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)
def main():
shake = "docs2/shakespeare/"
twain = "docs2/twain/"
dickens = "docs2/dickens/"
txt = ".txt"
nn = nn_matrix.neural_network(6,3,[8],1, .3)
prsr = ip.parser()
shake_desired = np.array([[1], [0], [0]])
twain_desired = np.array([[0], [1], [0]])
dickens_desired = np.array([[0], [0], [1]])
input_shake = []
input_twain = []
input_dickens = []
for j in range(1, 15):
input_shake.append(np.array([prsr.parse(shake+str(j)+txt)]).T)
input_twain.append(np.array([prsr.parse(twain+str(j)+txt)]).T)
input_dickens.append(np.array([prsr.parse(dickens+str(j)+txt)]).T)
# print (input_shake)
# print(input_twain)
# print(input_dickens)
for x in range(0, 100):
for i in range(0, 14):
# 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)
nn.output(input_dickens[i])
nn.back_prop(dickens_desired)
for a in range(14,19):
stra = str(a)
test_shake = prsr.parse(shake+stra+txt)
test_twain = prsr.parse(twain+stra+txt)
test_dickens = prsr.parse(dickens+stra+txt)
shake_out = nn.output(test_shake)
twain_out = nn.output(test_twain)
dickens_out = nn.output(test_dickens)
print("shake_out"+stra + ": ", test(shake_out))
print("twain_out"+stra+": ", test(twain_out))
print("dickens_out"+stra+": ", test(dickens_out))
print("\n")
print(shake_out)
print(twain_out)
print(dickens_out)
print("\n")
def __init__(self, load):
self.lastPercent = 0.0
self.bestPercent = 0.0
if (load != '0'):
print("\ncreating train data...")
sys.stdout.flush()
self.td = train.train_data(files_location)
pickle.dump(self.td, open("_train_data.p", "wb"))
print("created sucessfully\n")
sys.stdout.flush()
else:
print("\nloading train data...")
sys.stdout.flush()
self.td = pickle.load(open("_train_data.p", "rb"))
print("loaded sucessfully\n")
sys.stdout.flush()
self.nn = nn_matrix.neural_network(len(self.td.inputs[0][0]),len(self.td.authors),nlayers,dummy_var, learn_rate)
def main():
train_rounds = 500
learn_rate = .5
shake = "docs2/shakespeare/"
twain = "docs2/twain/"
dickens = "docs2/dickens/"
doyle = "docs2/doyle/"
milton = "docs2/milton/"
txt = ".txt"
nn = nn_matrix.neural_network(10,5,[20],1, learn_rate)
prsr = ip.parser()
shake_desired = np.array([[1], [0], [0], [0], [0]])
twain_desired = np.array([[0], [1], [0], [0], [0]])
dickens_desired = np.array([[0], [0], [1], [0], [0]])
doyle_desired = np.array([[0], [0], [0], [1], [0]])
milton_desired = np.array([[0], [0], [0], [0], [1]])
input_shake = []
input_twain = []
input_dickens = []
input_doyle = []
input_milton = []
for j in range(1, 16):
input_shake.append(np.array([prsr.parse(shake+str(j)+txt)]).T)
input_twain.append(np.array([prsr.parse(twain+str(j)+txt)]).T)
input_dickens.append(np.array([prsr.parse(dickens+str(j)+txt)]).T)
input_doyle.append(np.array([prsr.parse(doyle+str(j)+txt)]).T)
input_milton.append(np.array([prsr.parse(milton+str(j)+txt)]).T)
for x in range(0, train_rounds):
for i in range(0, 15):
nn.output(input_shake[i])
nn.back_prop(shake_desired)
nn.output(input_twain[i])
nn.back_prop(twain_desired)
nn.output(input_dickens[i])
nn.back_prop(dickens_desired)
nn.output(input_doyle[i])
nn.back_prop(doyle_desired)
nn.output(input_milton[i])
nn.back_prop(milton_desired)
total_wrong = 0
total_tests = 0
for a in range(16,19):
total_tests += 5
stra = str(a)
test_shake = prsr.parse(shake+stra+txt)
test_twain = prsr.parse(twain+stra+txt)
test_dickens = prsr.parse(dickens+stra+txt)
test_doyle = prsr.parse(doyle+stra+txt)
test_milton = prsr.parse(milton+stra+txt)
shake_out = nn.output(test_shake)
twain_out = nn.output(test_twain)
dickens_out = nn.output(test_dickens)
doyle_out = nn.output(test_doyle)
milton_out = nn.output(test_milton)
# print("test"+stra+":")
if (test(shake_out) != "shakespeare"):
total_wrong += 1
print("shake_out"+stra+": ", test(shake_out))
if (test(twain_out) != "twain"):
total_wrong += 1
print("twain_out"+stra+": ", test(twain_out))
if (test(dickens_out) != "dickens"):
total_wrong += 1
print("dikns_out"+stra+": ", test(dickens_out))
if (test(doyle_out) != "doyle"):
total_wrong += 1
print("doyle_out"+stra+": ", test(doyle_out))
if (test(milton_out) != "milton"):
total_wrong +=1
print("miltn_out"+stra+": ", test(milton_out))
# print(shake_out)
# print(twain_out)
# print(dickens_out)
# print(doyle_out)
# print("\n")
for a in range(19, 30):
total_tests += 4
stra = str(a)
test_shake = prsr.parse(shake+stra+txt)
test_twain = prsr.parse(twain+stra+txt)
test_doyle = prsr.parse(doyle+stra+txt)
test_milton = prsr.parse(milton+stra+txt)
shake_out = nn.output(test_shake)
twain_out = nn.output(test_twain)
doyle_out = nn.output(test_doyle)
milton_out = nn.output(test_milton)
# print("test"+stra+":")
if (test(shake_out) != "shakespeare"):
total_wrong += 1
print("shake_out"+stra + ": ", test(shake_out))
if (test(twain_out) != "twain"):
total_wrong += 1
print("twain_out"+stra+": ", test(twain_out))
if (test(doyle_out) != "doyle"):
total_wrong += 1
print("doyle_out"+stra+": ", test(doyle_out))
if (test(milton_out) != "milton"):
total_wrong +=1
print("miltn_out"+stra+": ", test(milton_out))
print("correct percentage:", 1 - total_wrong/total_tests)
print("number wrong: ", total_wrong)
print("number of tests:", total_tests)
print("traing rounds:", train_rounds)
print("learning rate:", learn_rate)