AI：基于TensorFlow的Keras训练神经网络模型二分分类预测，Python

╰半橙微兮° 2023-06-13 04:39 16阅读 0赞

# AI：基于TensorFlow的Keras训练神经网络模型二分分类预测，Python #

人类很容易就能实现按照一个参照值把一个数区分为大于它或者小于它。现在实现一个简单的二分分类，让机器学习如何像人类一样区分一个数和另外一个数的大小关系。假设有一批0到100以内\[0,100)的一维随机数据样本，要求实现标签为0和1的两种分类：

（1）\[0,50)，小于50的作为一种分类，分类标签为0。

（2）\[50,100)，大于50的数据作为一种分类，分类标签为1。

实现思路：先随机生成正确的样本数据喂给Keras神经网络训练模型，然后通过Keras训练好的模型进行预测。

Python实现的源代码：

from keras.models import Sequential
    from keras.layers import Dense
    import numpy as np
    from tensorflow import keras
    
    
    class LossHistory(keras.callbacks.Callback):
        def on_train_begin(self, logs={}):
            self.losses = []
    
        def on_batch_end(self, batch, logs={}):
            self.losses.append(logs.get('loss'))
    
    
    # 制造一批二维数组
    def make_data(LEN):
        a = np.random.randint(low=0, high=2, size=LEN * 2)
        x = np.array(a).reshape(LEN, 2)
        for i in range(LEN):
            if x[i][1] == 0:
                x[i][0] = np.random.randint(low=0, high=50)
            if x[i][1] == 1:
                x[i][0] = np.random.randint(low=50, high=100)
        return x
    
    
    def make_model():
        # 构建神经网络
        model = Sequential()
        model.add(Dense(units=32, input_dim=1, activation="relu"))
        model.add(Dense(units=16, activation="relu"))
        model.add(Dense(units=1, activation="sigmoid"))
    
        # 编译模型
        model.compile(loss="binary_crossentropy", optimizer="sgd", metrics=['accuracy'])
        return model
    
    
    if __name__ == "__main__":
        # LEN条训练数据
        LEN = 200
        data = make_data(LEN)
    
        model = make_model()
    
        # 训练模型
        history = LossHistory()
        model.fit(data[:, 0], [data[:, 1]], epochs=500, verbose=0, callbacks=[history])
        print(history.losses)
    
        data_eval = make_data(int(LEN / 2))
        score = model.evaluate(data_eval[:, 0], data_eval[:, 1], batch_size=100, verbose=0)
        print(score[0])
        print(score[1])
    
        # 随机生成一批测试数据
        x = np.random.randint(low=0, high=100, size=LEN)
    
        # 利用模型对测试数据进行预测
        y = model.predict_classes(x)
    
        # print(x)
        # print("-----")
        # print(y)
        # 输出预测结果，左侧是待测试数据，右侧是神经网络训练后形成的模型预测的结果。
        count = 0
        result = ("成功", "失败！！！！！")
        r = "*"
        for i in range(LEN):
            if (x[i] < 50):
                if (y[i][0] == 0):
                    count = count + 1
                    r = result[0]
                else:
                    r = result[1]
            if (x[i] >= 50):
                if (y[i][0] == 1):
                    count = count + 1
                    r = result[0]
                else:
                    r = result[1]
    
            print(("值=%s,预测分类:%s\t预测结果:%s") % (x[i], y[i][0], r))
    
        print("#####")
        print("预测准确率:%s" % (float(count / LEN)))

