def test_cross_entropy_binary_unmatched_axes(input_tensor):
"""If y and t have different axes, an error should be thrown immediately"""
y = input_tensor
feature_axis, batch_axis = y.axes
t = ng.placeholder([ng.make_axis(feature_axis.length), batch_axis])
with pytest.raises(ng.UnmatchedAxesError):
ng.cross_entropy_binary_inner(y, t)
def test_cross_entropy_binary(transformer_factory):
"""TODO."""
N = ng.make_axis(name='N')
W = ng.make_axis(name='W')
delta = .001
W.length = 20
N.length = 128
axes = ng.make_axes([W, N])
p_u = ng.placeholder(axes)
u = rng.uniform(-3.0, 3.0, p_u.axes)
p_v = ng.placeholder(axes)
v = rng.uniform(-3.0, 3.0, p_u.axes)
y = ng.sigmoid(p_u)
t = ng.softmax(p_v)
val_u = ng.cross_entropy_binary_inner(y, t)
ex = ExecutorFactory()
dval_u_num_fun = ex.numeric_derivative(val_u, p_u, delta, p_v)
dval_u_graph_fun = ex.derivative(val_u, p_u, p_v)
dval_u_num = dval_u_num_fun(u, v)
dval_u_graph = dval_u_graph_fun(u, v)
np.testing.assert_allclose(dval_u_graph, dval_u_num, atol=1e-2, rtol=1e-2)
def fuse_ce_binary_inner_callback(self, op, label_map_op_list):
"""
Callback function that handles fusion for cross_entropy_binary_inner pattern
"""
for (label_map, op) in label_map_op_list:
# Matched Sigmoid pattern, do the replacement here.
y = label_map[self.ce_y_label]
t = label_map[self.ce_t_label]
cross_without_opt_op = ng.cross_entropy_binary_inner(
y, t, enable_sig_opt=False, enable_diff_opt=True)
self.replace_op(op, cross_without_opt_op)
def test_cross_entropy_binary_logistic_shortcut(input_tensor):
"""TODO."""
p_u = input_tensor
p_v = ng.placeholder(p_u.axes)
u = rng.uniform(-3.0, 3.0, p_u.axes)
v = np_softmax(rng.uniform(-3.0, 3.0, p_u.axes), 0)
cel = cross_entropy_binary_logistic(u, v)
cel_shortcut = cross_entropy_binary_logistic_shortcut(u, v)
ng.testing.assert_allclose(cel, cel_shortcut, rtol=1e-5)
with executor(ng.cross_entropy_binary_inner(ng.sigmoid(p_u), p_v), p_u, p_v) as ex:
cel_graph = ex(u, v)
ng.testing.assert_allclose(cel, cel_graph, rtol=1e-5)
def test_cross_entropy_binary_logistic_shortcut(transformer_factory):
"""TODO."""
N = ng.make_axis(name='N')
W = ng.make_axis(name='W')
W.length = 20
N.length = 128
axes = ng.make_axes([W, N])
p_u = ng.placeholder(axes)
u = rng.uniform(-3.0, 3.0, p_u.axes)
p_v = ng.placeholder(axes)
v = np_softmax(rng.uniform(-3.0, 3.0, p_u.axes), 0)
cel = cross_entropy_binary_logistic(u, v)
cel_shortcut = cross_entropy_binary_logistic_shortcut(u, v)
np.testing.assert_allclose(cel, cel_shortcut, rtol=1e-5)
cel_graph = executor(ng.cross_entropy_binary_inner(ng.sigmoid(p_u), p_v), p_u, p_v)(u, v)
np.testing.assert_allclose(cel, cel_graph, rtol=1e-5)
def test_cross_entropy_binary(input_tensor):
"""TODO."""
p_u = input_tensor
p_v = ng.placeholder(p_u.axes)
u = rng.uniform(-3.0, 3.0, p_u.axes)
v = rng.uniform(-3.0, 3.0, p_u.axes)
delta = .001
y = ng.sigmoid(p_u)
t = ng.softmax(p_v)
val_u = ng.cross_entropy_binary_inner(y, t)
with ExecutorFactory() as ex:
dval_u_num_fun = ex.numeric_derivative(val_u, p_u, delta, p_v)
dval_u_graph_fun = ex.derivative(val_u, p_u, p_v)
dval_u_num = dval_u_num_fun(u, v)
dval_u_graph = dval_u_graph_fun(u, v)
ng.testing.assert_allclose(dval_u_graph, dval_u_num, atol=1e-2, rtol=1e-2)
def construct_ce_binary_inner_pattern(self):
"""
Generate graph op that represents a pattern for Cross Entropy Binary Inner operation
ng.cross_entropy_binary_inner(y, t, enable_sig_opt=True, enable_diff_opt=True)
Returns:
Single pattern that matches "optimized" Cross Entropy Binary Inner
"""
self.ce_x_label = "X"
self.ce_y_label = "Y"
self.ce_t_label = "T"
x = PatternLabelOp(self.ce_x_label, axes={ng.make_axis(name='N')})
y = PatternLabelOp(self.ce_y_label, axes={ng.make_axis(name='N')})
t = PatternLabelOp(self.ce_t_label, axes={ng.make_axis(name='N')})
y.deriv_handler = ng.SigmoidOp(x)
cross_op = ng.cross_entropy_binary_inner(y,
t,
enable_sig_opt=True,
enable_diff_opt=True)
return cross_op
