def test_timeseries_classification_sequential_tf_rnn(self):
np.random.seed(1337)
(x_train, y_train), _ = test_utils.get_test_data(
train_samples=100,
test_samples=0,
input_shape=(4, 10),
num_classes=2,
)
y_train = utils.to_categorical(y_train)
with base_layer.keras_style_scope():
model = keras.models.Sequential()
model.add(
keras.layers.RNN(
legacy_cells.LSTMCell(5),
return_sequences=True,
input_shape=x_train.shape[1:],
)
)
model.add(
keras.layers.RNN(
legacy_cells.GRUCell(
y_train.shape[-1],
activation="softmax",
dtype=tf.float32,
)
)
)
model.compile(
loss="categorical_crossentropy",
optimizer=keras.optimizers.optimizer_v2.adam.Adam(0.005),
metrics=["acc"],
run_eagerly=test_utils.should_run_eagerly(),
)
history = model.fit(
x_train,
y_train,
epochs=15,
batch_size=10,
validation_data=(x_train, y_train),
verbose=2,
)
self.assertGreater(history.history["val_acc"][-1], 0.7)
_, val_acc = model.evaluate(x_train, y_train)
self.assertAlmostEqual(history.history["val_acc"][-1], val_acc)
predictions = model.predict(x_train)
self.assertEqual(predictions.shape, (x_train.shape[0], 2))
def testWrapperV2Build(self, wrapper):
cell = legacy_cells.LSTMCell(10)
wrapper = wrapper(cell)
wrapper.build((1,))
self.assertTrue(cell.built)