def testResidualWrapperWithSlice(self):
wrapper_type = cell_wrappers.ResidualWrapper
x = tf.convert_to_tensor(
np.array([[1.0, 1.0, 1.0, 1.0, 1.0]]), dtype="float32"
)
m = tf.convert_to_tensor(np.array([[0.1, 0.1, 0.1]]), dtype="float32")
base_cell = legacy_cells.GRUCell(
3,
kernel_initializer=tf.compat.v1.constant_initializer(0.5),
bias_initializer=tf.compat.v1.constant_initializer(0.5),
)
g, m_new = base_cell(x, m)
def residual_with_slice_fn(inp, out):
inp_sliced = tf.slice(inp, [0, 0], [-1, 3])
return inp_sliced + out
g_res, m_new_res = wrapper_type(base_cell, residual_with_slice_fn)(x, m)
self.evaluate([tf.compat.v1.global_variables_initializer()])
res_g, res_g_res, res_m_new, res_m_new_res = self.evaluate(
[g, g_res, m_new, m_new_res]
)
# Residual connections
self.assertAllClose(res_g_res, res_g + [1.0, 1.0, 1.0])
# States are left untouched
self.assertAllClose(res_m_new, res_m_new_res)
def testDeviceWrapper(self):
wrapper_type = cell_wrappers.DeviceWrapper
x = tf.zeros([1, 3])
m = tf.zeros([1, 3])
cell = legacy_cells.GRUCell(3)
wrapped_cell = wrapper_type(cell, "/cpu:0")
self.assertDictEqual({"cell": cell}, wrapped_cell._trackable_children())
wrapped_cell.get_config() # Should not throw an error
outputs, _ = wrapped_cell(x, m)
self.assertIn("cpu:0", outputs.device.lower())
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 testResidualWrapper(self):
wrapper_type = cell_wrappers.ResidualWrapper
x = tf.convert_to_tensor(np.array([[1., 1., 1.]]), dtype="float32")
m = tf.convert_to_tensor(np.array([[0.1, 0.1, 0.1]]), dtype="float32")
base_cell = legacy_cells.GRUCell(
3,
kernel_initializer=tf.compat.v1.constant_initializer(0.5),
bias_initializer=tf.compat.v1.constant_initializer(0.5))
g, m_new = base_cell(x, m)
wrapper_object = wrapper_type(base_cell)
self.assertDictEqual({"cell": base_cell},
wrapper_object._trackable_children())
wrapper_object.get_config() # Should not throw an error
g_res, m_new_res = wrapper_object(x, m)
self.evaluate([tf.compat.v1.global_variables_initializer()])
res = self.evaluate([g, g_res, m_new, m_new_res])
# Residual connections
self.assertAllClose(res[1], res[0] + [1., 1., 1.])
# States are left untouched
self.assertAllClose(res[2], res[3])