Introduction to Sequential Model in Keras: A Beginner's Guide

 In Keras, the Sequential model is a linear stack of layers that are executed in order, which is one of the simplest and most commonly used models for deep learning. It is a user-friendly interface that allows for easy construction of neural networks with just a few lines of code.

The Sequential model in Keras can be initialized by creating an instance of the Sequential class. Once the model is created, you can add layers to it using the add() method. Each layer is added one after the other in the order that they are added, and the output of one layer is passed as input to the next layer.

Here's an example of how to create a Sequential model with two dense layers:

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

model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(784,)))
model.add(Dense(10, activation='softmax'))

In this example, the first layer is a dense layer with 64 units and a ReLU activation function. The input shape for this layer is (784,), which corresponds to a flattened input image of 28x28 pixels. The second layer is a dense layer with 10 units and a softmax activation function, which outputs a probability distribution over the 10 possible classes.

Once the layers are added to the Sequential model, you can compile it by specifying the loss function, optimizer, and evaluation metric. The model can then be trained on a dataset using the fit() method.

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

model.fit(x_train, y_train,
          batch_size=128,
          epochs=20,
          validation_data=(x_test, y_test))

In this example, the model is compiled with a categorical cross-entropy loss function, the Adam optimizer, and accuracy as the evaluation metric. The model is then trained on the training dataset (x_train, y_train) with a batch size of 128 for 20 epochs. The validation data is specified using the validation_data parameter, which is used to evaluate the performance of the model on a separate validation dataset (x_test, y_test).

Overall, the Sequential model in Keras provides a simple and intuitive interface for constructing and training deep neural networks, making it a popular choice for beginners and experts alike.

Keywords: Sequential Model, Keras, Linear stack of layers, deep learning, dense layer, activation function, ReLU, softmax, compile model, categorical cross-entropy, optimizer, accuracy, fit model, validation dataset, beginner's guide.