debut nouveau model

sobek/activationFunctions.py

@@ -0,0 +1,23 @@
+class activationFunction:
+    def applyTo(value):
+        pass
+
+    def applyDerivateTo(value):
+        pass
+
+class sigmoid(activationFunction):
+    def applyTo(value):
+        return 1.0/(1.0+np.exp(-value))
+
+    def applyDerivateTo(value):
+        return sigmoid.applyTo(value) * (1 - sigmoid.applyTo(value))
+
+class reLu(activationFunction):
+    def applyTo(value):
+        return max(0, value)
+
+    def applyDerivateTo(value):
+        return 0 if (value < 0) else 1
+
+class softMax(activationFunction):
+    pass

sobek/layers.py

@@ -0,0 +1,30 @@
+import numpy as np
+
+class layer:
+    def __init__(self, neurons, activationFunction)
+        self.neurons = neurons
+        self.activationFunction = activationFunction
+
+    def process(_input, __storeValues=False)
+
+class dense(layer):
+    def process(_input, __storeValues=False):
+        
+        _input = np.dot(layerWeights, _input)
+        _input = np.add(_input, layerBias)
+
+        if (__storeValues):
+            self.activation = _input
+
+        _input = self.activationFunction.applyTo(_input)
+
+        if (__storeValues):
+            self.output = _input
+
+        return _input
+
+class convolution(layer):
+    pass
+
+class flatten(layer):
+    pass

sobek/network.py

@@ -194,3 +194,4 @@ class model:
         self.layers = []
 
     def add(layerType, activation):
+        self.layers.add()