softmax

layer.py

@@ -177,7 +177,36 @@ def relu(v):
     relu_f = lambda x: np.max([x,0])
     relu_diff = lambda x: 1 if x > 0 else 0
     return FunctionOp(relu_f,relu_diff,"Relu",v)
+#row vector
+def softmaxHelp(i):
+    e = np.exp(i)
+    sumofe = np.sum(e,axis=e.ndim - 1)
+    sumofe = sumofe.reshape(*sumofe.shape,1)
+    return e / sumofe
+class SoftmaxWithNegativeLogLikelihood(OpTree):
+    #row vector
+    def __init__(self, i, y):
+        super().__init__()
+        self.i = i
+        self.s = softmaxHelp(i)
+        self.v = -y*self.s
     
+    def __str__(self):
+        return f"SoftmaxWithNegativeLogLikelihoodOp"
+
+    def mermaid_graph(self,writer):
+        self.i.mermaid_graph(writer)
+        writer.write(f'{id(self.i)}-->{id(self)}[SoftmaxWithNegativeLogLikelihoodOp]\n')
+
+    def numpy(self):
+        return self.v
+    
+    def softmax_numpy(self):
+        return self.s
+
+    def backprop(self,seed):
+        self.i.backprop(seed * (self.s-y))
+
 class Variable(OpTree):
     def __init__(self,x):
         super().__init__()

p4.py

@@ -0,0 +1,10 @@
+from sklearn import datasets
+import numpy as np
+X, y = datasets.fetch_openml('mnist_784', version=1, return_X_y=True, cache=True, as_frame= False)
+print(X,y)
+
+gen:np.random.Generator = np.random.default_rng()
+
+input_var = Variable(X)
+weight = Variable(gen.normal(100,784))
+bias = Variable(np.array([1]))