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Modernize Python 2 code to get ready for Python 3

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cclauss
2017-11-25 10:23:50 +01:00
parent a03b2eafc0
commit 4e06949072
95 changed files with 580 additions and 521 deletions
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9
Neural_Network/convolution_neural_network.py
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@@ -15,6 +15,7 @@
Date: 2017.9.20
- - - - - -- - - - - - - - - - - - - - - - - - - - - - -
'''
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
@@ -192,8 +193,8 @@ class CNN():
def trian(self,patterns,datas_train, datas_teach, n_repeat, error_accuracy,draw_e = bool):
#model traning
print('----------------------Start Training-------------------------')
print(' - - Shape: Train_Data ',np.shape(datas_train))
print(' - - Shape: Teach_Data ',np.shape(datas_teach))
print((' - - Shape: Train_Data ',np.shape(datas_train)))
print((' - - Shape: Teach_Data ',np.shape(datas_teach)))
rp = 0
all_mse = []
mse = 10000
@@ -262,7 +263,7 @@ class CNN():
plt.grid(True, alpha=0.5)
plt.show()
print('------------------Training Complished---------------------')
print(' - - Training epoch: ', rp, ' - - Mse: %.6f' % mse)
print((' - - Training epoch: ', rp, ' - - Mse: %.6f' % mse))
if draw_e:
draw_error()
return mse
@@ -271,7 +272,7 @@ class CNN():
#model predict
produce_out = []
print('-------------------Start Testing-------------------------')
print(' - - Shape: Test_Data ',np.shape(datas_test))
print((' - - Shape: Test_Data ',np.shape(datas_test)))
for p in range(len(datas_test)):
data_test = np.asmatrix(datas_test[p])
data_focus1, data_conved1 = self.convolute(data_test, self.conv1, self.w_conv1,

7
Neural_Network/perceptron.py
View File

@@ -9,6 +9,7 @@
p2 = 1
'''
from __future__ import print_function
import random
@@ -52,7 +53,7 @@ class Perceptron:
epoch_count = epoch_count + 1
# if you want controle the epoch or just by erro
if erro == False:
print('\nEpoch:\n',epoch_count)
print(('\nEpoch:\n',epoch_count))
print('------------------------\n')
#if epoch_count > self.epoch_number or not erro:
break
@@ -66,10 +67,10 @@ class Perceptron:
y = self.sign(u)
if y == -1:
print('Sample: ', sample)
print(('Sample: ', sample))
print('classification: P1')
else:
print('Sample: ', sample)
print(('Sample: ', sample))
print('classification: P2')
def sign(self, u):