Sequential models in Keras are the most common type of model used for building neural networks. They are straightforward to use and highly flexible. In this article, we will discuss the basics of sequential models in Keras.
What is a Sequential Model?
A sequential model in Keras is a linear stack of layers. You can add layers to a sequential model using the add() method. The layers are added in the order they are called, which is also the order in which they are executed during the forward pass.
Basic Structure
Here is a basic structure of a sequential model:
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))
In this example, we have a model with two layers. The first layer is a dense layer with 64 units and a ReLU activation function. The second layer is a dense layer with 10 units and a softmax activation function.
Adding Layers
You can add layers to a sequential model using the add() method. Here's an example of adding more layers:
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=32, activation='relu'))
model.add(Dense(units=10, activation='softmax'))
Compiling the Model
After adding layers to your model, you need to compile it. Compilation involves specifying the optimizer, loss function, and metrics:
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
Training the Model
Once the model is compiled, you can train it using the fit() method:
model.fit(x_train, y_train, epochs=10, batch_size=32)
Summary
A sequential model in Keras is a simple and powerful way to build neural networks. It allows you to stack layers in a linear fashion and is highly flexible. For more information on building neural networks with Keras, check out our Keras Tutorial.
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