class MergeLayersTest(keras_parameterized.TestCase):
def test_merge_add(self):
i1 = keras.layers.Input(shape=(4, 5))
i2 = keras.layers.Input(shape=(4, 5))
i3 = keras.layers.Input(shape=(4, 5))
add_layer = keras.layers.Add()
o = add_layer([i1, i2, i3])
self.assertListEqual(o.shape.as_list(), [None, 4, 5])
model = keras.models.Model([i1, i2, i3], o)
model.run_eagerly = testing_utils.should_run_eagerly()
x1 = np.random.random((2, 4, 5))
x2 = np.random.random((2, 4, 5))
x3 = np.random.random((2, 4, 5))
out = model.predict([x1, x2, x3])
self.assertEqual(out.shape, (2, 4, 5))
self.assertAllClose(out, x1 + x2 + x3, atol=1e-4)
self.assertEqual(
add_layer.compute_mask([i1, i2, i3], [None, None, None]), None)
self.assertTrue(
np.all(
K.eval(
add_layer.compute_mask(
[i1, i2],
[K.variable(x1), K.variable(x2)]))))
with self.assertRaisesRegex(ValueError, '`mask` should be a list.'):
add_layer.compute_mask([i1, i2, i3], x1)
with self.assertRaisesRegex(ValueError, '`inputs` should be a list.'):
add_layer.compute_mask(i1, [None, None, None])
with self.assertRaisesRegex(ValueError,
' should have the same length.'):
add_layer.compute_mask([i1, i2, i3], [None, None])
def test_merge_subtract(self):
i1 = keras.layers.Input(shape=(4, 5))
i2 = keras.layers.Input(shape=(4, 5))
i3 = keras.layers.Input(shape=(4, 5))
subtract_layer = keras.layers.Subtract()
o = subtract_layer([i1, i2])
self.assertListEqual(o.shape.as_list(), [None, 4, 5])
model = keras.models.Model([i1, i2], o)
model.run_eagerly = testing_utils.should_run_eagerly()
x1 = np.random.random((2, 4, 5))
x2 = np.random.random((2, 4, 5))
out = model.predict([x1, x2])
self.assertEqual(out.shape, (2, 4, 5))
self.assertAllClose(out, x1 - x2, atol=1e-4)
self.assertEqual(subtract_layer.compute_mask([i1, i2], [None, None]),
None)
self.assertTrue(
np.all(
K.eval(
subtract_layer.compute_mask(
[i1, i2],
[K.variable(x1), K.variable(x2)]))))
with self.assertRaisesRegex(ValueError, '`mask` should be a list.'):
subtract_layer.compute_mask([i1, i2], x1)
with self.assertRaisesRegex(ValueError, '`inputs` should be a list.'):
subtract_layer.compute_mask(i1, [None, None])
with self.assertRaisesRegex(
ValueError, 'layer should be called on exactly 2 inputs'):
subtract_layer([i1, i2, i3])
with self.assertRaisesRegex(
ValueError, 'layer should be called on exactly 2 inputs'):
subtract_layer([i1])
def test_merge_multiply(self):
i1 = keras.layers.Input(shape=(4, 5))
i2 = keras.layers.Input(shape=(4, 5))
i3 = keras.layers.Input(shape=(4, 5))
o = keras.layers.multiply([i1, i2, i3])
self.assertListEqual(o.shape.as_list(), [None, 4, 5])
model = keras.models.Model([i1, i2, i3], o)
model.run_eagerly = testing_utils.should_run_eagerly()
x1 = np.random.random((2, 4, 5))
x2 = np.random.random((2, 4, 5))
x3 = np.random.random((2, 4, 5))
out = model.predict([x1, x2, x3])
self.assertEqual(out.shape, (2, 4, 5))
self.assertAllClose(out, x1 * x2 * x3, atol=1e-4)
def test_merge_average(self):
i1 = keras.layers.Input(shape=(4, 5))
i2 = keras.layers.Input(shape=(4, 5))
o = keras.layers.average([i1, i2])
self.assertListEqual(o.shape.as_list(), [None, 4, 5])
model = keras.models.Model([i1, i2], o)
model.run_eagerly = testing_utils.should_run_eagerly()
x1 = np.random.random((2, 4, 5))
x2 = np.random.random((2, 4, 5))
out = model.predict([x1, x2])
self.assertEqual(out.shape, (2, 4, 5))
self.assertAllClose(out, 0.5 * (x1 + x2), atol=1e-4)
def test_merge_maximum(self):
i1 = keras.layers.Input(shape=(4, 5))
i2 = keras.layers.Input(shape=(4, 5))
o = keras.layers.maximum([i1, i2])
self.assertListEqual(o.shape.as_list(), [None, 4, 5])
model = keras.models.Model([i1, i2], o)
model.run_eagerly = testing_utils.should_run_eagerly()
x1 = np.random.random((2, 4, 5))
x2 = np.random.random((2, 4, 5))
out = model.predict([x1, x2])
self.assertEqual(out.shape, (2, 4, 5))
self.assertAllClose(out, np.maximum(x1, x2), atol=1e-4)
def test_merge_minimum(self):
i1 = keras.layers.Input(shape=(4, 5))
i2 = keras.layers.Input(shape=(4, 5))
o = keras.layers.minimum([i1, i2])
self.assertListEqual(o.shape.as_list(), [None, 4, 5])
model = keras.models.Model([i1, i2], o)
model.run_eagerly = testing_utils.should_run_eagerly()
x1 = np.random.random((2, 4, 5))
x2 = np.random.random((2, 4, 5))
out = model.predict([x1, x2])
self.assertEqual(out.shape, (2, 4, 5))
self.assertAllClose(out, np.minimum(x1, x2), atol=1e-4)
def test_merge_concatenate(self):
i1 = keras.layers.Input(shape=(4, 5))
i2 = keras.layers.Input(shape=(4, 5))
concat_layer = keras.layers.Concatenate(axis=1)
o = concat_layer([i1, i2])
self.assertListEqual(o.shape.as_list(), [None, 8, 5])
model = keras.models.Model([i1, i2], o)
model.run_eagerly = testing_utils.should_run_eagerly()
x1 = np.random.random((2, 4, 5))
x2 = np.random.random((2, 4, 5))
out = model.predict([x1, x2])
self.assertEqual(out.shape, (2, 8, 5))
self.assertAllClose(out, np.concatenate([x1, x2], axis=1), atol=1e-4)
self.assertEqual(concat_layer.compute_mask([i1, i2], [None, None]),
None)
self.assertTrue(
np.all(
K.eval(
concat_layer.compute_mask(
[i1, i2],
[K.variable(x1), K.variable(x2)]))))
