def calculate(inlayer3, weights1, bias1, weights2, bias2, error1, goku, q):
hlayer1 = [0, 0, 0, 0, 0, 0, 0, 0]
hlayer1 = nn.cal_hid(inlayer3, weights1, hlayer1, bias1)
y = nn.cal_out(hlayer1, weights2, bias2)
error = nn.cal_error(y, t)
if error1 > error:
goku = q
error1 = error
return error1, goku
def nn1(weights1, weights2, bias1, bias2):
import neural_network as nn
f = open('move1.txt', 'r')
inlayer = [0, 0, 0, 0, 0, 0, 0, 0, 0]
# bias1 = [] # Bias between Input and Hidden Layer
# bias2 = 0.0 # Bias between Hidden layer and Output
olayerlayer = 0.0
# weights1 = [] # input to hidden1
# weights2 = [] # hidden1 to hidden2
y = 0.0
error = 0.0
cost = 0.0
'''
weights1 = nn.w1(weights1)
weights2 = nn.w2(weights2)
bias1, bias2 = nn.bias(bias1, bias2)
weights1, weights2, bias1, bias2 = test.w()'''
for k in range(50):
cost = 0.0
f.seek(0, 0)
for j in range(100000):
count1 = f.readline()
count = str()
for i in range(len(count1) - 1):
count += count1[i]
count = count.split(',')
l = []
for i in range(len(count)):
l.append(int(count[i]))
# print(l)
# print(l)
inlayer = []
for i in range(9):
inlayer.append(l[i])
t = l[9]
# print(inlayer, t)
hlayer1 = [0, 0, 0, 0, 0, 0, 0, 0]
hlayer1 = nn.cal_hid(inlayer, weights1, hlayer1, bias1)
y = nn.cal_out(hlayer1, weights2, bias2)
error = nn.cal_error(y, t)
if k == 49:
errors1.append(error)
cost += error
weights1, weights2, bias1, bias2 = nn.Adjust_weights(
inlayer, weights1, bias1, hlayer1, weights2, bias2, y, t)
del (count, count1)
print("Epoch ", k + 1, " : ")
print("\tCost : ", cost)
errors.append(cost)
f.close()
return weights1, weights2, bias1, bias2
count = str()
for i in range(len(count1) - 1):
count += count1[i]
count = count.split(',')
l = []
for i in range(len(count)):
l.append(int(count[i]))
# print(l)
inlayer = []
for i in range(9):
inlayer.append(l[i])
t = l[9]
# print(inlayer, t)
hlayer1 = [0, 0, 0, 0, 0, 0, 0, 0]
hlayer1 = nn.cal_hid(inlayer, weights1, hlayer1, bias1)
y, olayer = nn.cal_out(hlayer1, weights2, y, bias2)
error = nn.cal_error(y, t)
if k == 49999:
errors1.append(error)
cost += error
weights1, weights2, bias1, bias2 = nn.Adjust_weights(
inlayer, weights1, bias1, hlayer1, weights2, bias2, y, t)
del (count, count1)
print("Epoch ", k, " : ")
print("Cost : ", cost)
errors.append(cost)
if k % 10 == 0:
print(weights1, '\n', weights2, '\n', bias1, '\n', bias2)
plt.figure(1)