def testResidualWrapperWithSlice(self):
wrapper_type = rnn_cell_wrapper_v2.ResidualWrapper
x = ops.convert_to_tensor_v2_with_dispatch(np.array(
[[1., 1., 1., 1., 1.]]),
dtype="float32")
m = ops.convert_to_tensor_v2_with_dispatch(np.array([[0.1, 0.1, 0.1]]),
dtype="float32")
base_cell = rnn_cell_impl.GRUCell(
3,
kernel_initializer=init_ops.constant_initializer(0.5),
bias_initializer=init_ops.constant_initializer(0.5))
g, m_new = base_cell(x, m)
def residual_with_slice_fn(inp, out):
inp_sliced = array_ops.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([variables_lib.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., 1., 1.])
# States are left untouched
self.assertAllClose(res_m_new, res_m_new_res)
def testDeviceWrapper(self):
wrapper_type = rnn_cell_wrapper_v2.DeviceWrapper
x = array_ops.zeros([1, 3])
m = array_ops.zeros([1, 3])
cell = rnn_cell_impl.GRUCell(3)
wrapped_cell = wrapper_type(cell, "/cpu:0")
(name, dep), = wrapped_cell._checkpoint_dependencies
wrapped_cell.get_config() # Should not throw an error
self.assertIs(dep, cell)
self.assertEqual("cell", name)
outputs, _ = wrapped_cell(x, m)
self.assertIn("cpu:0", outputs.device.lower())
def testDeviceWrapper(self):
wrapper_type = rnn_cell_wrapper_v2.DeviceWrapper
x = array_ops.zeros([1, 3])
m = array_ops.zeros([1, 3])
cell = rnn_cell_impl.GRUCell(3)
wrapped_cell = wrapper_type(cell, "/cpu:0")
children = wrapped_cell._trackable_children()
wrapped_cell.get_config() # Should not throw an error
self.assertIn("cell", children)
self.assertIs(children["cell"], cell)
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), _ = testing_utils.get_test_data(train_samples=100,
test_samples=0,
input_shape=(4,
10),
num_classes=2)
y_train = np_utils.to_categorical(y_train)
with base_layer.keras_style_scope():
model = keras.models.Sequential()
model.add(
keras.layers.RNN(rnn_cell.LSTMCell(5),
return_sequences=True,
input_shape=x_train.shape[1:]))
model.add(
keras.layers.RNN(
rnn_cell.GRUCell(y_train.shape[-1],
activation='softmax',
dtype=dtypes.float32)))
model.compile(loss='categorical_crossentropy',
optimizer=keras.optimizer_v2.adam.Adam(0.005),
metrics=['acc'],
run_eagerly=testing_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 = rnn_cell_wrapper_v2.ResidualWrapper
x = ops.convert_to_tensor_v2_with_dispatch(np.array([[1., 1., 1.]]),
dtype="float32")
m = ops.convert_to_tensor_v2_with_dispatch(np.array([[0.1, 0.1, 0.1]]),
dtype="float32")
base_cell = rnn_cell_impl.GRUCell(
3,
kernel_initializer=init_ops.constant_initializer(0.5),
bias_initializer=init_ops.constant_initializer(0.5))
g, m_new = base_cell(x, m)
wrapper_object = wrapper_type(base_cell)
(name, dep), = wrapper_object._checkpoint_dependencies
wrapper_object.get_config() # Should not throw an error
self.assertIs(dep, base_cell)
self.assertEqual("cell", name)
g_res, m_new_res = wrapper_object(x, m)
self.evaluate([variables_lib.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])