@combinations.generate(strategy_combinations())
def test_calling_model_with_numpy_arrays(self, distribution):
    with self.cached_session():
        model = get_model()
        optimizer = gradient_descent.GradientDescentOptimizer(0.001)
        loss = 'mse'
        metrics = ['mae']
        model.compile(optimizer, loss, metrics=metrics, distribute=distribution)
        inputs = np.zeros((64, 3), dtype=np.float32)
        targets = np.zeros((64, 4), dtype=np.float32)
        model.fit(inputs, targets, epochs=1, batch_size=2, verbose=0, validation_data=(inputs, targets))
        model.evaluate(inputs, targets)
        model.evaluate(inputs, targets, steps=2)
        model.evaluate(inputs, targets, batch_size=8)
        model.predict(inputs)
        model.predict(inputs, steps=2)
        model.predict(inputs, batch_size=8)
@combinations.generate(strategy_combinations())
def test_calling_model_with_numpy_arrays(self, distribution):
    with self.cached_session():
        model = get_model()
        optimizer = gradient_descent.GradientDescentOptimizer(0.001)
        loss = 'mse'
        metrics = ['mae']
        model.compile(optimizer, loss, metrics=metrics, distribute=distribution)
        inputs = np.zeros((64, 3), dtype=np.float32)
        targets = np.zeros((64, 4), dtype=np.float32)
        model.fit(inputs, targets, epochs=1, batch_size=2, verbose=0, validation_data=(inputs, targets))
        model.evaluate(inputs, targets)
        model.evaluate(inputs, targets, steps=2)
        model.evaluate(inputs, targets, batch_size=8)
        model.predict(inputs)
        model.predict(inputs, steps=2)
        model.predict(inputs, batch_size=8)