def run_eval_metrics_correctness_test(self, distribution):
with self.cached_session():
self.set_up_test_config()
model = self.get_model(distribution=distribution)
# verify correctness of stateful and stateless metrics.
x = np.ones((100, 4)).astype('float32')
y = np.ones((100, 1)).astype('float32')
dataset = tf.data.Dataset.from_tensor_slices((x, y)).repeat()
dataset = keras_correctness_test_base.batch_wrapper(dataset, 4)
outs = model.evaluate(dataset, steps=10)
self.assertEqual(outs[1], 1.)
self.assertEqual(outs[2], 1.)
y = np.zeros((100, 1)).astype('float32')
dataset = tf.data.Dataset.from_tensor_slices((x, y)).repeat()
dataset = keras_correctness_test_base.batch_wrapper(dataset, 4)
outs = model.evaluate(dataset, steps=10)
self.assertEqual(outs[1], 0.)
self.assertEqual(outs[2], 0.)
def run_metric_correctness_test(self, distribution):
with self.cached_session():
self.set_up_test_config()
x_train, y_train, _ = self.get_data()
model = self.get_model(distribution=distribution)
batch_size = 64
batch_size = (keras_correctness_test_base.get_batch_size(
batch_size, distribution))
train_dataset = tf.data.Dataset.from_tensor_slices(
(x_train, y_train))
train_dataset = (keras_correctness_test_base.batch_wrapper(
train_dataset, batch_size))
history = model.fit(x=train_dataset, epochs=2, steps_per_epoch=10)
self.assertEqual(history.history['binary_accuracy'], [1.0, 1.0])