how to build one using Keras. Good luck on your deep learning journey!