def test_op_cross_entropy_with_soft_max_and_axis(output_vector, target_vector, axis, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
x = AA(output_vector, dtype=dt)
t = AA(target_vector, dtype=dt)
expected_forward = []
expected_backward_left = []
expected_backward_right = []
for sample, target in zip(x, t):
ox = sample - sample.max() # subtract max to avoid overflow
exp_x = np.exp(ox)
s_max = exp_x / np.sum(exp_x) # softmax function
forward = np.asarray(-np.sum(target * np.log(s_max, dtype=dt), dtype=dt))
expected_forward.append(forward.tolist())
s = np.sum(target, dtype=dt)
backward = np.subtract(s_max * s, target)
expected_backward_left.append(backward.tolist())
expected_backward_right.append(-1*sample)
expected_forward = [np.reshape(AA(expected_forward, dtype=dt), (x.shape[0], 1))]
expected_backward_left = AA(expected_backward_left, dtype=dt)
expected_backward = {
'left_arg': [expected_backward_left],
'right_arg': [expected_backward_right]
}
from cntk.losses import cross_entropy_with_softmax
_test_binary_op(precision, device_id, cross_entropy_with_softmax,
output_vector, target_vector,
expected_forward, expected_backward, op_param_dict={'axis': axis})
def test_op_classification_error_with_axis(output_vector, target_vector, axis, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
x = AA(output_vector, dtype=dt)
t = AA(target_vector, dtype=dt)
forward = []
expected_backward_left = []
expected_backward_right = []
for sample, target in zip(x, t):
different_position = np.argmax(target) != np.argmax(sample)
forward.append([int(different_position)])
zero_backward = np.zeros_like(target, dtype=dt)
expected_backward_left.append(zero_backward)
expected_backward_right.append(zero_backward)
forward = np.mean(forward)
expected_forward = AA([forward], dtype=dt)
expected_backward_left = AA([expected_backward_left], dtype=dt)
expected_backward_right = AA([expected_backward_right], dtype=dt)
expected_backward = {
'left_arg': expected_backward_left,
'right_arg': expected_backward_right
}
from cntk.metrics import classification_error
_test_binary_op(precision, device_id, classification_error,
output_vector, target_vector,
expected_forward, expected_backward, op_param_dict={'axis':axis})
def test_op_cross_entropy_with_soft_max(output_vector, target_vector, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
o = AA(output_vector, dtype=dt)
t = AA(target_vector, dtype=dt)
ox = o - o.max() # subtract max to avoid overflow
exp_x = np.exp(ox)
s_max = exp_x / np.sum(exp_x) # softmax function
expected_forward = np.asarray(-np.sum(t * np.log(s_max, dtype=dt), dtype=dt))
expected_forward.shape = (1,1,1) + expected_forward.shape
s = np.sum(t, dtype=dt)
backward = np.subtract(s_max * s, t)
backward.shape = (1,) + backward.shape
expected_backward = {
'left_arg': backward,
'right_arg': [-1*o]
}
from cntk.losses import cross_entropy_with_softmax
_test_binary_op(precision, device_id, cross_entropy_with_softmax,
output_vector, target_vector,
expected_forward, expected_backward)
def test_op_classification_error_with_axis(output_vector, target_vector, axis, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
x = AA(output_vector, dtype=dt)
t = AA(target_vector, dtype=dt)
forward = []
expected_backward_left = []
expected_backward_right = []
for sample, target in zip(x, t):
different_position = np.argmax(target) != np.argmax(sample)
forward.append([int(different_position)])
zero_backward = np.zeros_like(target, dtype=dt)
expected_backward_left.append(zero_backward)
expected_backward_right.append(zero_backward)
forward = np.mean(forward)
expected_forward = AA([forward], dtype=dt)
expected_backward_left = AA([expected_backward_left], dtype=dt)
expected_backward_right = AA([expected_backward_right], dtype=dt)
expected_backward = {
'left_arg': expected_backward_left,
'right_arg': expected_backward_right
}
from cntk.metrics import classification_error
_test_binary_op(precision, device_id, classification_error,
output_vector, target_vector,
expected_forward, expected_backward, op_param_dict={'axis':axis})
def test_op_classification_error(output_vector, target_vector, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
o = AA(output_vector, dtype=dt)
t = AA(target_vector, dtype=dt)
different_position = np.argmax(t) != np.argmax(o)
expected_forward = [AA([[int(different_position)]], dtype=dt)]
zero_backward = np.zeros_like(t, dtype=dt)
left_backward = np.copy(zero_backward)
zero_backward[..., np.argmax(o)] = -1.
