Use print() function in Python 2 and 3

src/py2.x/dl/bp.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 
+from __future__ import print_function
 import random
 from numpy import *
 
@@ -273,7 +274,7 @@ def dump(self):
         '''
         # 遍历层的所有的节点 nodes，将节点信息打印出来
         for node in self.nodes:
-            print node
+            print(node)
 
 
 # Connection 对象类，主要负责记录连接的权重，以及这个连接所关联的上下游的节点
@@ -396,7 +397,7 @@ def dump(self):
             None
         '''
         for conn in self.connections:
-            print conn
+            print(conn)
 
 
 # Network 对象，提供相应 API
@@ -743,7 +744,7 @@ def gradient_check(network, sample_feature, sample_label):
         expected_gradient = (error2 - error1) / (2 * epsilon)
 
         # 打印
-        print 'expected gradient: \t%f\nactual gradient: \t%f' % (expected_gradient, actual_gradient)
+        print('expected gradient: \t%f\nactual gradient: \t%f' % (expected_gradient, actual_gradient))
 
 
 def train_data_set():
@@ -804,7 +805,7 @@ def test(network, data):
     # 对测试数据进行预测
     predict_data = network.predict(norm_data)
     # 将结果打印出来
-    print '\ttestdata(%u)\tpredict(%u)' % (data, normalizer.denorm(predict_data))
+    print('\ttestdata(%u)\tpredict(%u)' % (data, normalizer.denorm(predict_data)))
 
 
 def correct_ratio(network):
@@ -821,7 +822,7 @@ def correct_ratio(network):
     for i in range(256):
         if normalizer.denorm(network.predict(normalizer.norm(i))) == i:
             correct += 1.0
-    print 'correct_ratio: %.2f%%' % (correct / 256 * 100)
+    print('correct_ratio: %.2f%%' % (correct / 256 * 100))
 
 
 def gradient_check_test():

src/py2.x/dl/cnn.py

@@ -2,6 +2,7 @@
 # -*- coding: UTF-8 -*-
 
 
+from __future__ import print_function
 import numpy as np
 from activators import ReluActivator, IdentityActivator
 
@@ -387,14 +388,14 @@ def init_test():
 def test():
     a, b, cl = init_test()
     cl.forward(a)
-    print cl.output_array
+    print(cl.output_array)
 
 def test_bp():
     a, b, cl = init_test()
     cl.backward(a, b, IdentityActivator())
     cl.update()
-    print cl.filters[0]
-    print cl.filters[1]
+    print(cl.filters[0])
+    print(cl.filters[1])
 
 
 def gradient_check():
@@ -427,8 +428,8 @@ def gradient_check():
                 err2 = error_function(cl.output_array)
                 expect_grad = (err1 - err2) / (2 * epsilon)
                 cl.filters[0].weights[d,i,j] += epsilon
-                print 'weights(%d,%d,%d): expected - actural %f - %f' % (
-                    d, i, j, expect_grad, cl.filters[0].weights_grad[d,i,j])
+                print('weights(%d,%d,%d): expected - actural %f - %f' % (
+                    d, i, j, expect_grad, cl.filters[0].weights_grad[d,i,j]))
 
 
 def init_pool_test():
@@ -456,10 +457,10 @@ def init_pool_test():
 def test_pool():
     a, b, mpl = init_pool_test()
     mpl.forward(a)
-    print 'input array:\n%s\noutput array:\n%s' % (a, mpl.output_array)
+    print('input array:\n%s\noutput array:\n%s' % (a, mpl.output_array))
 
 
 def test_pool_bp():
     a, b, mpl = init_pool_test()
     mpl.backward(a, b)
-    print 'input array:\n%s\nsensitivity array:\n%s\ndelta array:\n%s' % (a, b, mpl.delta_array)
+    print('input array:\n%s\nsensitivity array:\n%s\ndelta array:\n%s' % (a, b, mpl.delta_array))

src/py2.x/dl/fc.py

@@ -2,6 +2,7 @@
 # -*- coding: UTF-8 -*-
 
 
+from __future__ import print_function
 import random
 import numpy as np
 from activators import SigmoidActivator, IdentityActivator
@@ -57,7 +58,7 @@ def update(self, learning_rate):
         self.b += learning_rate * self.b_grad
 
     def dump(self):
-        print 'W: %s\nb:%s' % (self.W, self.b)
+        print('W: %s\nb:%s' % (self.W, self.b))
 
