def setUp(self):
super(DataAdapterTestBase, self).setUp()
self.batch_size = 5
self.numpy_input = np.zeros((50, 10))
self.numpy_target = np.ones(50)
self.tensor_input = tf.constant(2.0, shape=(50, 10))
self.tensor_target = tf.ones((50, ))
self.arraylike_input = DummyArrayLike(self.numpy_input)
self.arraylike_target = DummyArrayLike(self.numpy_target)
self.dataset_input = tf.data.Dataset.from_tensor_slices(
(self.numpy_input,
self.numpy_target)).shuffle(50).batch(self.batch_size)
def generator():
while True:
yield (np.zeros(
(self.batch_size, 10)), np.ones(self.batch_size))
self.generator_input = generator()
self.iterator_input = data_utils.threadsafe_generator(generator)()
self.sequence_input = TestSequence(batch_size=self.batch_size,
feature_shape=10)
self.text_input = [['abc']]
self.bytes_input = [[b'abc']]
self.model = keras.models.Sequential(
[keras.layers.Dense(8, input_shape=(10, ), activation='softmax')])
batch_index = i * batch_size % num_samples
i += 1
start = batch_index
end = start + cur_batch_size
x = arr_data[start:end]
y = arr_labels[start:end]
w = arr_weights[start:end]
if mode == 1:
yield x
elif mode == 2:
yield x, y
else:
yield x, y, w
custom_generator_threads = data_utils.threadsafe_generator(custom_generator)
class TestGeneratorMethods(keras_parameterized.TestCase):
@keras_parameterized.run_with_all_model_types
@keras_parameterized.run_all_keras_modes
@data_utils.dont_use_multiprocessing_pool
def test_fit_generator_method(self):
model = testing_utils.get_small_mlp(num_hidden=3,
num_classes=4,
input_dim=2)
model.compile(loss='mse',
optimizer=rmsprop.RMSprop(1e-3),
metrics=['mae',
metrics_module.CategoricalAccuracy()])