# Should work with unit-length input.
unit_length_o = concat_layer([i1])
self.assertListEqual(unit_length_o.shape.as_list(), i1.shape.as_list())
with self.assertRaisesRegex(ValueError, '`mask` should be a list.'):
concat_layer.compute_mask([i1, i2], x1)
with self.assertRaisesRegex(ValueError, '`inputs` should be a list.'):
concat_layer.compute_mask(i1, [None, None])
with self.assertRaisesRegex(ValueError, 'should have the same length'):
concat_layer.compute_mask([i1, i2], [None])
with self.assertRaisesRegex(
ValueError, 'layer should be called on a list of inputs'):
concat_layer(i1)
def test_merge_dot(self):
i1 = keras.layers.Input(shape=(4, ))
i2 = keras.layers.Input(shape=(4, ))
o = keras.layers.dot([i1, i2], axes=1)
self.assertListEqual(o.shape.as_list(), [None, 1])
model = keras.models.Model([i1, i2], o)
model.run_eagerly = testing_utils.should_run_eagerly()
_ = keras.layers.Dot(axes=1).get_config()
x1 = np.random.random((2, 4))
x2 = np.random.random((2, 4))
out = model.predict([x1, x2])
self.assertEqual(out.shape, (2, 1))
expected = np.zeros((2, 1))
expected[0, 0] = np.dot(x1[0], x2[0])
expected[1, 0] = np.dot(x1[1], x2[1])
self.assertAllClose(out, expected, atol=1e-4)
# Test with negative tuple of axes.
o = keras.layers.dot([i1, i2], axes=(-1, -1))
self.assertListEqual(o.shape.as_list(), [None, 1])
model = keras.models.Model([i1, i2], o)
model.run_eagerly = testing_utils.should_run_eagerly()
out = model.predict([x1, x2])
self.assertEqual(out.shape, (2, 1))
self.assertAllClose(out, expected, atol=1e-4)
# test compute_output_shape
layer = keras.layers.Dot(axes=-1)
self.assertEqual(layer.compute_output_shape([(4, 5), (4, 5)]), (4, 1))
@parameterized.named_parameters(
*testing_utils.generate_combinations_with_testcase_name(layer=[
keras.layers.Add, keras.layers.Subtract, keras.layers.Multiply,
keras.layers.Minimum, keras.layers.Maximum, keras.layers.Average,
keras.layers.Concatenate
]))
def test_merge_with_ragged_input(self, layer):
ragged_data = ragged_factory_ops.constant(
[[1., 1., 1.], [1., 1.], [1., 1., 1., 1.]], ragged_rank=1)
dense_data = ragged_data.to_tensor()
input1 = keras.Input(shape=(None, ), ragged=True)
input2 = keras.Input(shape=(None, ), ragged=True)
out = keras.layers.Add()([input1, input2])
model = keras.models.Model(inputs=[input1, input2], outputs=out)
out_ragged = model.predict([ragged_data, ragged_data], steps=1)
out_ragged = ragged_tensor.convert_to_tensor_or_ragged_tensor(
out_ragged).to_tensor()
input1 = keras.Input(shape=(None, ))
input2 = keras.Input(shape=(None, ))
out = keras.layers.Add()([input1, input2])
model = keras.models.Model(inputs=[input1, input2], outputs=out)
out_dense = model.predict([dense_data, dense_data], steps=1)
self.assertAllEqual(out_dense, out_ragged)
@parameterized.named_parameters(
*testing_utils.generate_combinations_with_testcase_name(layer=[
keras.layers.Add, keras.layers.Subtract, keras.layers.Multiply,
keras.layers.Minimum, keras.layers.Maximum, keras.layers.Average
]))
def test_merge_with_scalar_input(self, layer):
x1 = np.array((1))
x2 = np.array((2))
out = layer()([x1, x2])
self.assertEqual(out.shape, ())
if use_dataset:
if action == "predict":
input_data = dataset_ops.DatasetV2.from_tensor_slices(
input_data).batch(batch_size)
else:
input_data = dataset_ops.DatasetV2.from_tensor_slices(
(input_data, expected_output)).batch(batch_size)
expected_output = None
return (input_data, expected_output)
@keras_parameterized.run_with_all_model_types
@keras_parameterized.run_all_keras_modes
@parameterized.named_parameters(
*testing_utils.generate_combinations_with_testcase_name(
use_dict=[True, False],
use_dataset=[True, False],
action=["predict", "evaluate", "fit"]))
class SparseTensorInputTest(keras_parameterized.TestCase):
def test_sparse_tensors(self, use_dict, use_dataset, action):
data = [(sparse_tensor.SparseTensor([[0, 0, 0], [1, 0, 0], [1, 0, 1]],
[1, 2, 3], [2, 1, 3]),
np.array([[[1, -1, -1]], [[2, 3, -1]]])),
(sparse_tensor.SparseTensor(
[[0, 0, 0], [1, 0, 0], [1, 0, 1], [2, 0, 1]], [5, 6, 7, 8],
[3, 1, 4]),
np.array([[[5, -1, -1, -1]], [[6, 7, -1, -1]],
[[-1, 8, -1, -1]]]))]
