Neural Network with Keras in Python has found its way into various applications, including artificial intelligence and machine learning. In this tutorial, we will be discussing how to build a neural network using Keras and Python, one of the most commonly used machine learning libraries.
Neural Network with Keras: What is Keras?
Keras is an open-source neural network library that is used to build and train deep learning models. It is built on top of other machine learning libraries such as TensorFlow and Theano, which gives it the advantage of being able to run on both CPUs and GPUs.
Introduction to Neural Networks
Neural networks are modeled after the human brain and can be used to perform a variety of tasks. They are commonly used in image recognition, speech recognition, and natural language processing. Neural networks can be deep or shallow, depending on the number of layers they have.
Deep neural networks are composed of multiple layers, each with their own set of weights. These weights are learned during training, and the network can then use them to make predictions. In this tutorial, we will be building a deep neural network using Keras.
1. Setting up the Environment
Before we get started, we need to install Keras and its dependencies. We can do this by running the following command:
pip install keras
We also need to import the necessary libraries:
import numpy as np import keras from keras.models import Sequential from keras.layers import Dense from keras.optimizers import Adam
2. Building the Neural Network
Now that we have all the required libraries installed and imported, we can start building our neural network. First, we need to define the architecture of our network. For this tutorial, we will be building a simple neural network with one input layer, one hidden layer, and one output layer.
model = Sequential() model.add(Dense(10, input_shape=(2,), activation='relu')) model.add(Dense(1, activation='sigmoid'))
In the above code, we define our model as a Sequential model and add our layers one by one. Our input layer has two neurons, and our hidden layer has ten neurons with a ReLU activation function. Our output layer has one neuron, with a sigmoid activation function.
3. Compiling the Neural Network
After defining the architecture of our neural network, we need to compile it. This involves specifying the loss function, optimizer, and metrics.
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.05), metrics=['accuracy'])
In the above code for neural network with keras, we specify binary cross-entropy as our loss function, Adam as our optimizer, and accuracy as our metric.
4. Training the Neural Network
Now that our neural network is defined and compiled, we can train it using our dataset. For this tutorial, we will be generating a random dataset.
X = np.random.rand(4000, 2) y = (X[:,0] > 0.5) & (X[:,1] > 0.5)
In the above code, we generate a random dataset of 4000 examples, each with two features. We then label each example as 1 if both features are greater than 0.5, and 0 otherwise.
We can now train our neural network using the fit() method.
model.fit(X, y, epochs=20, batch_size=32)
In the above code, we train our neural network for 20 epochs with a batch size of 32.
5. Evaluating the Neural Network
After training our neural network with keras, we can evaluate its performance using the evaluate() method.
loss, accuracy = model.evaluate(X, y) print('Loss: ', loss) print('Accuracy: ', accuracy)
In the above code, we print out the loss and accuracy of our neural network.
In this tutorial, we saw how easy it is to build a neural network using Keras and Python. We learned what Keras is and how it is used to build and train deep learning models. We also learned about the architecture of neural networks, compiling, training and evaluating a model. With this knowledge, you can start building your own neural networks and apply them to various applications.
Want to learn more about Python, checkout the Python Official Documentation for detail.