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(),
experimental_run_tf_function=testing_utils.should_run_tf_function())
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 testWrapperV2Caller(self, wrapper):
"""Tests that wrapper V2 is using the LayerRNNCell's caller."""
with base_layer.keras_style_scope():
base_cell = rnn_cell_impl.MultiRNNCell(
[rnn_cell_impl.BasicRNNCell(1) for _ in range(2)])
rnn_cell = wrapper(base_cell)
inputs = ops.convert_to_tensor([[1]], dtype=dtypes.float32)
state = ops.convert_to_tensor([[1]], dtype=dtypes.float32)
_ = rnn_cell(inputs, [state, state])
weights = base_cell._cells[0].weights
self.assertLen(weights, expected_len=2)
self.assertTrue(all(["_wrapper" in v.name for v in weights]))
def _rnn_input(apply_wrapper):
"""Creates a RNN layer with/without wrapper and returns built rnn cell."""
with base_layer.keras_style_scope():
base_cell = rnn_cell_impl.MultiRNNCell(
[rnn_cell_impl.BasicRNNCell(1) for _ in range(2)])
if apply_wrapper:
rnn_cell = rnn_cell_impl.DropoutWrapperV2(base_cell)
else:
rnn_cell = base_cell
rnn_layer = keras_layers.RNN(rnn_cell)
inputs = ops.convert_to_tensor([[[1]]], dtype=dtypes.float32)
_ = rnn_layer(inputs)
return base_cell._cells[0]
def testRNNCellActsLikeKerasRNNCellInProperScope(self):
with base_layers.keras_style_scope():
kn1 = KerasNetworkTFRNNs(name="kn1")
kn2 = KerasNetworkKerasRNNs(name="kn2")
z = array_ops.zeros((2, 3))
kn1(z)
kn2(z)
# pylint: disable=protected-access
self.assertTrue(all("kn1" in v.name for v in kn1._cell.variables))
self.assertTrue(all("kn2" in v.name for v in kn2._cell.variables))
with base_layers.keras_style_scope():
kn1_new = KerasNetworkTFRNNs(name="kn1_new")
kn2_new = KerasNetworkKerasRNNs(name="kn2_new")
kn2_new(z)
# Most importantly, this doesn't fail due to variable scope reuse issues.
kn1_new(z)
self.assertTrue(all("kn1_new" in v.name for v in kn1_new._cell.variables))
self.assertTrue(all("kn2_new" in v.name for v in kn2_new._cell.variables))
def testRNNCellActsLikeKerasRNNCellInProperScope(self):
with base_layers.keras_style_scope():
kn1 = KerasNetworkTFRNNs(name="kn1")
kn2 = KerasNetworkKerasRNNs(name="kn2")
z = array_ops.zeros((2, 3))
kn1(z)
kn2(z)
# pylint: disable=protected-access
self.assertTrue(all("kn1" in v.name for v in kn1._cell.variables))
self.assertTrue(all("kn2" in v.name for v in kn2._cell.variables))
with base_layers.keras_style_scope():
kn1_new = KerasNetworkTFRNNs(name="kn1_new")
kn2_new = KerasNetworkKerasRNNs(name="kn2_new")
kn2_new(z)
# Most importantly, this doesn't fail due to variable scope reuse issues.
kn1_new(z)
self.assertTrue(all("kn1_new" in v.name for v in kn1_new._cell.variables))
self.assertTrue(all("kn2_new" in v.name for v in kn2_new._cell.variables))
def testKerasStyleAddWeight(self):
keras_layer = keras_base_layer.Layer(name='keras_layer')
with ops.name_scope('foo', skip_on_eager=False):
keras_variable = keras_layer.add_variable(
'my_var', [2, 2], initializer=init_ops.zeros_initializer())
self.assertEqual(keras_variable.name, 'foo/my_var:0')
with ops.name_scope('baz', skip_on_eager=False):
old_style_layer = base_layers.Layer(name='my_layer')
# Test basic variable creation.
variable = old_style_layer.add_variable(
'my_var', [2, 2], initializer=init_ops.zeros_initializer())
self.assertEqual(variable.name, 'my_layer/my_var:0')
with base_layers.keras_style_scope():
layer = base_layers.Layer(name='my_layer')
# Test basic variable creation.
with ops.name_scope('bar', skip_on_eager=False):
variable = layer.add_variable(
'my_var', [2, 2], initializer=init_ops.zeros_initializer())
self.assertEqual(variable.name, 'bar/my_var:0')
def testKerasStyleAddWeight(self):
keras_layer = keras_base_layer.Layer(name='keras_layer')
with ops.name_scope('foo'):
keras_variable = keras_layer.add_variable(
'my_var', [2, 2], initializer=init_ops.zeros_initializer())
self.assertEqual(keras_variable.name, 'foo/my_var:0')
with ops.name_scope('baz'):
old_style_layer = base_layers.Layer(name='my_layer')
# Test basic variable creation.
variable = old_style_layer.add_variable(
'my_var', [2, 2], initializer=init_ops.zeros_initializer())
self.assertEqual(variable.name, 'my_layer/my_var:0')
with base_layers.keras_style_scope():
layer = base_layers.Layer(name='my_layer')
# Test basic variable creation.
with ops.name_scope('bar'):
variable = layer.add_variable(
'my_var', [2, 2], initializer=init_ops.zeros_initializer())
self.assertEqual(variable.name, 'bar/my_var:0')
