def test_value_manipulation(self):
val = np.random.random((4, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
# get_value
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# set_value
val = np.random.random((4, 2))
KTH.set_value(xth, val)
KTF.set_value(xtf, val)
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# count_params
assert KTH.count_params(xth) == KTF.count_params(xtf)
# print_tensor
check_single_tensor_operation('print_tensor', ())
check_single_tensor_operation('print_tensor', (2,))
check_single_tensor_operation('print_tensor', (4, 3))
check_single_tensor_operation('print_tensor', (1, 2, 3))
val = np.random.random((3, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
assert KTH.get_variable_shape(xth) == KTF.get_variable_shape(xtf)
def test_value_manipulation(self):
val = np.random.random((4, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
# get_value
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# set_value
val = np.random.random((4, 2))
KTH.set_value(xth, val)
KTF.set_value(xtf, val)
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# count_params
assert KTH.count_params(xth) == KTF.count_params(xtf)
# print_tensor
check_single_tensor_operation('print_tensor', ())
check_single_tensor_operation('print_tensor', (2, ))
check_single_tensor_operation('print_tensor', (4, 3))
check_single_tensor_operation('print_tensor', (1, 2, 3))
val = np.random.random((3, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
assert KTH.get_variable_shape(xth) == KTF.get_variable_shape(xtf)
def reset_states(self):
assert self.stateful, 'Layer must be stateful.'
if hasattr(self, 'states'):
K.set_value(self.states[0], np.zeros((self.batch_size, self.output_dim)))
K.set_value(self.states[1], np.zeros((self.batch_size, self.output_dim)))
else:
self.states = [K.zeros((self.batch_size, self.output_dim)),
K.zeros((self.batch_size, self.output_dim))]
def test_value_manipulation(self):
val = np.random.random((4, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
# get_value
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# set_value
val = np.random.random((4, 2))
KTH.set_value(xth, val)
KTF.set_value(xtf, val)
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# count_params
assert KTH.count_params(xth) == KTF.count_params(xtf)
def test_value_manipulation(self):
val = np.random.random((4, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
# get_value
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# set_value
val = np.random.random((4, 2))
KTH.set_value(xth, val)
KTF.set_value(xtf, val)
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# count_params
assert KTH.count_params(xth) == KTF.count_params(xtf)
def shuffle_states(graph_model, indices):
for l in graph_model.layers:
if getattr(l, 'stateful', False):
for s in l.states:
K.set_value(s, s.get_value()[indices])
def set_state(self, noise):
K.set_value(self.states[0], noise)
def shuffle_states(graph_model, indices):
for l in graph_model.layers:
if getattr(l, 'stateful', False):
for s in l.states:
K.set_value(s, s.get_value()[indices])
def set_state(self, noise):
K.set_value(self.states[1], noise)
