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import random | |
import math | |
import json | |
INPUTS = [[0,0],[0,1],[1,0],[1,1]] | |
OUTPUTS = [[0],[1],[1],[0]] | |
EPOCHS = 1000000 | |
ALPHAS = 20 | |
WEPOCHS = EPOCHS // 100 | |
VARIANCE_W = 0.5 | |
VARIANCE_B = 0 | |
w11 = random.uniform(-VARIANCE_W,VARIANCE_W) | |
w21 = random.uniform(-VARIANCE_W,VARIANCE_W) | |
b1 = VARIANCE_B | |
w12 = random.uniform(-VARIANCE_W,VARIANCE_W) | |
w22 = random.uniform(-VARIANCE_W,VARIANCE_W) | |
b2 = VARIANCE_B | |
w13 = random.uniform(-VARIANCE_W,VARIANCE_W) | |
w23 = random.uniform(-VARIANCE_W,VARIANCE_W) | |
b3 = VARIANCE_B | |
o1 = random.uniform(-VARIANCE_W,VARIANCE_W) | |
o2 = random.uniform(-VARIANCE_W,VARIANCE_W) | |
o3 = random.uniform(-VARIANCE_W,VARIANCE_W) | |
ob = VARIANCE_B | |
## Tudo a 0.5 | |
# VARIANCE_W = 0.5 | |
# VARIANCE_B = 1 | |
# w11 = VARIANCE_W | |
# w21 = VARIANCE_W | |
# b1 = VARIANCE_B | |
# w12 = VARIANCE_W | |
# w22 = VARIANCE_W | |
# b2 = VARIANCE_B | |
# w13 = VARIANCE_W | |
# w23 = VARIANCE_W | |
# b3 = VARIANCE_B | |
# o1 = VARIANCE_W | |
# o2 = VARIANCE_W | |
# o3 = VARIANCE_W | |
# ob = VARIANCE_B | |
def sigmoid(x): | |
return 1.0 / (1.0 + math.exp(-x)) | |
def sigmoid_prime(x): # x already sigmoided | |
return x * (1 - x) | |
def relu(x): | |
return max(0,x) | |
def relu_prime(x): | |
return 1 if x>0 else 0 | |
def tanh(x): | |
return math.tanh(x) | |
def tanh_prime(x): | |
return 1 - x**2 | |
def softmax(x): | |
return math.exp(x) / (math.exp(x) + 1) | |
def softmax_prime(x): | |
return x * (1 - x) | |
def predict(i1, i2, activation=sigmoid): | |
s1 = w11 * i1 + w21 * i2 + b1 | |
# s1 = sigmoid(s1) | |
s1 = activation(s1) | |
s2 = w12 * i1 + w22 * i2 + b2 | |
# s2 = sigmoid(s2) | |
s2 = activation(s2) | |
s3 = w13 * i1 + w23 * i2 + b3 | |
# s3 = sigmoid(s3) | |
s3 = activation(s3) | |
output = s1 * o1 + s2 * o2 + s3 * o3 + ob | |
# output = sigmoid(output) | |
output = activation(output) | |
return output | |
def learn(i1,i2,target, activation, activation_prime, alpha=0.2): | |
global w11,w21,b1,w12,w22,b2,w13,w23,b3 | |
global o1,o2,o3,ob | |
s1 = w11 * i1 + w21 * i2 + b1 | |
# s1 = sigmoid(s1) | |
s1 = activation(s1) | |
s2 = w12 * i1 + w22 * i2 + b2 | |
# s2 = sigmoid(s2) | |
s2 = activation(s2) | |
s3 = w13 * i1 + w23 * i2 + b3 | |
# s3 = sigmoid(s3) | |
s3 = activation(s3) | |
output = s1 * o1 + s2 * o2 + s3 * o3 + ob | |
# output = sigmoid(output) | |
output = activation(output) | |
error = target - output | |
# derror = error * sigmoid_prime(output) | |
derror = error * activation_prime(output) | |
# ds1 = derror * o1 * sigmoid_prime(s1) | |
ds1 = derror * o1 * activation_prime(s1) | |
# ds2 = derror * o2 * sigmoid_prime(s2) | |
ds2 = derror * o2 * activation_prime(s2) | |
# ds3 = derror * o3 * sigmoid_prime(s3) | |
ds3 = derror * o3 * activation_prime(s3) | |
o1 += alpha * s1 * derror | |
o2 += alpha * s2 * derror | |
o3 += alpha * s3 * derror | |
ob += alpha * derror | |
w11 += alpha * i1 * ds1 | |
w21 += alpha * i2 * ds1 | |
b1 += alpha * ds1 | |
w12 += alpha * i1 * ds2 | |
w22 += alpha * i2 * ds2 | |
b2 += alpha * ds2 | |
w13 += alpha * i1 * ds3 | |
w23 += alpha * i2 * ds3 | |
b3 += alpha * ds3 | |
def train(epochs=EPOCHS, alpha=ALPHAS): | |
modelo = None | |
for epoch in range(1,epochs+1): | |
indexes = [0,1,2,3] | |
random.shuffle(indexes) | |
for j in indexes: | |
learn(INPUTS[j][0],INPUTS[j][1],OUTPUTS[j][0], activation=sigmoid, activation_prime=sigmoid_prime, alpha=alpha) | |
if epoch%WEPOCHS == 0: | |
cost = 0 | |
for j in range(4): | |
o = predict(INPUTS[j][0],INPUTS[j][1], activation=sigmoid) | |
cost += (OUTPUTS[j][0] - o) ** 2 | |
cost /= 4 | |
print("epoch", epoch, "mean squared error:", cost) | |
modelo = { | |
"w11": w11, | |
"w21": w21, | |
"b1": b1, | |
"w12": w12, | |
"w22": w22, | |
"b2": b2, | |
"w13": w13, | |
"w23": w23, | |
"b3": b3, | |
"o1": o1, | |
"o2": o2, | |
"o3": o3, | |
"ob": ob | |
} | |
return modelo | |
def save_model(modelo, filename): | |
with open(filename, 'w') as json_file: | |
json.dump(modelo, json_file) | |
## Main | |
def main(): | |
# Train model | |
modelo = train() | |
print(modelo) | |
# Save model to file | |
save_model(modelo, "modelo.json") | |
for i in range(4): | |
result = predict(INPUTS[i][0],INPUTS[i][1], activation=sigmoid) | |
print("for input", INPUTS[i], "expected", OUTPUTS[i][0], "predicted", f"{result:4.4}", "which is", "correct" if round(result)==OUTPUTS[i][0] else "incorrect") | |
# print("for input", INPUTS[i], "expected", OUTPUTS[i][0], "predicted", result, "which is", "correct" if round(result)==OUTPUTS[i][0] else "incorrect") | |
if __name__ == "__main__": | |
main() |