def test_gru(self):
"""Test invoking GRU in eager mode."""
with context.eager_mode():
with tfe.IsolateTest():
batch_size = 10
n_hidden = 7
in_channels = 4
n_steps = 6
input = np.random.rand(batch_size, n_steps,
in_channels).astype(np.float32)
layer = layers.GRU(n_hidden, batch_size)
result, state = layer(input)
assert result.shape == (batch_size, n_steps, n_hidden)
assert len(layer.variables) == 4
# Creating a second layer should produce different results, since it has
# different random weights.
layer2 = layers.GRU(n_hidden, batch_size)
result2, state2 = layer2(input)
assert not np.allclose(result, result2)
# But evaluating the first layer again should produce the same result as before.
result3, state3 = layer(input)
assert np.allclose(result, result3)
# But if we specify a different starting state, that should produce a
# different result.
result4, state4 = layer(input, initial_state=state3)
assert not np.allclose(result, result4)
def _create_decoder(self, n_layers, dropout):
"""Create the decoder layers."""
prev_layer = layers.Repeat(
self._max_output_length, in_layers=self.embedding)
for i in range(n_layers):
if dropout > 0.0:
prev_layer = layers.Dropout(dropout, in_layers=prev_layer)
prev_layer = layers.GRU(
self._embedding_dimension, self.batch_size, in_layers=prev_layer)
return layers.Dense(
len(self._output_tokens),
in_layers=prev_layer,
activation_fn=tf.nn.softmax)
def _create_encoder(self, n_layers, dropout):
"""Create the encoder layers."""
prev_layer = self._features
for i in range(n_layers):
if dropout > 0.0:
prev_layer = layers.Dropout(dropout, in_layers=prev_layer)
prev_layer = layers.GRU(
self._embedding_dimension, self.batch_size, in_layers=prev_layer)
prev_layer = layers.Gather(in_layers=[prev_layer, self._gather_indices])
if self._variational:
self._embedding_mean = layers.Dense(
self._embedding_dimension, in_layers=prev_layer)
self._embedding_stddev = layers.Dense(
self._embedding_dimension, in_layers=prev_layer)
prev_layer = layers.CombineMeanStd(
[self._embedding_mean, self._embedding_stddev], training_only=True)
return prev_layer