def test_stacked_rnn_compute_output_shape():
cells = [recurrent.LSTMCell(3), recurrent.LSTMCell(6)]
layer = recurrent.RNN(cells, return_state=True, return_sequences=True)
output_shape = layer.compute_output_shape((None, timesteps, embedding_dim))
expected_output_shape = [(None, timesteps, 6), (None, 6), (None, 6),
(None, 3), (None, 3)]
assert output_shape == expected_output_shape
def test_stacked_rnn_dropout():
cells = [recurrent.LSTMCell(3, dropout=0.1, recurrent_dropout=0.1),
recurrent.LSTMCell(3, dropout=0.1, recurrent_dropout=0.1)]
layer = recurrent.RNN(cells)
x = keras.Input((None, 5))
y = layer(x)
model = keras.models.Model(x, y)
model.compile('sgd', 'mse')
x_np = np.random.random((6, 5, 5))
y_np = np.random.random((6, 3))
model.train_on_batch(x_np, y_np)
def test_stacked_rnn_compute_output_shape():
cells = [recurrent.LSTMCell(3), recurrent.LSTMCell(6)]
layer = recurrent.RNN(cells, return_state=True, return_sequences=True)
output_shape = layer.compute_output_shape((None, timesteps, embedding_dim))
expected_output_shape = [(None, timesteps, 6), (None, 3), (None, 3),
(None, 6), (None, 6)]
assert output_shape == expected_output_shape
# Test reverse_state_order = True for stacked cell.
stacked_cell = recurrent.StackedRNNCells(cells, reverse_state_order=True)
layer = recurrent.RNN(stacked_cell,
return_state=True,
return_sequences=True)
output_shape = layer.compute_output_shape((None, timesteps, embedding_dim))
expected_output_shape = [(None, timesteps, 6), (None, 6), (None, 6),
(None, 3), (None, 3)]
assert output_shape == expected_output_shape
def test_stacked_rnn_attributes():
cells = [recurrent.LSTMCell(3),
recurrent.LSTMCell(3, kernel_regularizer='l2')]
layer = recurrent.RNN(cells)
layer.build((None, None, 5))
# Test regularization losses
assert len(layer.losses) == 1
# Test weights
assert len(layer.trainable_weights) == 6
cells[0].trainable = False
assert len(layer.trainable_weights) == 3
assert len(layer.non_trainable_weights) == 3
x = keras.Input((None, 5))
y = K.sum(x)
cells[0].add_loss(y, inputs=x)