# Prepare the model to test.
input_name = get_input_name(use_dict)
model_input = input_layer.Input(shape=(1, None),
sparse=True,
class ConvLSTMTest(keras_parameterized.TestCase):
@parameterized.named_parameters(
*testing_utils.generate_combinations_with_testcase_name(
data_format=['channels_first', 'channels_last'],
return_sequences=[True, False]))
def test_conv_lstm(self, data_format, return_sequences):
num_row = 3
num_col = 3
filters = 2
num_samples = 1
input_channel = 2
input_num_row = 5
input_num_col = 5
sequence_len = 2
if data_format == 'channels_first':
inputs = np.random.rand(num_samples, sequence_len,
input_channel,
input_num_row, input_num_col)
else:
inputs = np.random.rand(num_samples, sequence_len,
input_num_row, input_num_col,
input_channel)
# test for return state:
x = keras.Input(batch_shape=inputs.shape)
kwargs = {'data_format': data_format,
'return_sequences': return_sequences,
'return_state': True,
'stateful': True,
'filters': filters,
'kernel_size': (num_row, num_col),
'padding': 'valid'}
layer = keras.layers.ConvLSTM2D(**kwargs)
layer.build(inputs.shape)
outputs = layer(x)
_, states = outputs[0], outputs[1:]
self.assertEqual(len(states), 2)
model = keras.models.Model(x, states[0])
state = model.predict(inputs)
self.assertAllClose(
keras.backend.eval(layer.states[0]), state, atol=1e-4)
# test for output shape:
testing_utils.layer_test(
keras.layers.ConvLSTM2D,
kwargs={'data_format': data_format,
'return_sequences': return_sequences,
'filters': filters,
'kernel_size': (num_row, num_col),
'padding': 'valid'},
input_shape=inputs.shape)
def test_conv_lstm_statefulness(self):
# Tests for statefulness
num_row = 3
num_col = 3
filters = 2
num_samples = 1
input_channel = 2
input_num_row = 5
input_num_col = 5
sequence_len = 2
inputs = np.random.rand(num_samples, sequence_len,
input_num_row, input_num_col,
input_channel)
with self.cached_session():
model = keras.models.Sequential()
kwargs = {'data_format': 'channels_last',
'return_sequences': False,
'filters': filters,
'kernel_size': (num_row, num_col),
'stateful': True,
'batch_input_shape': inputs.shape,
'padding': 'same'}
layer = keras.layers.ConvLSTM2D(**kwargs)
model.add(layer)
model.compile(optimizer='sgd', loss='mse')
out1 = model.predict(np.ones_like(inputs))
# train once so that the states change
model.train_on_batch(np.ones_like(inputs),
np.random.random(out1.shape))
out2 = model.predict(np.ones_like(inputs))
# if the state is not reset, output should be different
self.assertNotEqual(out1.max(), out2.max())
# check that output changes after states are reset
# (even though the model itself didn't change)
layer.reset_states()
out3 = model.predict(np.ones_like(inputs))
self.assertNotEqual(out3.max(), out2.max())
# check that container-level reset_states() works
model.reset_states()
out4 = model.predict(np.ones_like(inputs))
self.assertAllClose(out3, out4, atol=1e-5)
# check that the call to `predict` updated the states
out5 = model.predict(np.ones_like(inputs))
self.assertNotEqual(out4.max(), out5.max())
def test_conv_lstm_regularizers(self):
# check regularizers
num_row = 3
num_col = 3
filters = 2
num_samples = 1
input_channel = 2
input_num_row = 5
input_num_col = 5
sequence_len = 2
inputs = np.random.rand(num_samples, sequence_len,
input_num_row, input_num_col,
input_channel)
with self.cached_session():
kwargs = {'data_format': 'channels_last',
'return_sequences': False,
'kernel_size': (num_row, num_col),
'stateful': True,
'filters': filters,
'batch_input_shape': inputs.shape,
'kernel_regularizer': keras.regularizers.L1L2(l1=0.01),
'recurrent_regularizer': keras.regularizers.L1L2(l1=0.01),
'activity_regularizer': 'l2',
'bias_regularizer': 'l2',
'kernel_constraint': 'max_norm',
'recurrent_constraint': 'max_norm',
'bias_constraint': 'max_norm',
'padding': 'same'}
layer = keras.layers.ConvLSTM2D(**kwargs)
layer.build(inputs.shape)
self.assertEqual(len(layer.losses), 3)
layer(keras.backend.variable(np.ones(inputs.shape)))
self.assertEqual(len(layer.losses), 4)
def test_conv_lstm_dropout(self):
# check dropout
with self.cached_session():
testing_utils.layer_test(
keras.layers.ConvLSTM2D,
kwargs={'data_format': 'channels_last',
'return_sequences': False,
'filters': 2,
'kernel_size': (3, 3),
'padding': 'same',
'dropout': 0.1,
'recurrent_dropout': 0.1},
input_shape=(1, 2, 5, 5, 2))
def test_conv_lstm_cloning(self):
with self.cached_session():
model = keras.models.Sequential()
model.add(keras.layers.ConvLSTM2D(5, 3, input_shape=(None, 5, 5, 3)))
test_inputs = np.random.random((2, 4, 5, 5, 3))
reference_outputs = model.predict(test_inputs)
weights = model.get_weights()
# Use a new graph to clone the model
with self.cached_session():
clone = keras.models.clone_model(model)
clone.set_weights(weights)
outputs = clone.predict(test_inputs)
self.assertAllClose(reference_outputs, outputs, atol=1e-5)
@test.disable_with_predicate(
pred=test.is_built_with_rocm,
skip_message='Skipping the test as OOM occurred with 1 GB budget.')
