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

Creating a sequential model in Python using Keras is a simple process.

First, you must import the necessary Keras libraries:

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

Next, you must create an instance of the Sequential model:

model = Sequential()

You can then add layers to the model:

model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))

You must then compile the model, specifying the optimizer and loss function:

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

Finally, you can train the model:

model.fit(x_train, y_train, epochs=5, batch_size=32)

Code explanation

1. from keras.models import Sequential - This imports the Sequential model from the Keras library.
2. from keras.layers import Dense - This imports the Dense layer from the Keras library.
3. model = Sequential() - This creates an instance of the Sequential model.
4. model.add(Dense(units=64, activation='relu', input_dim=100)) - This adds a Dense layer with 64 units, a ReLU activation function, and an input dimension of 100.
5. model.add(Dense(units=10, activation='softmax')) - This adds a Dense layer with 10 units and a Softmax activation function.
6. model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) - This compiles the model, specifying the loss function, optimizer, and metrics.
7. model.fit(x_train, y_train, epochs=5, batch_size=32) - This trains the model on the training data for 5 epochs with a batch size of 32.

Helpful links

• Keras Documentation
• Keras Sequential Model Guide

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