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2_author_basictest.py
37 lines (35 loc) · 1.12 KB
/
2_author_basictest.py
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import neural_network_matrix as nn_matrix
import info_parser as ip
import numpy as np
def main():
shake = "docs/shakespeare-"
txt = ".txt"
twain = "docs/twain-"
nn = nn_matrix.neural_network(6,2,[8],1, 0.5)
prsr = ip.parser()
shake_desired = np.array([[1], [0]])
twain_desired = np.array([[0], [1]])
input_shake = []
input_twain = []
for j in range(1, 6):
input_shake.append(np.array([prsr.parse(shake+str(j)+txt)]).T)
input_twain.append(np.array([prsr.parse(twain+str(j)+txt)]).T)
print (input_shake)
print(input_twain)
for x in range(0, 1000):
for i in range(0, 5):
# input_shake = np.array([prsr.parse(shake+str(i)+txt)]).T
# print("input_shake: ", input_shake)
# input_twain = np.array([prsr.parse(twain+str(i)+txt)]).T
# print("input_twain: ", input_twain)
nn.output(input_shake[i])
nn.back_prop(shake_desired)
nn.output(input_twain[i])
nn.back_prop(twain_desired)
test_shake = prsr.parse(shake+"test1"+txt)
test_twain = prsr.parse(twain+"test1"+txt)
shake_out = nn.output(test_shake)
twain_out = nn.output(test_twain)
print("shake_out: ", shake_out)
print("twain_out: ", twain_out)
main()