@combinations.generate(strategy_combinations())
def test_fit_eval_and_predict_methods_on_dataset(self, distribution):
    with self.cached_session():
        model = get_model()
        optimizer = gradient_descent.GradientDescentOptimizer(0.001)
        loss = 'mse'
        metrics = ['mae', keras.metrics.CategoricalAccuracy()]
        model.compile(optimizer, loss, metrics=metrics, distribute=distribution)
        dataset = get_dataset(distribution)
        model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=1)
        model.evaluate(dataset, steps=2, verbose=1)
        model.predict(get_predict_dataset(distribution), steps=2)
@combinations.generate(strategy_combinations())
def test_fit_eval_and_predict_methods_on_dataset(self, distribution):
    with self.cached_session():
        model = get_model()
        optimizer = gradient_descent.GradientDescentOptimizer(0.001)
        loss = 'mse'
        metrics = ['mae', keras.metrics.CategoricalAccuracy()]
        model.compile(optimizer, loss, metrics=metrics, distribute=distribution)
        dataset = get_dataset(distribution)
        model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=1)
        model.evaluate(dataset, steps=2, verbose=1)
        model.predict(get_predict_dataset(distribution), steps=2)