right_backward = zero_backward
expected_backward = {
'left_arg': [left_backward],
'right_arg': [right_backward]
}
from cntk.metrics import classification_error
_test_binary_op(precision, device_id, classification_error,
output_vector, target_vector,
expected_forward, expected_backward)
def test_op_classification_error(output_vector, target_vector, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
o = AA(output_vector, dtype=dt)
t = AA(target_vector, dtype=dt)
different_position = np.argmax(t) != np.argmax(o)
expected_forward = [AA([[int(different_position)]], dtype=dt)]
zero_backward = np.zeros_like(t, dtype=dt)
left_backward = np.copy(zero_backward)
zero_backward[..., np.argmax(o)] = -1.
right_backward = zero_backward
expected_backward = {
'left_arg': [left_backward],
'right_arg': [right_backward]
}
from cntk.metrics import classification_error
_test_binary_op(precision, device_id, classification_error,
output_vector, target_vector,
expected_forward, expected_backward)
def test_op_cross_entropy_with_soft_max_and_axis(output_vector, target_vector, axis, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
x = AA(output_vector, dtype=dt)
t = AA(target_vector, dtype=dt)
expected_forward = []
expected_backward_left = []
expected_backward_right = []
for sample, target in zip(x, t):
ox = sample - sample.max() # subtract max to avoid overflow
exp_x = np.exp(ox)
s_max = exp_x / np.sum(exp_x) # softmax function
forward = np.asarray(-np.sum(target * np.log(s_max, dtype=dt), dtype=dt))
expected_forward.append(forward.tolist())
s = np.sum(target, dtype=dt)
backward = np.subtract(s_max * s, target)
expected_backward_left.append(backward.tolist())
expected_backward_right.append(-1*sample)
expected_forward = [np.reshape(AA(expected_forward, dtype=dt), (x.shape[0], 1))]
expected_backward_left = AA(expected_backward_left, dtype=dt)
expected_backward = {
'left_arg': [expected_backward_left],
'right_arg': [expected_backward_right]
}
from cntk.losses import cross_entropy_with_softmax
_test_binary_op(precision, device_id, cross_entropy_with_softmax,
output_vector, target_vector,
expected_forward, expected_backward, op_param_dict={'axis': axis})
def test_op_cross_entropy_with_soft_max(output_vector, target_vector, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
o = AA(output_vector, dtype=dt)
t = AA(target_vector, dtype=dt)
ox = o - o.max() # subtract max to avoid overflow
exp_x = np.exp(ox)
s_max = exp_x / np.sum(exp_x) # softmax function
expected_forward = np.asarray(-np.sum(t * np.log(s_max, dtype=dt), dtype=dt))
expected_forward.shape = (1,1,1) + expected_forward.shape
s = np.sum(t, dtype=dt)
backward = np.subtract(s_max * s, t)
backward.shape = (1,) + backward.shape
expected_backward = {
'left_arg': backward,
'right_arg': [-1*o]
}
from cntk.losses import cross_entropy_with_softmax
_test_binary_op(precision, device_id, cross_entropy_with_softmax,
output_vector, target_vector,
expected_forward, expected_backward)
def test_op_squared_error(output_vector, target_vector, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
o = AA(output_vector, dtype=dt)
t = AA(target_vector, dtype=dt)
expected_forward = AA([np.sum((t - o)**2)])
backward = 2 * np.subtract(o, t)
expected_backward = {'left_arg': [backward], 'right_arg': [-1 * backward]}
from cntk.losses import squared_error
_test_binary_op(precision, device_id, squared_error, output_vector,
target_vector, expected_forward, expected_backward)
def test_op_squared_error(output_vector, target_vector, device_id, precision):
dt = PRECISION_TO_TYPE[precision]
o = AA(output_vector, dtype=dt)
t = AA(target_vector, dtype=dt)
expected_forward = AA([np.sum((t - o)**2)])
backward = 2 * np.subtract(o, t)
expected_backward = {
'left_arg': [backward],
'right_arg': [-1*backward]
}
from cntk.losses import squared_error
_test_binary_op(precision, device_id, squared_error,
output_vector, target_vector,
expected_forward, expected_backward)
