Italian Keras Tutorial: Part 1 (Dense and Sequential)

In this tutorial, we are going to learn how to use Keras, a powerful and easy-to-use deep learning library, to build a simple neural network model using the Dense and Sequential layers. The Keras library has quickly become one of the most popular tools for building and training deep learning models, due to its simplicity and flexibility.

First, we need to install Keras along with its backend, which can be either TensorFlow or Theano. To install Keras with TensorFlow backend, you can use the following command:

pip install tensorflow keras

Once Keras is installed, we can start building our neural network model. In this tutorial, we are going to create a simple model that has one input layer, one hidden layer, and one output layer. We will use the Dense layer for all layers and the Sequential model to define the architecture of our model.

Here is the code to build the model:

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

# define the model
model = Sequential()
model.add(Dense(units=32, activation='relu', input_dim=10)) # input layer
model.add(Dense(units=64, activation='relu')) # hidden layer
model.add(Dense(units=1, activation='sigmoid')) # output layer

In this code snippet, we first import the Sequential and Dense layers from Keras. We then create a Sequential model using the Sequential() function. Next, we add three Dense layers to the model using the add() method. The units parameter specifies the number of neurons in the layer, the activation parameter specifies the activation function for the layer, and the input_dim parameter is used only for the input layer to specify the input shape.

After defining the model, we need to compile it before training. To compile the model, we need to specify the loss function, the optimizer, and any metrics to monitor during training. Here is the code to compile the model:

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

In this code snippet, we use binary cross-entropy as the loss function since this is a binary classification problem. We use the Adam optimizer, one of the most commonly used optimizers in deep learning, and we monitor the accuracy metric during training.

Now, we can train the model using our data. To train the model, we need to provide the input data and the corresponding labels. Here is the code to train the model:

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

In this code snippet, X_train is the input data, y_train is the corresponding labels, epochs specifies the number of training epochs, and batch_size specifies the number of samples per gradient update. The fit() method trains the model on the input data for the specified number of epochs using the specified batch size.

After training the model, we can evaluate its performance on a separate test dataset. Here is the code to evaluate the model:

loss, accuracy = model.evaluate(X_test, y_test)
print('Test accuracy:', accuracy)

In this code snippet, X_test is the test input data, y_test is the corresponding test labels, and the evaluate() method calculates the loss and accuracy of the model on the test dataset.

And that’s it! In this tutorial, we have learned how to build a simple neural network model using the Dense and Sequential layers in Keras. We have also seen how to compile, train, and evaluate the model. Keras provides a simple and flexible interface for building deep learning models, making it an excellent choice for beginners and experts alike.