def test_conv_lstm_with_initial_state(self):
num_samples = 32
sequence_len = 5
encoder_inputs = keras.layers.Input((None, 32, 32, 3))
encoder = keras.layers.ConvLSTM2D(
filters=32, kernel_size=(3, 3), padding='same',
return_sequences=False, return_state=True)
_, state_h, state_c = encoder(encoder_inputs)
encoder_states = [state_h, state_c]
decoder_inputs = keras.layers.Input((None, 32, 32, 4))
decoder_lstm = keras.layers.ConvLSTM2D(
filters=32, kernel_size=(3, 3), padding='same',
return_sequences=False, return_state=False)
decoder_outputs = decoder_lstm(decoder_inputs, initial_state=encoder_states)
output = keras.layers.Conv2D(
1, (3, 3), padding='same', activation='relu')(decoder_outputs)
model = keras.Model([encoder_inputs, decoder_inputs], output)
model.compile(
optimizer='sgd', loss='mse',
run_eagerly=testing_utils.should_run_eagerly())
x_1 = np.random.rand(num_samples, sequence_len, 32, 32, 3)
x_2 = np.random.rand(num_samples, sequence_len, 32, 32, 4)
y = np.random.rand(num_samples, 32, 32, 1)
model.fit([x_1, x_2], y)
model.predict([x_1, x_2])
class CuDNNTest(keras_parameterized.TestCase):
@parameterized.named_parameters(
*testing_utils.generate_combinations_with_testcase_name(
layer_class=[keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM],
return_sequences=[True, False]))
@test_util.run_gpu_only
def test_cudnn_rnn_return_sequence(self, layer_class, return_sequences):
input_size = 10
timesteps = 6
units = 2
num_samples = 32
testing_utils.layer_test(
layer_class,
kwargs={'units': units,
'return_sequences': return_sequences},
input_shape=(num_samples, timesteps, input_size))
@parameterized.named_parameters(
*testing_utils.generate_combinations_with_testcase_name(
layer_class=[keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM],
go_backwards=[True, False]))
@test_util.run_gpu_only
def test_cudnn_rnn_go_backward(self, layer_class, go_backwards):
input_size = 10
timesteps = 6
units = 2
num_samples = 32
testing_utils.layer_test(
layer_class,
kwargs={'units': units,
'go_backwards': go_backwards},
input_shape=(num_samples, timesteps, input_size))
@parameterized.named_parameters(
('cudnngru', keras.layers.CuDNNGRU),
('cudnnlstm', keras.layers.CuDNNLSTM),
)
@test_util.run_gpu_only
def test_return_state(self, layer_class):
input_size = 10
timesteps = 6
units = 2
num_samples = 32
num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1
inputs = keras.Input(batch_shape=(num_samples, timesteps, input_size))
layer = layer_class(units, return_state=True, stateful=True)
outputs = layer(inputs)
_, state = outputs[0], outputs[1:]
self.assertEqual(len(state), num_states)
model = keras.models.Model(inputs, state[0])
model.run_eagerly = testing_utils.should_run_eagerly()
inputs = np.random.random((num_samples, timesteps, input_size))
state = model.predict(inputs)
np.testing.assert_allclose(
keras.backend.eval(layer.states[0]), state, atol=1e-4)
@parameterized.named_parameters(
('cudnngru', keras.layers.CuDNNGRU),
('cudnnlstm', keras.layers.CuDNNLSTM),
)
@test_util.run_gpu_only
def test_time_major_input(self, layer_class):
input_size = 10
timesteps = 6
units = 2
num_samples = 32
model = keras.models.Sequential()
model.add(
keras.layers.Lambda(lambda t: array_ops.transpose(t, [1, 0, 2])))
layer = layer_class(units, time_major=True, return_sequences=True)
model.add(layer)
model.add(
keras.layers.Lambda(lambda t: array_ops.transpose(t, [1, 0, 2])))
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(learning_rate=0.001))
model.fit(
np.ones((num_samples, timesteps, input_size)),
np.ones((num_samples, timesteps, units)))
out = model.predict(np.ones((num_samples, timesteps, input_size)))
self.assertEqual(out.shape, (num_samples, timesteps, units))
@parameterized.named_parameters(
('cudnngru', keras.layers.CuDNNGRU),
('cudnnlstm', keras.layers.CuDNNLSTM),
)
@test_util.run_gpu_only
def test_specify_initial_state_keras_tensor(self, layer_class):
input_size = 10
timesteps = 6
units = 2
num_samples = 32
num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1
inputs = keras.Input((timesteps, input_size))
initial_state = [keras.Input((units,)) for _ in range(num_states)]
layer = layer_class(units)
if len(initial_state) == 1:
output = layer(inputs, initial_state=initial_state[0])
else:
output = layer(inputs, initial_state=initial_state)
self.assertTrue(
any(initial_state[0] is t
for t in layer._inbound_nodes[0].input_tensors))
model = keras.models.Model([inputs] + initial_state, output)
model.compile(
loss='categorical_crossentropy',
optimizer=RMSprop(learning_rate=0.001),
run_eagerly=testing_utils.should_run_eagerly())
inputs = np.random.random((num_samples, timesteps, input_size))
initial_state = [
np.random.random((num_samples, units)) for _ in range(num_states)
]
targets = np.random.random((num_samples, units))
model.fit([inputs] + initial_state, targets)
class CuDNNV1OnlyTest(keras_parameterized.TestCase):
@test_util.run_gpu_only
def test_trainability(self):
input_size = 10
units = 2
for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
layer = layer_class(units)
layer.build((None, None, input_size))
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.trainable_weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 0)
layer.trainable = False
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 3)
self.assertEqual(len(layer.trainable_weights), 0)
layer.trainable = True
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.trainable_weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 0)
@parameterized.named_parameters(
*testing_utils.generate_combinations_with_testcase_name(
rnn_type=['LSTM', 'GRU'], to_cudnn=[True, False],
bidirectional=[False], implementation=[1, 2],
model_nest_level=[1, 2], model_type=['seq', 'func']))
@test_util.run_v1_only('b/120911602, b/112083752')
@test_util.run_gpu_only
def test_load_weights_between_noncudnn_rnn(self, rnn_type, to_cudnn,
bidirectional, implementation,
model_nest_level, model_type):
input_size = 10
timesteps = 6
input_shape = (timesteps, input_size)
units = 2
num_samples = 32
inputs = np.random.random((num_samples, timesteps, input_size))
rnn_layer_kwargs = {
'recurrent_activation': 'sigmoid',
# ensure biases are non-zero and properly converted
'bias_initializer': 'random_uniform',
'implementation': implementation
}
if rnn_type == 'LSTM':
rnn_layer_class = keras.layers.LSTM
cudnn_rnn_layer_class = keras.layers.CuDNNLSTM
else:
rnn_layer_class = keras.layers.GRU
cudnn_rnn_layer_class = keras.layers.CuDNNGRU
rnn_layer_kwargs['reset_after'] = True
layer = rnn_layer_class(units, **rnn_layer_kwargs)
cudnn_layer = cudnn_rnn_layer_class(units)
model = self._make_nested_model(input_shape, layer, model_nest_level,
model_type)
cudnn_model = self._make_nested_model(input_shape, cudnn_layer,
model_nest_level, model_type)
if to_cudnn:
self._convert_model_weights(model, cudnn_model)
else:
self._convert_model_weights(cudnn_model, model)
self.assertAllClose(model.predict(inputs), cudnn_model.predict(inputs),
atol=1e-4)
def _make_nested_model(self, input_shape, layer, level=1, model_type='func'):
# example: make_nested_seq_model((1,), Dense(10), level=2).summary()
def make_nested_seq_model(input_shape, layer, level=1):
model = layer
for i in range(1, level + 1):
layers = [keras.layers.InputLayer(input_shape),
model] if (i == 1) else [model]
model = keras.models.Sequential(layers)
if i > 1:
model.build((None,) + input_shape)
return model
# example: make_nested_func_model((1,), Dense(10), level=2).summary()
def make_nested_func_model(input_shape, layer, level=1):
model_input = keras.layers.Input(input_shape)
model = layer
for _ in range(level):
model = keras.models.Model(model_input, model(model_input))
return model
if model_type == 'func':
return make_nested_func_model(input_shape, layer, level)
elif model_type == 'seq':
return make_nested_seq_model(input_shape, layer, level)
def _convert_model_weights(self, source_model, target_model):
_, fname = tempfile.mkstemp('.h5')
source_model.save_weights(fname)
target_model.load_weights(fname)
os.remove(fname)
@test_util.run_gpu_only
def test_preprocess_weights_for_loading_gru_incompatible(self):
"""Test loading weights between incompatible layers.
