Remove lfs

.gitattributes

@@ -1,2 +1 @@
 .csv filter=lfs diff=lfs merge=lfs -text
-resources/mnist/mnist_train.csv filter=lfs diff=lfs merge=lfs -text

nnf/activations/__init__.py

@@ -3,5 +3,7 @@
 from nnf.activations.softmax import Softmax
 from nnf.activations.sigmoid import Sigmoid
 from nnf.activations.leaky_relu import LeakyReLU
+from nnf.activations.tanh import Tanh
 
-__all__ = ['Activation', 'ReLU', 'Softmax', 'Sigmoid', 'LeakyReLU']
+
+__all__ = ['Activation', 'ReLU', 'Softmax', 'Sigmoid', 'LeakyReLU', 'Tanh']

nnf/activations/base.py

@@ -13,4 +13,7 @@ def forward(self, inputs):
         raise NotImplementedError
 
     def backward(self, dvalues):
-        raise NotImplementedError
+        raise NotImplementedError
+
+    def get_params(self):
+        return super().get_params()

nnf/activations/leaky_relu.py

@@ -1,4 +1,6 @@
 import numpy as np
+from typing import override, Dict
+
 from nnf.activations.base import Activation
 
 class LeakyReLU(Activation):
@@ -50,3 +52,18 @@ def backward(self, dvalues):
         # For inputs <= 0, multiply the gradient by alpha
         self.dinputs[self.inputs <= 0] *= self.alpha
         return self.dinputs
+
+    @override
+    def get_params(self):
+        return {
+            "type"  : "LeakyReLU",
+            "attrs" : {
+                "alpha": self.alpha
+            }
+        }
+
+    @override
+    def set_params(self, params : Dict):
+        for key, val in params.items():
+            setattr(self, key, val)
+

nnf/activations/relu.py

@@ -1,4 +1,6 @@
 import numpy as np
+from typing import override
+
 from nnf.activations.base import Activation
 
 class ReLU(Activation):
@@ -25,4 +27,11 @@ def backward(self, dvalues):
 
         self.dinputs = dvalues.copy()
         self.dinputs[self.inputs <= 0] = 0
-        return self.dinputs
+        return self.dinputs
+
+    @override
+    def get_params(self):
+        return {
+            "type" : "ReLU",
+            "attrs" : {}
+        }

nnf/activations/sigmoid.py

@@ -1,4 +1,6 @@
 import numpy as np
+from typing import override
+
 from nnf.activations.base import Activation
 
 class Sigmoid(Activation):
@@ -26,4 +28,11 @@ def backward(self, dvalues):
 
         # derivative of the sigmoid: f'(x) = f(x) * (1 - f(x))
         self.dinputs = dvalues * (self.output * (1 - self.output))
-        return self.dinputs
+        return self.dinputs
+
+    @override
+    def get_params(self):
+        return {
+            "type" : "Sigmoid",
+            "attrs" : {}
+        }

nnf/activations/softmax.py

@@ -33,6 +33,8 @@
 """
 
 import numpy as np
+from typing import override
+
 from nnf.activations.base import Activation
 
 class Softmax(Activation):
@@ -84,3 +86,10 @@ def backward(self, dvalues):
         """
         self.dinputs = dvalues  # usually combined with loss
         return self.dinputs
+
+    @override
+    def get_params(self):
+        return {
+            "type" : "Softmax",
+            "attrs" : {}
+        }

nnf/activations/tanh.py

@@ -31,6 +31,8 @@
 """
 
 import numpy as np
+from typing import override
+
 from nnf.activations.base import Activation
 
 class Tanh(Activation):
@@ -70,3 +72,10 @@ def backward(self, dvalues):
         """
         self.dinputs = dvalues * (1 - self.output ** 2)
         return self.dinputs
+
+    @override
+    def get_params(self):
+        return {
+            "type"  : "Tanh",
+            "attrs" : {}
+        }

nnf/layers/base.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from typing import Dict
 
 class Layer(ABC):
     def __init__(self):
@@ -19,4 +20,10 @@ def forward(self, inputs):
 
     @abstractmethod
     def backward(self, dvalues):
+        pass
+
+    def get_params(self):
+        return {}
+
+    def set_params(self, params : Dict):
         pass

nnf/layers/dense.py

@@ -1,4 +1,6 @@
 import numpy as np
+from typing import override, Dict, List
+
 from nnf.layers.base import Layer
 
 class Dense(Layer):
@@ -8,12 +10,12 @@ class Dense(Layer):
     --------------------------------------------
     """
 
-    def __init__(self, n_inputs, n_neurons):
+    def __init__(self, n_inputs = 1, n_neurons = 1):
         """
         The function initializes weights with random values and biases with zeros.
         """
         super().__init__()
-
+        
         self.n_inputs = n_inputs
         self.n_neurons = n_neurons
 
@@ -30,6 +32,7 @@ def __init__(self, n_inputs, n_neurons):
         self.dbiases = None
 
         # Parameters
+
         self.params = self.weights.size + self.biases.size
 
     def forward(self, inputs):
@@ -52,4 +55,31 @@ def backward(self, dvalues):
         self.dbiases = np.sum(dvalues, axis=0, keepdims=True)
 
         self.dinputs = np.dot(dvalues, self.weights.T)
-        return self.dinputs
+        return self.dinputs
+
+    @override
+    def get_params(self):
+        return {
+            "type"  : "Dense",
+            "attrs" : {
+                "n_inputs"  : self.n_inputs,
+                "n_neurons" : self.n_neurons,
+                "trainable" : self.trainable,
+                "weights"   : self.weights,
+                "biases"    : self.biases,
+                "dweights"  : self.dweights,
+                "dbiases"   : self.dbiases
+            }  
+        }
+
+    @override
+    def set_params(self, params : Dict):
+        for key, val in params.items():
+            # If the key is one of the specified attributes (e.g., "n_inputs", "n_neurons", etc.)
+            # and the value is a list, convert the value to a NumPy array and set it as an attribute.
+            # Basically, to how they were originally!
+            if key in ("n_inputs", "n_neurons", "weights", "biases", "dweights", "dbiases") and type(key) == List:
+                setattr(self, key, np.array(val))
+            else:
+                setattr(self, key, val)
+