python-kerasHow do I use Python Keras to predict classes?

Keras is a deep learning library written in Python. It provides a high-level API for building and training neural networks. To use Keras to predict classes, you first need to define a model. The model should include the input layer, output layer, and any hidden layers you want to include. Then you need to compile the model with a loss function and optimizer. After that, you can train the model on a dataset. Finally, you can use the model to make predictions on new data.

Example code

# define model
model = Sequential()
model.add(Dense(32, input_dim=16))
model.add(Activation('relu'))
model.add(Dense(2))
model.add(Activation('softmax'))

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

# train model
model.fit(X_train, y_train, epochs=10, batch_size=32)

# predict classes
predictions = model.predict_classes(X_test)

Code explanation

1. Define the model: This involves creating an instance of the Sequential class and adding layers to it. The input layer should have a dimension equal to the number of features in the dataset. The output layer should have a dimension equal to the number of classes.
2. Compile the model: This involves configuring the model for training by specifying a loss function, an optimizer, and any metrics you want to track.
3. Train the model: This involves feeding the training data to the model. You can also specify the number of epochs and the batch size.
4. Predict classes: This involves using the model to make predictions on new data.

Helpful links

• Keras Documentation
• Getting Started with Keras

Edit this code on GitHub