python-kerasHow do I create a sequential model with Python and Keras?

Creating a sequential model with Python and Keras is an easy task. A sequential model is a linear stack of layers.

To create a sequential model with Python and Keras, you need to import the Sequential class from Keras. Then you can add layers to the model by calling the add() method.

from keras.models import Sequential

model = Sequential()
model.add(Dense(32, activation='relu', input_dim=100))
model.add(Dense(1, activation='sigmoid'))

The above code creates a sequential model with two layers. The first layer has 32 neurons, an relu activation function, and an input dimension of 100. The second layer has 1 neuron and a sigmoid activation function.

Once you have constructed the model, you need to compile it with an appropriate loss function and optimizer.

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

The above code compiles the model with an rmsprop optimizer, a binary_crossentropy loss function, and an accuracy metric.

Finally, you can then train the model with the fit() method.

model.fit(data, labels, epochs=10, batch_size=32)

The above code trains the model for 10 epochs with a batch size of 32.

Helpful links

• Keras Documentation - Sequential Model
• Keras Documentation - Compiling a Model
• Keras Documentation - Training a Model

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