Should fail fast with an exception.
"""
input_shape = (3, 5)
def gru(cudnn=False, **kwargs):
layer_class = keras.layers.CuDNNGRU if cudnn else keras.layers.GRUV1
return layer_class(2, input_shape=input_shape, **kwargs)
def get_layer_weights(layer):
layer.build(input_shape=input_shape)
return layer.get_weights()
def assert_not_compatible(src, dest, message):
with self.assertRaises(ValueError) as ex:
keras.saving.hdf5_format.preprocess_weights_for_loading(
dest,
get_layer_weights(src))
self.assertIn(message, str(ex.exception))
assert_not_compatible(
gru(),
gru(cudnn=True),
'GRU(reset_after=False) is not compatible with CuDNNGRU')
assert_not_compatible(
gru(cudnn=True),
gru(),
'CuDNNGRU is not compatible with GRU(reset_after=False)')
assert_not_compatible(
gru(),
gru(reset_after=True),
'GRU(reset_after=False) is not compatible with '
'GRU(reset_after=True)')
assert_not_compatible(
gru(reset_after=True),
gru(),
'GRU(reset_after=True) is not compatible with '
'GRU(reset_after=False)')
class CuDNNV1OnlyTest(keras_parameterized.TestCase):
@test_util.run_gpu_only
def test_trainability(self):
input_size = 10
units = 2
for layer_class in [keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM]:
layer = layer_class(units)
layer.build((None, None, input_size))
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.trainable_weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 0)
layer.trainable = False
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 3)
self.assertEqual(len(layer.trainable_weights), 0)
layer.trainable = True
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.trainable_weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 0)
# TODO(b/156439419): Reenable after the bug is fixed.
@parameterized.named_parameters(
*testing_utils.generate_combinations_with_testcase_name(
rnn_type=['LSTM', 'GRU'], to_cudnn=[True, False],
bidirectional=[True, False], implementation=[1, 2],
model_nest_level=[1, 2], model_type=['seq', 'func']))
@test_util.run_v1_only('b/120911602, b/112083752')
@test_util.run_gpu_only
def DISALBED_test_load_weights_between_noncudnn_rnn(
self, rnn_type, to_cudnn, bidirectional, implementation,
model_nest_level, model_type):
input_size = 10
timesteps = 6
input_shape = (timesteps, input_size)
units = 2
num_samples = 32
inputs = np.random.random((num_samples, timesteps, input_size))
rnn_layer_kwargs = {
'recurrent_activation': 'sigmoid',
# ensure biases are non-zero and properly converted
'bias_initializer': 'random_uniform',
'implementation': implementation
}
if rnn_type == 'LSTM':
rnn_layer_class = keras.layers.LSTM
cudnn_rnn_layer_class = keras.layers.CuDNNLSTM
else:
rnn_layer_class = keras.layers.GRU
cudnn_rnn_layer_class = keras.layers.CuDNNGRU
rnn_layer_kwargs['reset_after'] = True
layer = rnn_layer_class(units, **rnn_layer_kwargs)
if bidirectional:
layer = keras.layers.Bidirectional(layer)
cudnn_layer = cudnn_rnn_layer_class(units)
if bidirectional:
cudnn_layer = keras.layers.Bidirectional(cudnn_layer)
model = self._make_nested_model(input_shape, layer, model_nest_level,
model_type)
cudnn_model = self._make_nested_model(input_shape, cudnn_layer,
model_nest_level, model_type)
if to_cudnn:
self._convert_model_weights(model, cudnn_model)
else:
self._convert_model_weights(cudnn_model, model)
self.assertAllClose(model.predict(inputs), cudnn_model.predict(inputs),
atol=1e-4)
def _make_nested_model(self, input_shape, layer, level=1, model_type='func'):
# example: make_nested_seq_model((1,), Dense(10), level=2).summary()
def make_nested_seq_model(input_shape, layer, level=1):
model = layer
for i in range(1, level + 1):
layers = [keras.layers.InputLayer(input_shape),
model] if (i == 1) else [model]
model = keras.models.Sequential(layers)
if i > 1:
model.build((None,) + input_shape)
return model
# example: make_nested_func_model((1,), Dense(10), level=2).summary()
def make_nested_func_model(input_shape, layer, level=1):
model_input = keras.layers.Input(input_shape)
model = layer
for _ in range(level):
model = keras.models.Model(model_input, model(model_input))
return model
if model_type == 'func':
return make_nested_func_model(input_shape, layer, level)
elif model_type == 'seq':
return make_nested_seq_model(input_shape, layer, level)
def _convert_model_weights(self, source_model, target_model):
_, fname = tempfile.mkstemp('.h5')
source_model.save_weights(fname)
target_model.load_weights(fname)
os.remove(fname)
@parameterized.named_parameters(
*testing_utils.generate_combinations_with_testcase_name(
rnn_type=['LSTM', 'GRU'], to_cudnn=[True, False]))
@test_util.run_v1_only('b/120911602')
@test_util.run_gpu_only
def test_load_weights_between_noncudnn_rnn_time_distributed(self, rnn_type,
to_cudnn):
