Enhance activations testing (#992)

test/specialfunctions/CMakeLists.txt

@@ -9,4 +9,6 @@ set(fppFiles
 fypp_f90("${fyppFlags}" "${fppFiles}" outFiles)
 
 ADDTEST(specialfunctions_gamma)
-ADDTEST(specialfunctions_activations)
+ADDTEST(specialfunctions_activations)
+
+target_link_libraries(specialfunctions_activations PRIVATE stdlib_io)

test/specialfunctions/generate_activation_data.py

@@ -0,0 +1,47 @@
+# --- python script: generate_activation_data.py ---
+# place this in test/specialfunctions/
+
+import numpy as np
+import torch
+import os
+
+# create a directory to store the reference data
+data_dir = 'data'
+if not os.path.exists(data_dir):
+    os.makedirs(data_dir)
+
+# define a range of inputs to test
+x_np = np.linspace(-10.0, 10.0, num=1001, dtype=np.float64)
+x_torch = torch.tensor(x_np, requires_grad=True)
+
+# map the activation functions available in stdlib to their PyTorch equivalents
+activations = {
+    "sigmoid": torch.sigmoid,
+    "tanh": torch.tanh,
+    "relu": torch.relu,
+    "elu": torch.nn.functional.elu,
+    "selu": torch.nn.functional.selu,
+    "softplus": torch.nn.functional.softplus,
+    "softsign": torch.nn.functional.softsign,
+}
+
+print("Generating reference data...")
+
+for name, func in activations.items():
+    # Reset gradients for each function
+    if x_torch.grad is not None:
+        x_torch.grad.zero_()
+
+    # calculate the function and its derivative
+    y = func(x_torch)
+    y.sum().backward()
+    derivative = x_torch.grad.numpy()
+
+    # save input, output, and derivative as .npy files
+    np.save(f'{data_dir}/{name}_input.npy', x_np)
+    np.save(f'{data_dir}/{name}_output.npy', y.detach().numpy())
+    np.save(f'{data_dir}/{name}_derivative.npy', derivative)
+
+    print(f"Generated data for: {name}")
+
+print("All reference data generated successfully.")

test/specialfunctions/test_specialfunctions_activations.fypp

@@ -6,6 +6,7 @@ module test_specialfunctions_activation
     use stdlib_kinds
     use stdlib_specialfunctions
     use stdlib_math, only: linspace
+        use stdlib_io_npy_load, only: load_npy
     implicit none
     private
 
@@ -31,7 +32,8 @@ contains
             new_unittest("sigmoid", test_sigmoid),          &
             new_unittest("silu"   , test_silu),             &
             new_unittest("softmax", test_softmax),          &
-            new_unittest("logsoftmax", test_logsoftmax)     &
+            new_unittest("logsoftmax", test_logsoftmax),     &
+            new_unittest("sigmoid_pytorch", test_sigmoid_pytorch) &
             ]
     end subroutine collect_specialfunctions_activation
 
@@ -382,6 +384,29 @@ contains
         #:endfor
     end subroutine test_logsoftmax
 
+    subroutine test_sigmoid_pytorch(error)
+        type(error_type), allocatable, intent(out) :: error
+
+        real(dp), allocatable :: x(:), y_ref(:), y_calc(:)
+        integer :: n
+
+        ! 1. Load the data generated by Python
+        ! Make sure you ran 'python generate_data.py' in this folder first!
+        call load_npy('data/sigmoid_input.npy', x)
+        call load_npy('data/sigmoid_output.npy', y_ref)
+
+        n = size(x)
+        allocate(y_calc(n))
+
+        ! 2. Calculate using stdlib
+        y_calc = sigmoid(x)
+
+        ! 3. Compare
+        call check(error, norm2(y_calc - y_ref) < 1.0e-9_dp, &
+                   "Sigmoid mismatch against PyTorch reference")
+        if (allocated(error)) return
+
+    end subroutine test_sigmoid_pytorch
 
 end module test_specialfunctions_activation