def test_gru(self):
"""Test that GRU can be invoked."""
batch_size = 10
n_hidden = 7
in_channels = 4
n_repeat = 2
n_steps = 6
in_tensor = np.random.rand(batch_size, n_steps, in_channels)
with self.session() as sess:
in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32)
out_tensor = GRU(n_hidden, batch_size)(in_tensor)
sess.run(tf.global_variables_initializer())
out_tensor = out_tensor.eval()
assert out_tensor.shape == (batch_size, n_steps, n_hidden)
def test_gru(self):
"""Test that GRU can be invoked."""
batch_size = 10
n_hidden = 7
in_channels = 4
n_repeat = 2
n_steps = 6
in_tensor = np.random.rand(batch_size, n_steps, in_channels)
with self.session() as sess:
in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32)
out_tensor = GRU(n_hidden, batch_size)(in_tensor)
sess.run(tf.global_variables_initializer())
out_tensor = out_tensor.eval()
assert out_tensor.shape == (batch_size, n_steps, n_hidden)
def test_recurrent_layer(self):
"""Test a model that includes a recurrent layer."""
batch_size = 5
tg = dc.models.TensorGraph(batch_size=batch_size, use_queue=False)
features = Feature(shape=(None, 10, 1))
gru = GRU(10, batch_size, in_layers=features)
loss = ReduceMean(in_layers=gru)
tg.add_output(gru)
tg.set_loss(loss)
input = np.random.rand(batch_size, 10, 1)
# If we don't specify the initial state, it should default to zeros.
predictions1 = tg.predict_on_batch(input)
# Explicitly specifying the zero state should give the same result.
initial_state = gru.rnn_initial_states[0]
zero_state = gru.rnn_zero_states[0]
generator = [{features: input, initial_state: zero_state}]
predictions2 = tg.predict_on_generator(generator)
# Specifying a different initial state should produce a different result.
generator = [{
features: input,
initial_state: np.ones(zero_state.shape)
}]
predictions3 = tg.predict_on_generator(generator)
assert np.allclose(predictions1, predictions2)
assert not np.allclose(predictions1, predictions3)
def test_GRU_pickle(): tg = TensorGraph() feature = Feature(shape=(tg.batch_size, 10, 10)) layer = GRU(n_hidden=10, batch_size=tg.batch_size, in_layers=feature) tg.add_output(layer) tg.set_loss(layer) tg.build() tg.save()
def create_layers(self, state, **kwargs):
reshaped = Reshape(shape=(1, -1, 10), in_layers=state)
gru = GRU(n_hidden=10, batch_size=1, in_layers=reshaped)
output = SoftMax(
in_layers=[Reshape(in_layers=[gru], shape=(-1, env.n_actions))])
value = Variable([0.0])
return {'action_prob': output, 'value': value}
