Create bp_test.py

Author: zhaozhiyong19890102

Chapter_6 BP/bp_test.py

@@ -0,0 +1,100 @@
+# coding:UTF-8
+'''
+Date:20160831
+@author: zhaozhiyong
+'''
+import numpy as np
+from bp_train import get_predict
+
+def load_data(file_name):
+    '''导入数据
+    input:  file_name(string):文件的存储位置
+    output: feature_data(mat):特征
+    '''
+    f = open(file_name)  # 打开文件
+    feature_data = []
+    for line in f.readlines():
+        feature_tmp = []
+        lines = line.strip().split("\t")
+        for i in xrange(len(lines)):
+            feature_tmp.append(float(lines[i]))        
+        feature_data.append(feature_tmp)
+    f.close()  # 关闭文件
+    return np.mat(feature_data)
+
+def generate_data():
+    '''在[-4.5,4.5]之间随机生成20000组点
+    '''
+    # 1、随机生成数据点
+    data = np.mat(np.zeros((20000, 2)))
+    m = np.shape(data)[0]
+    x = np.mat(np.random.rand(20000, 2))
+    for i in xrange(m):
+        data[i, 0] = x[i, 0] * 9 - 4.5
+        data[i, 1] = x[i, 1] * 9 - 4.5
+    # 2、将数据点保存到文件“test_data”中
+    f = open("test_data", "w")
+    m,n = np.shape(dataTest)
+    for i in xrange(m):
+        tmp =[]
+        for j in xrange(n):
+            tmp.append(str(dataTest[i,j]))
+        f.write("\t".join(tmp) + "\n")
+    f.close()       
+
+def load_model(file_w0, file_w1, file_b0, file_b1):
+    
+    def get_model(file_name):
+        f = open(file_name)
+        model = []
+        for line in f.readlines():
+            lines = line.strip().split("\t")
+            model_tmp = []
+            for x in lines:
+                model_tmp.append(float(x.strip()))
+            model.append(model_tmp)
+        f.close()
+        return np.mat(model)
+    
+    # 1、导入输入层到隐含层之间的权重
+    w0 = get_model(file_w0)
+    
+    # 2、导入隐含层到输出层之间的权重
+    w1 = get_model(file_w1)
+    
+    # 3、导入输入层到隐含层之间的权重
+    b0 = get_model(file_b0)
+    
+    # 4、导入隐含层到输出层之间的权重
+    b1 = get_model(file_b1)
+
+    return w0, w1, b0, b1
+
+def save_predict(file_name, pre):
+    '''保存最终的预测结果
+    input:  pre(mat):最终的预测结果
+    output:
+    '''
+    f = open(file_name, "w")
+    m = np.shape(pre)[0]
+    result = []
+    for i in xrange(m):
+        result.append(str(pre[i, 0]))
+    f.write("\n".join(result))
+    f.close()
+
+if __name__ == "__main__":
+    generate_data()
+    # 1、导入测试数据
+    print "--------- 1.load data ------------"
+    dataTest = load_data("test_data")
+    # 2、导入BP神经网络模型
+    print "--------- 2.load model ------------"
+    w0, w1, b0, b1 = load_model("weight_w0", "weight_w1", "weight_b0", "weight_b1")
+    # 3、得到最终的预测值
+    print "--------- 3.get prediction ------------"
+    result = get_predict(dataTest, w0, w1, b0, b1)
+    # 4、保存最终的预测结果
+    print "--------- 4.save result ------------"
+    pre = np.argmax(result, axis=1)
+    save_predict("result", pre)