Complete Keras Tutorial for Beginners: Simplilearn’s Full Course

Keras Tutorial: A Comprehensive Guide for Beginners

In this tutorial, we will dive deep into the world of Keras, a popular deep learning library that allows you to build and train deep learning models with ease. This tutorial is designed for beginners who have little to no experience with deep learning or neural networks.

What is Keras?

Keras is an open-source deep learning library written in Python that allows you to build and train deep learning models quickly and easily. Keras is built on top of other deep learning libraries such as TensorFlow, Theano, and Microsoft Cognitive Toolkit, which allows you to take advantage of the power and efficiency of these underlying tools.

Keras provides a simple and intuitive interface for building deep learning models, making it ideal for beginners and experts alike. Whether you are a seasoned deep learning practitioner or just getting started with neural networks, Keras can help you create sophisticated and highly accurate models with minimal effort.

Installing Keras

Before we can start building deep learning models with Keras, we need to install the library on our system. Keras can easily be installed using the Python package manager pip. To install Keras, run the following command in your terminal or command prompt:

pip install keras

Once Keras is installed, we can begin creating our first deep learning model.

Building a Simple Neural Network with Keras

To demonstrate the ease of building neural networks with Keras, we will create a simple feedforward neural network using the Sequential model class. This model will have one input layer, one hidden layer, and one output layer.

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()

model.add(Dense(units=32, activation='relu', input_shape=(784,)))
model.add(Dense(units=10, activation='softmax'))

model.summary()

In this code snippet, we first import the Sequential class from the Keras models module and the Dense class from the Keras layers module. We then create a Sequential model object and add two Dense layers to it. The first Dense layer has 32 units, uses the ReLU activation function, and takes an input shape of (784,). The second Dense layer has 10 units and uses the softmax activation function.

We then call the summary() method on the model to display a summary of the model architecture, including the number of parameters in each layer.

Compiling and Training the Model

After building the model, we need to compile it by specifying the loss function, optimizer, and metrics to use during training. We can then train the model by providing the input data and target labels.

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

model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))

In this code snippet, we compile the model using the categorical_crossentropy loss function, the Adam optimizer, and the accuracy metric. We then call the fit() method on the model to train it using the training data X_train and target labels y_train for 10 epochs with a batch size of 32. We also specify the validation data to evaluate the model performance on the test data X_test and y_test after each epoch.

Evaluating the Model

Once the model has been trained, we can evaluate its performance on unseen data by calling the evaluate() method.

loss, accuracy = model.evaluate(X_test, y_test)

print(f'Loss: {loss}')
print(f'Accuracy: {accuracy}')

In this code snippet, we evaluate the model on the test data X_test and y_test and store the loss and accuracy values in the variables loss and accuracy, respectively. We then print the loss and accuracy values to the console.

Conclusion

In this tutorial, we have covered the basics of building, compiling, training, and evaluating deep learning models with Keras. We have shown how easy it is to create a simple neural network using Keras’s high-level API and how to train and evaluate the model using training and test data.

Keras is a powerful and flexible deep learning library that can help beginners and experts alike build highly accurate and sophisticated deep learning models with minimal effort. Whether you are just starting out with deep learning or looking to enhance your existing skills, Keras is a valuable tool to have in your toolkit.

I hope you found this tutorial helpful and informative. Thank you for reading!