 
 # 神经网络类
@@ -149,8 +150,8 @@ def gradient_check(self, sample_feature, sample_label):
                     err2 = self.loss(sample_label, output)
                     expect_grad = (err1 - err2) / (2 * epsilon)
                     fc.W[i,j] += epsilon
-                    print 'weights(%d,%d): expected - actural %.4e - %.4e' % (
-                        i, j, expect_grad, fc.W_grad[i,j])
+                    print('weights(%d,%d): expected - actural %.4e - %.4e' % (
+                        i, j, expect_grad, fc.W_grad[i,j]))
 
 
 from bp import train_data_set
@@ -197,7 +198,7 @@ def correct_ratio(network):
     for i in range(256):
         if normalizer.denorm(network.predict(normalizer.norm(i))) == i:
             correct += 1.0
-    print 'correct_ratio: %.2f%%' % (correct / 256 * 100)
+    print('correct_ratio: %.2f%%' % (correct / 256 * 100))
 
 
 def test():
@@ -208,10 +209,10 @@ def test():
     epoch = 10
     for i in range(epoch):
         net.train(labels, data_set, rate, mini_batch)
-        print 'after epoch %d loss: %f' % (
+        print('after epoch %d loss: %f' % (
             (i + 1),
             net.loss(labels[-1], net.predict(data_set[-1]))
-        )
+        ))
         rate /= 2
     correct_ratio(net)
 

src/py2.x/dl/linear_unit.py

@@ -2,6 +2,7 @@
 # -*- coding: UTF-8 -*-
 
 # 引入 Perceptron 类
+from __future__ import print_function
 from perceptron import Perceptron
 
 # 定义激活函数 f
@@ -112,10 +113,10 @@ def plot(linear_unit):
     # 首先训练我们的线性单元
     linear_unit = train_linear_unit()
     # 打印训练获得的权重 和 偏置
-    print linear_unit
+    print(linear_unit)
     # 测试
-    print 'Work 3.4 years, monthly salary = %.2f' % linear_unit.predict([3.4])
-    print 'Work 15 years, monthly salary = %.2f' % linear_unit.predict([15])
-    print 'Work 1.5 years, monthly salary = %.2f' % linear_unit.predict([1.5])
-    print 'Work 6.3 years, monthly salary = %.2f' % linear_unit.predict([6.3])
+    print('Work 3.4 years, monthly salary = %.2f' % linear_unit.predict([3.4]))
+    print('Work 15 years, monthly salary = %.2f' % linear_unit.predict([15]))
+    print('Work 1.5 years, monthly salary = %.2f' % linear_unit.predict([1.5]))
+    print('Work 6.3 years, monthly salary = %.2f' % linear_unit.predict([6.3]))
     plot(linear_unit)

src/py2.x/dl/lstm.py

@@ -2,6 +2,7 @@
 # -*- coding: UTF-8 -*-
 
 
+from __future__ import print_function
 import matplotlib.pyplot as plt
 import numpy as np
 from cnn import element_wise_op
@@ -319,8 +320,8 @@ def gradient_check():
             err2 = error_function(lstm.h_list[-1])
             expect_grad = (err1 - err2) / (2 * epsilon)
             lstm.Wfh[i,j] += epsilon
-            print 'weights(%d,%d): expected - actural %.4e - %.4e' % (
-                i, j, expect_grad, lstm.Wfh_grad[i,j])
+            print('weights(%d,%d): expected - actural %.4e - %.4e' % (
+                i, j, expect_grad, lstm.Wfh_grad[i,j]))
     return lstm
 
 

src/py2.x/dl/mnist.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
 
+from __future__ import print_function
 import struct
 from fc import *
 from datetime import datetime
@@ -124,7 +125,7 @@ def show(sample):
             else:
                 str += ' '
         str += '\n'
-    print str
+    print(str)
 
 
 def get_result(vec):
@@ -162,11 +163,11 @@ def train_and_evaluate():
     while True:
         epoch += 1
         network.train(train_labels, train_data_set, 0.01, 1)
-        print '%s epoch %d finished, loss %f' % (now(), epoch, 
-            network.loss(train_labels[-1], network.predict(train_data_set[-1])))
+        print('%s epoch %d finished, loss %f' % (now(), epoch, 
+            network.loss(train_labels[-1], network.predict(train_data_set[-1]))))
         if epoch % 2 == 0:
             error_ratio = evaluate(network, test_data_set, test_labels)
-            print '%s after epoch %d, error ratio is %f' % (now(), epoch, error_ratio)
+            print('%s after epoch %d, error ratio is %f' % (now(), epoch, error_ratio))
             if error_ratio > last_error_ratio:
                 break
             else:

src/py2.x/dl/perceptron.py

@@ -3,6 +3,7 @@
 
 # 神经元 / 感知器
 
+from __future__ import print_function
 class Perceptron():
     '''
     Desc:
@@ -178,9 +179,9 @@ def train_and_perceptron():
     # 训练 and 感知器
     and_perceptron = train_and_perceptron()
     # 打印训练获得的权重
-    print and_perceptron
+    print(and_perceptron)
     # 测试
-    print '1 and 1 = %d' % and_perceptron.predict([1, 1])
-    print '0 and 0 = %d' % and_perceptron.predict([0, 0])
-    print '1 and 0 = %d' % and_perceptron.predict([1, 0])
-    print '0 and 1 = %d' % and_perceptron.predict([0, 1])
+    print('1 and 1 = %d' % and_perceptron.predict([1, 1]))
+    print('0 and 0 = %d' % and_perceptron.predict([0, 0]))
+    print('1 and 0 = %d' % and_perceptron.predict([1, 0]))
+    print('0 and 1 = %d' % and_perceptron.predict([0, 1]))

src/py2.x/dl/recursive.py

@@ -2,6 +2,7 @@
 # -*- coding: UTF-8 -*-
 
 
+from __future__ import print_function
 import numpy as np
 from activators import IdentityActivator
 
@@ -114,11 +115,11 @@ def calc_gradient(self, parent):
         return W_grad, b_grad
 
     def dump(self, **kwArgs):
-        print 'root.data: %s' % self.root.data
-        print 'root.children_data: %s' % self.root.children_data
+        print('root.data: %s' % self.root.data)
+        print('root.children_data: %s' % self.root.children_data)
         if kwArgs.has_key('dump_grad'):
-            print 'W_grad: %s' % self.W_grad
-            print 'b_grad: %s' % self.b_grad
+            print('W_grad: %s' % self.W_grad)
+            print('b_grad: %s' % self.b_grad)
 
 
 def data_set():
@@ -167,8 +168,8 @@ def gradient_check():
             err2 = error_function(rnn.root.data)
             expect_grad = (err1 - err2) / (2 * epsilon)
             rnn.W[i,j] += epsilon
-            print 'weights(%d,%d): expected - actural %.4e - %.4e' % (
-                i, j, expect_grad, rnn.W_grad[i,j])
+            print('weights(%d,%d): expected - actural %.4e - %.4e' % (
+                i, j, expect_grad, rnn.W_grad[i,j]))
     return rnn
 
 

src/py2.x/dl/rnn.py

@@ -2,6 +2,7 @@
 # -*- coding: UTF-8 -*-
 
 
+from __future__ import print_function
 import numpy as np
 from cnn import element_wise_op
 from activators import ReluActivator, IdentityActivator
@@ -143,8 +144,8 @@ def gradient_check():
             err2 = error_function(rl.state_list[-1])
             expect_grad = (err1 - err2) / (2 * epsilon)
             rl.W[i,j] += epsilon
-            print 'weights(%d,%d): expected - actural %f - %f' % (
-                i, j, expect_grad, rl.gradient[i,j])
+            print('weights(%d,%d): expected - actural %f - %f' % (
+                i, j, expect_grad, rl.gradient[i,j]))
 
 
 def test():

src/py2.x/ml/1.MLFoundation/NumPy.py

@@ -7,6 +7,7 @@
 Author: Peter Harrington/1988/片刻
 GitHub: https://github.com/apachecn/AiLearning
 '''
+from __future__ import print_function
 
 from numpy import random, mat, eye
 
@@ -43,15 +44,15 @@
 TraRandMat = randMat.T
 ArrRandMat = randMat.A
 # 输出结果
-print 'randArray=(%s) \n' % type(randArray), randArray
-print 'randMat=(%s) \n' % type(randMat), randMat
-print 'invRandMat=(%s) \n' % type(invRandMat), invRandMat
-print 'TraRandMat=(%s) \n' % type(TraRandMat), TraRandMat
-print 'ArrRandMat=(%s) \n' % type(ArrRandMat), ArrRandMat
+print('randArray=(%s) \n' % type(randArray), randArray)
+print('randMat=(%s) \n' % type(randMat), randMat)
+print('invRandMat=(%s) \n' % type(invRandMat), invRandMat)
+print('TraRandMat=(%s) \n' % type(TraRandMat), TraRandMat)
+print('ArrRandMat=(%s) \n' % type(ArrRandMat), ArrRandMat)
 # 矩阵和逆矩阵 进行求积 (单位矩阵，对角线都为1嘛，理论上4*4的矩阵其他的都为0)
 myEye = randMat*invRandMat
 # 误差
-print myEye - eye(4)
+print(myEye - eye(4))
 
 '''
 如果上面的代码运行没有问题，说明numpy安装没有问题