Производительность больше вам не помешает

neuro_defs.py

@@ -1,32 +1,86 @@
-import math
-import random
-from auto_diff import auto_diff
 
-def func_active(x):
-	return
+from auto_diff import auto_diff
+import classes
+
+def sigmoid(x: auto_diff.Node):
+    return 1 / (1 + (-x).exp())
+
+def tanh(x: auto_diff.Node):
+	return (x.exp() - (-x).exp()) / (x.exp() + (-x).exp())
 
 class SimpleNN:
 	def __init__(self):
-		self.w_out = auto_diff.Node(random.uniform(-1, 1), label="w_out")
-		self.b_out = auto_diff.Node(random.uniform(-1, 1), label="b_out")
-		self.lr = 0.02   # скорость обучения
+		#111 - 1 слой, 1 нейрон, 1 вес
+		# self.w111 = auto_diff.Node(random.uniform(-1, 1))
+		# self.w112 = auto_diff.Node(random.uniform(-1, 1))
+		# self.b11 = auto_diff.Node(random.uniform(-1, 1))
 
-	def forward(self, x):
+		# self.w121 = auto_diff.Node(random.uniform(-1, 1))
+		# self.w122 = auto_diff.Node(random.uniform(-1, 1))
+		# self.b12 = auto_diff.Node(random.uniform(-1, 1))
+
+		# self.w131 = auto_diff.Node(random.uniform(-1, 1))
+		# self.w132 = auto_diff.Node(random.uniform(-1, 1))
+		# self.b13 = auto_diff.Node(random.uniform(-1, 1))
+
+		# self.w141 = auto_diff.Node(random.uniform(-1, 1))
+		# self.w142 = auto_diff.Node(random.uniform(-1, 1))
+		# self.b14 = auto_diff.Node(random.uniform(-1, 1))
+
+
+
+		# self.w211 = auto_diff.Node(random.uniform(-1, 1))
+		# self.w212 = auto_diff.Node(random.uniform(-1, 1))
+		# self.w213 = auto_diff.Node(random.uniform(-1, 1))
+		# self.w214 = auto_diff.Node(random.uniform(-1, 1))
+		# self.b21 = auto_diff.Node(random.uniform(-1, 1))
+
+		# self.w221 = auto_diff.Node(random.uniform(-1, 1))
+		# self.w222 = auto_diff.Node(random.uniform(-1, 1))
+		# self.w223 = auto_diff.Node(random.uniform(-1, 1))
+		# self.w224 = auto_diff.Node(random.uniform(-1, 1))
+		# self.b22 = auto_diff.Node(random.uniform(-1, 1))
+
+
+
+		# self.w1_out = auto_diff.Node(random.uniform(-1, 1))
+		# self.w2_out = auto_diff.Node(random.uniform(-1, 1))
+		# self.b_out = auto_diff.Node(random.uniform(-1, 1))
+
+		self.network = classes.NeuronNetwork(4, 2, 2)
+
+		self.lr = 0.1   # скорость обучения
+
+	def forward(self, x1, x2):
 		# прямой проход
-		self.z1 = self.w_out * x + self.b_out
-		return self.z1
 
-	def backward(self, x, y):
+		# #скрытые слои
+		# self.h11 = tanh(self.w111*x1 + self.w112*x2 + self.b11)
+		# self.h12 = tanh(self.w121*x1 + self.w122*x2 + self.b12)
+		# self.h13 = tanh(self.w131*x1 + self.w132*x2 + self.b13)
+		# self.h14 = tanh(self.w141*x1 + self.w142*x2 + self.b14)
+
+		# self.h21 = tanh(self.w211*self.h11 + self.w212*self.h12 + self.w213*self.h13 + self.w214*self.h14 + self.b21)
+		# self.h22 = tanh(self.w221*self.h11 + self.w222*self.h12 + self.w223*self.h13 + self.w224*self.h14 + self.b22)
+
+		# #выходной слой
+		# self.a1 = sigmoid(self.w1_out*self.h21 + self.w2_out*self.h22 + self.b_out)
+
+		self.a1 = self.network.forward(sigmoid, x1, x2)
+
+		return self.a1
+
+	def backward(self, y):
 		# вычисляем ошибку
-		error = (self.z1 - y)**2  # dL/da2
+		error = (self.a1 - y)**2  # dL/da2
 
 		auto_diff.backward(error)
-		self.w_out = auto_diff.Node(float(self.w_out) - self.lr * self.w_out.grad, label="w_out")
-		self.b_out = auto_diff.Node(float(self.b_out) - self.lr * self.b_out.grad, label="b_out")
+
+		self.network.update_weights(self.lr)
 
 	def train(self, dataset, answs, epochs=1000):
 		for _ in range(epochs):
 			for i in range(len(dataset)):
-				self.forward(dataset[i])
-				self.backward(dataset[i], answs[i])
+				self.forward(*dataset[i])
+				self.backward(answs[i])