# Similar test as test_load_weights_between_noncudnn_rnn() but has different
# rank of input due to usage of TimeDistributed. Issue: #10356.
input_size = 10
steps = 6
timesteps = 6
input_shape = (timesteps, steps, input_size)
units = 2
num_samples = 32
inputs = np.random.random((num_samples, timesteps, steps, input_size))
rnn_layer_kwargs = {
'recurrent_activation': 'sigmoid',
# ensure biases are non-zero and properly converted
'bias_initializer': 'random_uniform',
}
if rnn_type == 'LSTM':
rnn_layer_class = keras.layers.LSTM
cudnn_rnn_layer_class = keras.layers.CuDNNLSTM
else:
rnn_layer_class = keras.layers.GRU
cudnn_rnn_layer_class = keras.layers.CuDNNGRU
rnn_layer_kwargs['reset_after'] = True
layer = rnn_layer_class(units, **rnn_layer_kwargs)
layer = keras.layers.TimeDistributed(layer)
cudnn_layer = cudnn_rnn_layer_class(units)
cudnn_layer = keras.layers.TimeDistributed(cudnn_layer)
model = self._make_nested_model(input_shape, layer)
cudnn_model = self._make_nested_model(input_shape, cudnn_layer)
if to_cudnn:
self._convert_model_weights(model, cudnn_model)
else:
self._convert_model_weights(cudnn_model, model)
self.assertAllClose(model.predict(inputs), cudnn_model.predict(inputs),
atol=1e-4)
@test_util.run_gpu_only
def test_cudnnrnn_bidirectional(self):
rnn = keras.layers.CuDNNGRU
samples = 2
dim = 2
timesteps = 2
output_dim = 2
mode = 'concat'
x = np.random.random((samples, timesteps, dim))
target_dim = 2 * output_dim if mode == 'concat' else output_dim
y = np.random.random((samples, target_dim))
# test with Sequential model
model = keras.Sequential()
model.add(
keras.layers.Bidirectional(
rnn(output_dim), merge_mode=mode, input_shape=(None, dim)))
model.compile(loss='mse', optimizer='rmsprop')
model.fit(x, y, epochs=1, batch_size=1)
# test config
model.get_config()
model = keras.models.model_from_json(model.to_json())
model.summary()
# test stacked bidirectional layers
model = keras.Sequential()
model.add(
keras.layers.Bidirectional(
rnn(output_dim, return_sequences=True),
merge_mode=mode,
input_shape=(None, dim)))
model.add(keras.layers.Bidirectional(rnn(output_dim), merge_mode=mode))
model.compile(loss='mse', optimizer=R'rmsprop')
model.fit(x, y, epochs=1, batch_size=1)
# test with functional API
inputs = keras.Input((timesteps, dim))
outputs = keras.layers.Bidirectional(
rnn(output_dim), merge_mode=mode)(
inputs)
model = keras.Model(inputs, outputs)
model.compile(loss='mse', optimizer=R'rmsprop')
model.fit(x, y, epochs=1, batch_size=1)
# Bidirectional and stateful
inputs = keras.Input(batch_shape=(1, timesteps, dim))
outputs = keras.layers.Bidirectional(
rnn(output_dim, stateful=True), merge_mode=mode)(
inputs)
model = keras.Model(inputs, outputs)
model.compile(loss='mse', optimizer='rmsprop')
model.fit(x, y, epochs=1, batch_size=1)
@test_util.run_gpu_only
def test_preprocess_weights_for_loading_gru_incompatible(self):
"""Test loading weights between incompatible layers.
Should fail fast with an exception.
"""
input_shape = (3, 5)
def gru(cudnn=False, **kwargs):
layer_class = keras.layers.CuDNNGRU if cudnn else keras.layers.GRUV1
return layer_class(2, input_shape=input_shape, **kwargs)
def get_layer_weights(layer):
layer.build(input_shape=input_shape)
return layer.get_weights()
def assert_not_compatible(src, dest, message):
with self.assertRaises(ValueError) as ex:
keras.saving.hdf5_format.preprocess_weights_for_loading(
dest,
get_layer_weights(src))
self.assertIn(message, str(ex.exception))
assert_not_compatible(
gru(),
gru(cudnn=True),
'GRU(reset_after=False) is not compatible with CuDNNGRU')
assert_not_compatible(
gru(cudnn=True),
gru(),
'CuDNNGRU is not compatible with GRU(reset_after=False)')
assert_not_compatible(
gru(),
gru(reset_after=True),
'GRU(reset_after=False) is not compatible with '
'GRU(reset_after=True)')
assert_not_compatible(
gru(reset_after=True),
gru(),
'GRU(reset_after=True) is not compatible with '
'GRU(reset_after=False)')
class CuDNNTest(test.TestCase, parameterized.TestCase):
@test_util.run_in_graph_and_eager_modes
def test_cudnn_rnn_basics(self):
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
input_size = 10
timesteps = 6
units = 2
num_samples = 32
for layer_class in [
keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM
]:
for return_sequences in [True, False]:
with keras.utils.CustomObjectScope({
'keras.layers.CuDNNGRU':
keras.layers.CuDNNGRU,
'keras.layers.CuDNNLSTM':
keras.layers.CuDNNLSTM
}):
testing_utils.layer_test(layer_class,
kwargs={
'units':
units,
'return_sequences':
return_sequences
},
input_shape=(num_samples,
timesteps,
input_size))
for go_backwards in [True, False]:
with keras.utils.CustomObjectScope({
'keras.layers.CuDNNGRU':
keras.layers.CuDNNGRU,
'keras.layers.CuDNNLSTM':
keras.layers.CuDNNLSTM
}):
testing_utils.layer_test(layer_class,
kwargs={
'units':
units,
'go_backwards':
go_backwards
},
input_shape=(num_samples,
timesteps,
input_size))
@test_util.run_in_graph_and_eager_modes
def test_trainability(self):
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
input_size = 10
units = 2
for layer_class in [
keras.layers.CuDNNGRU, keras.layers.CuDNNLSTM
]:
layer = layer_class(units)
layer.build((None, None, input_size))
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.trainable_weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 0)
layer.trainable = False
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 3)
self.assertEqual(len(layer.trainable_weights), 0)
layer.trainable = True
self.assertEqual(len(layer.weights), 3)
self.assertEqual(len(layer.trainable_weights), 3)
self.assertEqual(len(layer.non_trainable_weights), 0)
@parameterized.named_parameters(
('cudnngru', keras.layers.CuDNNGRU),
('cudnnlstm', keras.layers.CuDNNLSTM),
)
def test_regularizer(self, layer_class):
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
input_size = 10
timesteps = 6
units = 2
num_samples = 32
layer = layer_class(
units,
return_sequences=False,
input_shape=(timesteps, input_size),
kernel_regularizer=keras.regularizers.l1(0.01),
recurrent_regularizer=keras.regularizers.l1(0.01),
bias_regularizer='l2')
layer.build((None, None, input_size))
self.assertEqual(len(layer.losses), 3)
layer = layer_class(units,
return_sequences=False,
input_shape=(timesteps, input_size),
activity_regularizer='l2')
self.assertTrue(layer.activity_regularizer)
