def test_Sum(tmpdir):
in1_data = np.asarray([[1., 2., 3., 4.]], np.float32)
in2_data = np.asarray([[0., 5., -3., 2.]], np.float32)
in1 = C.input_variable(np.shape(in1_data))
in2 = C.input_variable(np.shape(in2_data))
model = C.sum([in1, in2])
verify_two_input(model, in1_data, in2_data, tmpdir, 'Sum_2')
def test_Sum(tmpdir, dtype):
with C.default_options(dtype = dtype):
in1_data = np.asarray([[1., 2., 3., 4.]], dtype = dtype)
in2_data = np.asarray([[0., 5., -3., 2.]], dtype = dtype)
in1 = C.input_variable(np.shape(in1_data))
in2 = C.input_variable(np.shape(in2_data))
model = C.sum([in1, in2])
verify_two_input(model, in1_data, in2_data, tmpdir, 'Sum_2')
def test_Sum(tmpdir, dtype):
with C.default_options(dtype=dtype):
in1_data = np.asarray([[1., 2., 3., 4.]], dtype=dtype)
in2_data = np.asarray([[0., 5., -3., 2.]], dtype=dtype)
in1 = C.input_variable(np.shape(in1_data))
in2 = C.input_variable(np.shape(in2_data))
model = C.sum([in1, in2])
verify_two_input(model, in1_data, in2_data, tmpdir, 'Sum_2')
def _build_network(self, pretrained_policy):
self.input = C.input_variable(self.observation_space_shape,
name='image frame')
self.target = C.input_variable((1, ), name='q_target')
self.action_index = C.input_variable((1, ))
one_hot_action = C.ops.squeeze(
C.one_hot(self.action_index, self.num_actions))
if pretrained_policy is None:
h = C.layers.Dense(64, activation=C.relu,
name='dense_1')(self.input)
h = C.layers.Dense(32, activation=C.relu, name='dense_1')(h)
self.action_probabilities = C.layers.Dense(self.num_actions,
activation=C.softmax,
name='dense_1')(h)
else:
self.action_probabilities = C.Function.load(pretrained_policy)(
self.input)
selected_action_probablity = C.ops.times_transpose(
self.action_probabilities, one_hot_action)
self.log_probability = C.ops.log(selected_action_probablity)
self.loss = C.sum(self.log_probability * self.target)