def test_cross_entropy(self, constructor):
# cross_entropy in pytorch combines softmax and the cross-entropy function
sl_function = lambda x, labels: sl_F.cross_entropy(
sl_F.softmax(x), labels.detach())
torch_function = lambda x, labels: torch_F.cross_entropy(
x, labels.long())
evaluate_function_with_pytorch(sl_function, torch_function,
constructor)
def test_exp(self, constructor):
evaluate_function_with_pytorch(lambda x: x.exp(), lambda x: x.exp(),
constructor)
def test_sum_self_keepdims(self, constructor):
evaluate_function_with_pytorch(lambda x: x.sum(axis=0, keepdims=False),
lambda x: x.sum(axis=0, keepdims=False),
constructor)
def test_transpose(self, constructor):
evaluate_function_with_pytorch(lambda x: x.T, lambda x: x.T,
constructor)
def test_reshape(self, constructor):
evaluate_function_with_pytorch(lambda x: x.reshape(
(-1, 1)), lambda x: x.reshape((-1, 1)), constructor)
def test_mean(self, constructor):
evaluate_function_with_pytorch(lambda x: x.mean(), lambda x: x.mean(),
constructor)
def test_matmul(self, constructor):
constructor[1][-1] = constructor[1][
-1][::-1] # change the size of last argument to match matmul
evaluate_function_with_pytorch(operator.matmul, operator.matmul,
constructor)
def test_pow(self, constructor):
constructor[
0] = np.random.rand # overriding function to keep the tests real valued
evaluate_function_with_pytorch(operator.pow, operator.pow, constructor)
def test_div(self, constructor):
evaluate_function_with_pytorch(operator.truediv, operator.truediv,
constructor)
def test_mul(self, constructor):
evaluate_function_with_pytorch(operator.mul, operator.mul, constructor)
def test_sub(self, constructor):
evaluate_function_with_pytorch(operator.sub, operator.sub, constructor)
def test_sum(self, constructor):
evaluate_function_with_pytorch(operator.add, operator.add, constructor)
def test_log(self, constructor):
constructor[
0] = np.random.rand # overriding function to keep the tests real valued
evaluate_function_with_pytorch(lambda x: x.log(), lambda x: x.log(),
constructor)
def test_softmax(self, constructor):
# adding extra pow function to make the softmax derivatives bigger
sl_function = lambda x: (sl_F.softmax(x)**4.2).sum()
torch_function = lambda x: (torch_F.softmax(x, dim=-1)**4.2).sum()
evaluate_function_with_pytorch(sl_function, torch_function,
constructor)
def test_relu(self, constructor):
evaluate_function_with_pytorch(sl_F.relu, torch_F.relu, constructor)