x = keras.backend.variable(
np.ones((num_samples, timesteps, input_size)))
layer(x)
self.assertEqual(len(layer.get_losses_for(x)), 1)
@parameterized.named_parameters(
('cudnngru', keras.layers.CuDNNGRU),
('cudnnlstm', keras.layers.CuDNNLSTM),
)
def test_return_state(self, layer_class):
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
input_size = 10
timesteps = 6
units = 2
num_samples = 32
num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1
inputs = keras.Input(batch_shape=(num_samples, timesteps,
input_size))
layer = layer_class(units, return_state=True, stateful=True)
outputs = layer(inputs)
_, state = outputs[0], outputs[1:]
self.assertEqual(len(state), num_states)
model = keras.models.Model(inputs, state[0])
inputs = np.random.random((num_samples, timesteps, input_size))
state = model.predict(inputs)
np.testing.assert_allclose(keras.backend.eval(layer.states[0]),
state,
atol=1e-4)
@parameterized.named_parameters(
('cudnngru', keras.layers.CuDNNGRU),
('cudnnlstm', keras.layers.CuDNNLSTM),
)
def test_specify_initial_state_keras_tensor(self, layer_class):
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
input_size = 10
timesteps = 6
units = 2
num_samples = 32
num_states = 2 if layer_class is keras.layers.CuDNNLSTM else 1
inputs = keras.Input((timesteps, input_size))
initial_state = [
keras.Input((units, )) for _ in range(num_states)
]
layer = layer_class(units)
if len(initial_state) == 1:
output = layer(inputs, initial_state=initial_state[0])
else:
output = layer(inputs, initial_state=initial_state)
self.assertIn(initial_state[0],
layer._inbound_nodes[0].input_tensors)
model = keras.models.Model([inputs] + initial_state, output)
model.compile(loss='categorical_crossentropy',
optimizer='adam')
inputs = np.random.random((num_samples, timesteps, input_size))
initial_state = [
np.random.random((num_samples, units))
for _ in range(num_states)
]
targets = np.random.random((num_samples, units))
model.fit([inputs] + initial_state, targets)
@parameterized.named_parameters(
('cudnngru', keras.layers.CuDNNGRU),
('cudnnlstm', keras.layers.CuDNNLSTM),
)
def test_statefulness(self, layer_class):
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
input_size = 10
timesteps = 6
units = 2
num_samples = 32
model = keras.models.Sequential()
model.add(
keras.layers.Embedding(10,
input_size,
input_length=timesteps,
batch_input_shape=(num_samples,
timesteps)))
layer = layer_class(units,
return_sequences=False,
stateful=True,
weights=None)
model.add(layer)
model.compile(optimizer='sgd', loss='mse')
out1 = model.predict(np.ones((num_samples, timesteps)))
self.assertEqual(out1.shape, (num_samples, units))
# train once so that the states change
model.train_on_batch(np.ones((num_samples, timesteps)),
np.ones((num_samples, units)))
out2 = model.predict(np.ones((num_samples, timesteps)))
# if the state is not reset, output should be different
self.assertNotEqual(out1.max(), out2.max())
# check that output changes after states are reset
# (even though the model itself didn't change)
layer.reset_states()
out3 = model.predict(np.ones((num_samples, timesteps)))
self.assertNotEqual(out2.max(), out3.max())
# check that container-level reset_states() works
model.reset_states()
out4 = model.predict(np.ones((num_samples, timesteps)))
self.assertAllClose(out3, out4, atol=1e-5)
# check that the call to `predict` updated the states
out5 = model.predict(np.ones((num_samples, timesteps)))
self.assertNotEqual(out4.max(), out5.max())
@parameterized.named_parameters(
*testing_utils.generate_combinations_with_testcase_name(
rnn_type=['LSTM', 'GRU'],
to_cudnn=[True, False],
bidirectional=[True, False],
implementation=[1, 2],
model_nest_level=[1, 2],
model_type=['seq', 'func']))
def test_load_weights_between_noncudnn_rnn(self, rnn_type, to_cudnn,
bidirectional, implementation,
model_nest_level, model_type):
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
input_size = 10
timesteps = 6
input_shape = (timesteps, input_size)
units = 2
num_samples = 32
inputs = np.random.random((num_samples, timesteps, input_size))
rnn_layer_kwargs = {
'recurrent_activation': 'sigmoid',
# ensure biases are non-zero and properly converted
'bias_initializer': 'random_uniform',
'implementation': implementation
}
if rnn_type == 'LSTM':
rnn_layer_class = keras.layers.LSTM
cudnn_rnn_layer_class = keras.layers.CuDNNLSTM
else:
rnn_layer_class = keras.layers.GRU
cudnn_rnn_layer_class = keras.layers.CuDNNGRU
rnn_layer_kwargs['reset_after'] = True
layer = rnn_layer_class(units, **rnn_layer_kwargs)
if bidirectional:
layer = keras.layers.Bidirectional(layer)
cudnn_layer = cudnn_rnn_layer_class(units)
if bidirectional:
cudnn_layer = keras.layers.Bidirectional(cudnn_layer)
model = self._make_nested_model(input_shape, layer,
model_nest_level, model_type)
cudnn_model = self._make_nested_model(input_shape, cudnn_layer,
model_nest_level,
model_type)
if to_cudnn:
self._convert_model_weights(model, cudnn_model)
else:
self._convert_model_weights(cudnn_model, model)
self.assertAllClose(model.predict(inputs),
cudnn_model.predict(inputs),
atol=1e-4)
def _make_nested_model(self,
input_shape,
layer,
level=1,
model_type='func'):
# example: make_nested_seq_model((1,), Dense(10), level=2).summary()
def make_nested_seq_model(input_shape, layer, level=1):
model = layer
for i in range(1, level + 1):
layers = [keras.layers.InputLayer(input_shape), model
] if (i == 1) else [model]
model = keras.models.Sequential(layers)
return model
# example: make_nested_func_model((1,), Dense(10), level=2).summary()
def make_nested_func_model(input_shape, layer, level=1):
model_input = keras.layers.Input(input_shape)
model = layer
for _ in range(level):
model = keras.models.Model(model_input, model(model_input))
return model
if model_type == 'func':
return make_nested_func_model(input_shape, layer, level)
elif model_type == 'seq':
return make_nested_seq_model(input_shape, layer, level)
def _convert_model_weights(self, source_model, target_model):
_, fname = tempfile.mkstemp('.h5')
source_model.save_weights(fname)
target_model.load_weights(fname)
os.remove(fname)
@parameterized.named_parameters(
*testing_utils.generate_combinations_with_testcase_name(
rnn_type=['LSTM', 'GRU'], to_cudnn=[True, False]))
def test_load_weights_between_noncudnn_rnn_time_distributed(
self, rnn_type, to_cudnn):
