Revert to rock solid foundation approach for module imports

- Fix module imports to use tinytorch.core.* instead of local module imports
- Activations module now imports from tinytorch.core.tensor for stability
- Layers module imports from tinytorch.core.tensor and tinytorch.core.activations
- Test files updated to use main package imports for dependencies
- This ensures students can focus on current module without dependency issues
- Previous modules are 'locked in' and guaranteed to work
- Mirrors real-world usage patterns like PyTorch
- Maintains educational progression while ensuring system stability

modules/activations/activations_dev.py

@@ -32,10 +32,12 @@ Every neural network you've heard of - from image recognition to language models
 #| export
 import math
 import numpy as np
+import matplotlib.pyplot as plt
+import os
 import sys
 from typing import Union, List
 
-# Import our Tensor class
+# Import our Tensor class from the main package (rock solid foundation)
 from tinytorch.core.tensor import Tensor
 
 # %%
@@ -43,7 +45,6 @@ from tinytorch.core.tensor import Tensor
 #| export
 def _should_show_plots():
     """Check if we should show plots (disable during testing)"""
-    import os
     # Check multiple conditions that indicate we're in test mode
     is_pytest = (
         'pytest' in sys.modules or
@@ -65,7 +66,6 @@ def visualize_activation_function(activation_fn, name: str, x_range: tuple = (-5
         return
         
     try:
-        import matplotlib.pyplot as plt
         
         # Generate input values
         x_vals = np.linspace(x_range[0], x_range[1], num_points)

modules/activations/tests/test_activations.py

@@ -1,29 +1,19 @@
 """
-Test suite for the TinyTorch Activations module.
-
-This test suite validates the mathematical correctness of activation functions:
-- ReLU: f(x) = max(0, x)
-- Sigmoid: f(x) = 1 / (1 + e^(-x))
-- Tanh: f(x) = tanh(x)
-
-Tests focus on:
-1. Mathematical correctness
-2. Numerical stability
-3. Edge cases
-4. Shape preservation
-5. Type consistency
+Test suite for the activations module.
+This tests the student implementations to ensure they work correctly.
 """
 
 import pytest
 import numpy as np
-import math
-from tinytorch.core.tensor import Tensor
-
-# Import the activation functions
 import sys
 import os
-sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
-from activations_dev import ReLU, Sigmoid, Tanh
+
+# Import from the main package (rock solid foundation)
+from tinytorch.core.tensor import Tensor
+
+# Import our implementations from the local module for testing
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+from activations_dev import ReLU, Sigmoid, Tanh, Softmax
 
 
 class TestReLU:

modules/layers/layers_dev.py

@@ -67,15 +67,11 @@ import numpy as np
 import math
 import sys
 from typing import Union, Optional, Callable
+
+# Import from the main package (rock solid foundation)
 from tinytorch.core.tensor import Tensor
-
-# Import activation functions from the activations module
 from tinytorch.core.activations import ReLU, Sigmoid, Tanh
 
-# Import our Tensor class
-# sys.path.append('../../')
-# from modules.tensor.tensor_dev import Tensor
-
 # print("🔥 TinyTorch Layers Module")
 # print(f"NumPy version: {np.__version__}")
 # print(f"Python version: {sys.version_info.major}.{sys.version_info.minor}")