def _benchmark_keras_model_evaluate(self, model):
    data = random_ops.random_uniform((10, 10), minval=(- 1), maxval=1)
    labels = random_ops.random_uniform((10, 10), minval=(- 1), maxval=1)
    dataset = dataset_ops.Dataset.from_tensors((data, labels)).repeat()
    model.compile(gradient_descent.GradientDescentOptimizer(learning_rate=0.001), loss='mse')
    func = (lambda : model.evaluate(dataset, steps=1000, verbose=0))
    model.evaluate(dataset, steps=1, verbose=0)
    self._run(func, 1)
def _benchmark_keras_model_evaluate(self, model):
    data = random_ops.random_uniform((10, 10), minval=(- 1), maxval=1)
    labels = random_ops.random_uniform((10, 10), minval=(- 1), maxval=1)
    dataset = dataset_ops.Dataset.from_tensors((data, labels)).repeat()
    model.compile(gradient_descent.GradientDescentOptimizer(learning_rate=0.001), loss='mse')
    func = (lambda : model.evaluate(dataset, steps=1000, verbose=0))
    model.evaluate(dataset, steps=1, verbose=0)
    self._run(func, 1)