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

To evaluate a model using Python and Keras, the following steps should be taken:

1. Compile the model using the compile() method. This should include a loss function, an optimizer, and any metrics that should be included in the evaluation.

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

2. Fit the model to the training data using the fit() method. This will train the model on the training data.

model.fit(X_train, y_train, batch_size=32, epochs=10, verbose=1)

3. Evaluate the model on the test data using the evaluate() method. This will return the loss and any metrics specified in the compile() step.

loss, accuracy = model.evaluate(X_test, y_test, verbose=0)

4. Use the predict() method to generate predictions on new data.

predictions = model.predict(X_new)

For more information, see the Keras documentation.

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