def test_gather(self):
"""Test that Gather can be invoked."""
in_tensor = np.random.uniform(size=(5, 4)).astype(np.float32)
with self.session() as sess:
out_tensor = Gather(indices=[[2], [3]])(in_tensor).eval()
assert np.array_equal([in_tensor[2], in_tensor[3]], out_tensor)
out_tensor = Gather()(in_tensor, np.array([[1, 1], [0, 3]])).eval()
assert np.array_equal([in_tensor[1, 1], in_tensor[0, 3]], out_tensor)
def test_Gather_pickle(): tg = TensorGraph() feature = Feature(shape=(tg.batch_size, 1)) layer = Gather(indices=[[0], [2], [3]], in_layers=feature) tg.add_output(layer) tg.set_loss(layer) tg.build() tg.save()
def create_layers(self, state, **kwargs):
i = Reshape(in_layers=[state[0]], shape=(-1, 1))
i = AddConstant(-1, in_layers=[i])
i = InsertBatchIndex(in_layers=[i])
# shape(i) = (batch_size, 1)
q = Reshape(in_layers=[state[1]], shape=(-1, self.n_queue_obs))
# shape(q) = (batch_size, n_queue_obs)
#q = Dense(16, in_layers=[q], activation_fn=tensorflow.nn.relu)
## shape(q) = (batch_size, 16)
x = q
if not self.single_layer:
for j in range(1):
x1 = Dense(8, in_layers=[x], activation_fn=tensorflow.nn.relu)
x = Concat(in_layers=[q, x1])
# 1) shape(x) = (batch_size, n_queue_obs)
# 2) shape(x) = (batch_size, n_queue_obs + 8)
ps = []
for j in range(self.n_products):
p = Dense(n_actions, in_layers=[x])
ps.append(p)
p = Stack(in_layers=ps, axis=1)
# shape(p) = (batch_size, n_products, n_actions)
p = Gather(in_layers=[p, i])
# shape(p) = (batch_size, n_actions)
p = SoftMax(in_layers=[p])
vs = []
for j in range(self.n_products):
v = Dense(1, in_layers=[x])
vs.append(v)
v = Stack(in_layers=vs, axis=1)
# shape(v) = (batch_size, n_products, 1)
v = Gather(in_layers=[v, i])
# shape(v) = (batch_size, 1)
return {'action_prob': p, 'value': v}