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Jonas Braus
2026-03-04 23:27:19 +01:00
commit d95ba0a638
6 changed files with 209 additions and 0 deletions
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connection.py
+23
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import random
class Connection:
def __init__(self, in_, out_):
self.weight = random.uniform(-1.0, 1.0)
self.in_ = in_
self.out_ = out_
self.value = 0.0
def transfer(self, value:float):
self.value = value
def adjust(self, delta:float, learning_rate:float):
old_weight = self.weight
gradient = delta * self.value
self.weight += learning_rate * gradient
if self.in_ is None:
return
error_for_prev = delta * old_weight
self.in_.adjust(error_for_prev, learning_rate)
data.train
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[
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]
main.py
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import string
from network import Network
from trainer import Trainer
network = Network()
network.build_layers(3, 36, 3, 12)
network.connect_layers()
trainer = Trainer(network, "./data.train")
trainer.train(20000)
print("Erkenne die Art eines zeichens (Konsontant, Vokal oder Zahl).")
while True:
inpt = input("Dein Zeichen: ").lower()
vec = [0 for _ in range(0, 36)]
refrence = string.ascii_lowercase + string.digits
vec[refrence.index(inpt)] = 1
output = network.fire(vec)
print(f"Konsontant: {round(output[0], 2)} | Vokal: {round(output[1], 2)} | Zahl: {round(output[2], 2)}")
if max(output) == output[0]:
print("AI Denkt: Konsontant")
elif max(output) == output[1]:
print("AI Denkt: Vokal")
else:
print("AI Denkt: Zahl")
network.py
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from neuron import Neuron
from connection import Connection
import random
import json
class Network():
def __init__(self):
self.layers = []
def build_layers(self, count:int, input_size:int, output_size:int, layer_size:int):
for layer in range(0, count):
self.layers.append([])
for _ in range(0, input_size if layer == 0 else output_size if layer == count - 1 else layer_size):
self.layers[layer].append(Neuron())
def _connect_layer(self, last_layer, current_layer):
for last_neuron in last_layer:
for current_neuron in current_layer:
connection = Connection(last_neuron, current_neuron)
last_neuron.connect_out(connection)
current_neuron.connect_in(connection)
def connect_layers(self):
for i in range(1, len(self.layers)):
last_layer = self.layers[i-1]
current_layer = self.layers[i]
self._connect_layer(last_layer, current_layer)
def fire(self, vec):
for l in range(0, len(self.layers)):
layer = self.layers[l]
for i in range(0, len(layer)):
neuron = layer[i]
neuron.calc(vec[i] if l == 0 else None)
output = []
for i in range(0, len(self.layers[-1])):
layer = self.layers[-1]
output.append(layer[i].value)
return output
neuron.py
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import random
import math
class Neuron:
def __init__(self):
self.bias = random.uniform(-1.0, 1.0)
self.in_ = []
self.out_ = []
self.value = 0.0
def connect_in(self, connection):
self.in_.append(connection)
def connect_out(self, connection):
self.out_.append(connection)
def calc(self, input_val=None):
if input_val is not None:
self.value = input_val
else:
z = self.bias
for connection in self.in_:
z += connection.value * connection.weight
z = max(-500, min(500, z))
self.value = 1.0 / (1.0 + math.exp(-z))
for connection in self.out_:
connection.transfer(self.value)
def adjust(self, error:float, learning_rate:float = 0.1):
derivative = self.value * (1.0 - self.value)
delta = error * derivative
self.bias += learning_rate * delta
for connection in self.in_:
connection.adjust(delta, learning_rate)
trainer.py
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from network import Network
import json
import random
class Trainer:
def __init__(self, network: Network, train_file):
self.network = network
with open(train_file, "r") as file:
f_data = file.read()
self.data = json.loads(f_data)
def adjust(self, check_vec, layer_idx):
layer = self.network.layers[layer_idx]
for i, neuron in enumerate(layer):
error = check_vec[i] - neuron.value
neuron.adjust(error)
def train(self, iters):
for i in range(0, iters):
print(f"Train {(i / iters) * 100}%")
sample = random.choice(self.data)
in_vec = sample[0]
check_vec = sample[1]
self.network.fire(in_vec)
self.adjust(check_vec, len(self.network.layers) - 1)