程序代码中make\_data()函数生成的训练样本数据，均是正确分类的数据样本，返回的原始内容为下面样式：

[[42  0]
     [61  1]
     [11  0]
     [72  1]
     [56  1]
     [38  0]
     [ 3  0]
     [85  1]
     [96  1]
     [45  0]
     [22  0]
     [17  0]
     [24  0]
     [77  1]
     [87  1]
     [ 8  0]
     [55  1]
     [95  1]
     [70  1]
     [74  1]
     [ 7  0]
     [ 6  0]
     [90  1]
     [58  1]
     [31  0]
     [18  0]
     [93  1]
     [43  0]
     [17  0]
     [37  0]
     [22  0]
     [41  0]
     [44  0]
     [36  0]
     [95  1]
     [ 6  0]
     [62  1]
     [50  1]
     [68  1]
     [38  0]
     [14  0]
     [22  0]
     [51  1]
     [27  0]
     [23  0]
     [23  0]
     [65  1]
     [33  0]
     [21  0]
     [ 9  0]
     [60  1]
     [ 6  0]
     [ 8  0]
     [32  0]
     [25  0]
     [12  0]
     [86  1]
     [41  0]
     [37  0]
     [19  0]
     [ 9  0]
     [50  1]
     [24  0]
     [ 5  0]
     [ 1  0]
     [32  0]
     [77  1]
     [32  0]
     [30  0]
     [25  0]
     [62  1]
     [99  1]
     [ 3  0]
     [44  0]
     [96  1]
     [50  1]
     [30  0]
     [ 7  0]
     [74  1]
     [70  1]
     [64  1]
     [21  0]
     [68  1]
     [15  0]
     [32  0]
     [43  0]
     [49  0]
     [49  0]
     [10  0]
     [66  1]
     [ 6  0]
     [83  1]
     [74  1]
     [79  1]
     [53  1]
     [80  1]
     [60  1]
     [25  0]
     [50  1]
     [93  1]
     [33  0]
     [96  1]
     [94  1]
     [92  1]
     [ 5  0]
     [15  0]
     [24  0]
     [85  1]
     [33  0]
     [91  1]
     [36  0]
     [ 3  0]
     [26  0]
     [78  1]
     [45  0]
     [62  1]
     [40  0]
     [74  1]
     [66  1]
     [15  0]
     [69  1]
     [63  1]
     [29  0]
     [94  1]
     [35  0]
     [90  1]
     [35  0]
     [66  1]
     [80  1]
     [90  1]
     [29  0]
     [42  0]
     [54  1]
     [85  1]
     [49  0]
     [93  1]
     [27  0]
     [22  0]
     [33  0]
     [25  0]
     [14  0]
     [37  0]
     [66  1]
     [43  0]
     [79  1]
     [16  0]
     [87  1]
     [65  1]
     [27  0]
     [87  1]
     [16  0]
     [56  1]
     [95  1]
     [79  1]
     [18  0]
     [29  0]
     [95  1]
     [48  0]
     [49  0]
     [29  0]
     [37  0]
     [63  1]
     [21  0]
     [38  0]
     [90  1]
     [41  0]
     [40  0]
     [50  1]
     [82  1]
     [56  1]
     [91  1]
     [22  0]
     [67  1]
     [81  1]
     [20  0]
     [44  0]
     [79  1]
     [93  1]
     [49  0]
     [32  0]
     [51  1]
     [38  0]
     [48  0]
     [93  1]
     [88  1]
     [89  1]
     [24  0]
     [98  1]
     [49  0]
     [56  1]
     [49  0]
     [12  0]
     [97  1]
     [79  1]
     [54  1]
     [ 0  0]
     [26  0]
     [ 5  0]
     [48  0]
     [20  0]]