# Similar test as test_load_weights_between_noncudnn_rnn() but has different
# rank of input due to usage of TimeDistributed. Issue: #10356.
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
input_size = 10
steps = 6
timesteps = 6
input_shape = (timesteps, steps, input_size)
units = 2
num_samples = 32
inputs = np.random.random(
(num_samples, timesteps, steps, input_size))
rnn_layer_kwargs = {
'recurrent_activation': 'sigmoid',
# ensure biases are non-zero and properly converted
'bias_initializer': 'random_uniform',
}
if rnn_type == 'LSTM':
rnn_layer_class = keras.layers.LSTM
cudnn_rnn_layer_class = keras.layers.CuDNNLSTM
else:
rnn_layer_class = keras.layers.GRU
cudnn_rnn_layer_class = keras.layers.CuDNNGRU
rnn_layer_kwargs['reset_after'] = True
layer = rnn_layer_class(units, **rnn_layer_kwargs)
layer = keras.layers.TimeDistributed(layer)
cudnn_layer = cudnn_rnn_layer_class(units)
cudnn_layer = keras.layers.TimeDistributed(cudnn_layer)
model = self._make_nested_model(input_shape, layer)
cudnn_model = self._make_nested_model(input_shape, cudnn_layer)
if to_cudnn:
self._convert_model_weights(model, cudnn_model)
else:
self._convert_model_weights(cudnn_model, model)
self.assertAllClose(model.predict(inputs),
cudnn_model.predict(inputs),
atol=1e-4)
@test_util.run_in_graph_and_eager_modes
def test_cudnnrnn_bidirectional(self):
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
rnn = keras.layers.CuDNNGRU
samples = 2
dim = 2
timesteps = 2
output_dim = 2
mode = 'concat'
x = np.random.random((samples, timesteps, dim))
target_dim = 2 * output_dim if mode == 'concat' else output_dim
y = np.random.random((samples, target_dim))
# test with Sequential model
model = keras.Sequential()
model.add(
keras.layers.Bidirectional(rnn(output_dim),
merge_mode=mode,
input_shape=(None, dim)))
model.compile(loss='mse',
optimizer=RMSPropOptimizer(learning_rate=0.001))
model.fit(x, y, epochs=1, batch_size=1)
# test config
model.get_config()
model = keras.models.model_from_json(model.to_json())
model.summary()
# test stacked bidirectional layers
model = keras.Sequential()
model.add(
keras.layers.Bidirectional(rnn(output_dim,
return_sequences=True),
merge_mode=mode,
input_shape=(None, dim)))
model.add(
keras.layers.Bidirectional(rnn(output_dim),
merge_mode=mode))
model.compile(loss='mse',
optimizer=RMSPropOptimizer(learning_rate=0.001))
model.fit(x, y, epochs=1, batch_size=1)
# test with functional API
inputs = keras.Input((timesteps, dim))
outputs = keras.layers.Bidirectional(rnn(output_dim),
merge_mode=mode)(inputs)
model = keras.Model(inputs, outputs)
model.compile(loss='mse',
optimizer=RMSPropOptimizer(learning_rate=0.001))
model.fit(x, y, epochs=1, batch_size=1)
# Bidirectional and stateful
inputs = keras.Input(batch_shape=(1, timesteps, dim))
outputs = keras.layers.Bidirectional(rnn(output_dim,
stateful=True),
merge_mode=mode)(inputs)
model = keras.Model(inputs, outputs)
model.compile(loss='mse',
optimizer=RMSPropOptimizer(learning_rate=0.001))
model.fit(x, y, epochs=1, batch_size=1)
def test_preprocess_weights_for_loading_gru_incompatible(self):
"""Test loading weights between incompatible layers.
Should fail fast with an exception.
"""
if test.is_gpu_available(cuda_only=True):
with self.test_session(use_gpu=True):
input_shape = (3, 5)
def gru(cudnn=False, **kwargs):
layer_class = keras.layers.CuDNNGRU if cudnn else keras.layers.GRU
return layer_class(2, input_shape=input_shape, **kwargs)
def get_layer_weights(layer):
layer.build(input_shape=input_shape)
return layer.get_weights()
def assert_not_compatible(src, dest, message):
with self.assertRaises(ValueError) as ex:
keras.engine.saving.preprocess_weights_for_loading(
dest, get_layer_weights(src))
self.assertIn(message, str(ex.exception))
assert_not_compatible(
gru(), gru(cudnn=True),
'GRU(reset_after=False) is not compatible with CuDNNGRU')
assert_not_compatible(
gru(cudnn=True), gru(),
'CuDNNGRU is not compatible with GRU(reset_after=False)')
assert_not_compatible(
gru(), gru(reset_after=True),
'GRU(reset_after=False) is not compatible with '
'GRU(reset_after=True)')
assert_not_compatible(
gru(reset_after=True), gru(),
'GRU(reset_after=True) is not compatible with '
'GRU(reset_after=False)')
