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

Creating a sequential model using Python and Keras is relatively straightforward. To begin, import the necessary libraries:

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

Next, create a Sequential model object:

model = Sequential()

The model can then be built by adding layers one at a time. For example, adding a Dense layer with 10 neurons and an input shape of 8:

model.add(Dense(10, input_shape=(8,)))

More layers can be added in a similar fashion. For example, adding a second Dense layer with 6 neurons:

model.add(Dense(6))

Once the model is built, it can be compiled using the .compile() method:

model.compile(optimizer='adam', loss='mse')

The model can then be fit using the .fit() method:

model.fit(X, y, epochs=20)

The model can then be evaluated using the .evaluate() method:

model.evaluate(X, y)

Code explanation

1. Importing Libraries: import keras, from keras.models import Sequential, from keras.layers import Dense
2. Creating a Sequential Model Object: model = Sequential()
3. Adding Layers: model.add(Dense(10, input_shape=(8,))), model.add(Dense(6))
4. Compiling the Model: model.compile(optimizer='adam', loss='mse')
5. Fitting the Model: model.fit(X, y, epochs=20)
6. Evaluating the Model: model.evaluate(X, y)

Helpful links

• Keras Documentation
• Getting Started with the Keras Sequential Model

Edit this code on GitHub