Производительность больше вам не помешает

classes.py

@@ -1,4 +1,6 @@
+import copy
 import random
+from auto_diff import auto_diff
 
 class DataSet:
 	def __init__(self, func, N=1000) -> None:
@@ -7,12 +9,97 @@ class DataSet:
 		self.test = []
 		self.test_answs = []
 
-		for i in range(N//5*4):
-			x = random.uniform(1, 9)
-			self.train.append(x)
-			self.train_answs.append(func(x))
+		def gen_data():
+			x1 = random.uniform(-1, 1)
+			x2 = random.uniform(-1, 1)
+			y = func(x1, x2)
+			return (x1, x2), y
+
+		for i in range(N):
+			(x1, x2), y = gen_data()
+			self.train.append((x1, x2))
+			self.train_answs.append(y)
 
 		for i in range(N//5):
-			x = random.uniform(1, 9)
-			self.test.append(x)
-			self.test_answs.append(func(x))
+			(x1, x2), y = gen_data()
+			self.test.append((x1, x2))
+			self.test_answs.append(y)
+
+
+	def __repr__(self):
+		return f"test: {self.test}\nansws: {self.test_answs}"
+	
+
+class Neuron:
+	def __init__(self, weights_num: int) -> None:
+		self.weights = [auto_diff.Node(random.uniform(-1, 1)) for _ in range(weights_num)]
+		self.b = auto_diff.Node(random.uniform(-1, 1))
+
+	def __repr__(self, debug: bool = False) -> str:
+		if not debug:
+			return f"<weights = {len(self.weights)} b = {float(self.b)}>"
+		else:
+			return f"<weights = {self.weights} b = {float(self.b)}>"
+		
+	def update_weights(self, lr):
+		for i in range(len(self.weights)):
+			self.weights[i] = auto_diff.update_weights(self.weights[i], lr)
+
+		self.b = auto_diff.update_weights(self.b, lr)
+
+def my_sum(array: list) -> int:
+	result = 0
+	for i in array:
+		result += i
+	return result
+
+class NeuronNetwork:
+	def __init__(self, neurons_num: int, layers_num: int, inputs_num: int, outputs_num: int = 1) -> None:
+		'''
+		neurons_num: количество нейронов на каждом слое\n
+		layers_num: количество слоёв\n
+		inputs_num: количество входных нейронов\n
+		outputs_num: количество выходных нейронов (по умолчанию 1)\n
+		'''
+		neurons = []
+		for layer in range(layers_num+1):
+			neurons.append([])
+
+			if layer == layers_num:
+				for neuron in range(outputs_num):
+					neurons[layer].append(Neuron(neurons_num))
+				continue
+
+			for neuron in range(neurons_num):
+				if layer == 0:
+					neurons[layer].append(Neuron(inputs_num))
+				else:
+					neurons[layer].append(Neuron(neurons_num))
+		
+		self.neurons = neurons
+
+	def forward(self, func, *args, func_out=None):
+		if func_out is None:
+			func_out = func
+
+
+		prev_out = [*args]
+		out = []
+		for layer in self.neurons[:-1]:
+			for neuron in layer:
+				out.append(func(my_sum([prev_out[i]*neuron.weights[i] for i in range(len(prev_out))]) + neuron.b))
+			prev_out = copy.deepcopy(out)
+			out = []
+
+		for neuron in self.neurons[-1]:
+			out.append(func_out(my_sum([prev_out[i]*neuron.weights[i] for i in range(len(prev_out))]) + neuron.b))
+		
+		return out[0]
+
+	def update_weights(self, lr=0.1):
+		for i in range(len(self.neurons)):
+			for j in range(len(self.neurons[i])):
+				self.neurons[i][j].update_weights(lr)
+
+if __name__ == "__main__":
+	print(NeuronNetwork(4, 4, 2).neurons)