左侧为基础数据，右侧0,1即为要对该数据进行分类的标签。以上为事先正确分类好的样本数据，目的是要把这批数据喂给神经网络训练模型。

然后再生成一批一维随机数据样本，利用训练好的神经网络模型，对该数据样本做预测，最终输出预测结果：

[11.244766, 3.8150086, 6.9662457, 1.0302663, 1.0896723, 2.6617458, 4.123061, 1.7894917, 0.917606, 0.74962133, 0.5680276, 0.76326954, 1.3762317, 3.0487409, 0.48978817, 0.58389676, 0.6217315, 0.5828403, 0.6257892, 0.57974935, 0.55443335, 0.6763031, 0.71688855, 1.4486533, 0.53834224, 0.41808736, 0.7221507, 0.6908778, 1.5172088, 0.689637, 0.58174795, 0.57682824, 0.4804348, 0.6396709, 0.5265458, 0.6348413, 0.54908764, 0.80897665, 0.60158485, 0.5159663, 0.52698576, 0.5709147, 0.5808643, 0.6462519, 0.6236482, 0.5940112, 0.5160579, 0.45896322, 0.46913052, 0.53210956, 0.59848094, 0.54664457, 0.50530136, 0.6172339, 0.60899776, 0.49314, 0.59885025, 0.6172176, 0.6026321, 0.52740794, 0.49615252, 0.5572733, 0.51327306, 0.62704206, 0.5258547, 0.5614842, 0.60290504, 0.57735574, 0.55800647, 0.676811, 0.610444, 0.64032316, 0.5052341, 0.7219678, 0.6444151, 0.5332043, 0.4469803, 0.70950305, 0.5743862, 0.48424333, 0.6750252, 0.5230763, 0.6429683, 0.48566234, 0.68288773, 0.45468298, 0.6517603, 0.56282043, 0.5598405, 0.57767004, 0.39591786, 0.66761345, 0.5937014, 0.5309913, 0.5377766, 0.51360166, 0.53723526, 0.56832695, 0.57593024, 0.5546211, 0.5481049, 0.40335667, 0.638633, 0.54731286, 0.65241766, 0.6030107, 0.49094075, 0.5779635, 0.518476, 0.58209044, 0.5645697, 0.36034155, 0.5699666, 0.6585594, 0.59549904, 0.49950755, 0.42562398, 0.5447415, 0.73725593, 0.73915136, 0.64923835, 0.48720303, 0.5742478, 0.5007781, 0.5341563, 0.44147, 0.56990254, 0.52662903, 0.4948697, 0.4400849, 0.6151228, 0.5738462, 0.57294685, 0.49568617, 0.67798984, 0.63652456, 0.57927257, 0.36494333, 0.66142523, 0.53471863, 0.54621184, 0.5678718, 0.5007334, 0.6445958, 0.52292836, 0.3831681, 0.7089562, 0.589084, 0.57387453, 0.57888556, 0.48568383, 0.45289963, 0.4556937, 0.5306964, 0.44788975, 0.45990357, 0.54875135, 0.5252254, 0.48404068, 0.6416881, 0.6711968, 0.6695987, 0.52409, 0.49268764, 0.57316816, 0.5131124, 0.4932562, 0.3757908, 0.5268316, 0.62321997, 0.5328926, 0.5854405, 0.47017413, 0.4953947, 0.39694026, 0.67602706, 0.5225835, 0.62859905, 0.4839614, 0.4457504, 0.48669112, 0.4908266, 0.43419725, 0.6157113, 0.50579524, 0.45955855, 0.4895284, 0.5547369, 0.6516021, 0.55994475, 0.5180168, 0.39936575, 0.5409136, 0.61375207, 0.539971, 0.43984652, 0.5408368, 0.5508492, 0.47431615, 0.59058803, 0.5202491, 0.39912075, 0.6555582, 0.49817184, 0.4866172, 0.62927127, 0.5030386, 0.605285, 0.49336094, 0.3318585, 0.57580507, 0.48524836, 0.3966067, 0.5181484, 0.63039577, 0.5503743, 0.4120491, 0.58154565, 0.41337758, 0.54935837, 0.5447369, 0.49173272, 0.50539595, 0.44785923, 0.5403025, 0.58892965, 0.5240449, 0.397339, 0.50420326, 0.48437983, 0.47368228, 0.50750124, 0.54412556, 0.46349573, 0.53669786, 0.51344776, 0.41494268, 0.70312226, 0.52002287, 0.5232929, 0.57823026, 0.5300293, 0.43815368, 0.48164767, 0.34128156, 0.58496314, 0.42453575, 0.5566851, 0.43810886, 0.47241902, 0.5903176, 0.47537476, 0.42657393, 0.5010649, 0.49438128, 0.5167884, 0.48412904, 0.5820276, 0.3690415, 0.5894414, 0.48869798, 0.5857731, 0.45376477, 0.4018171, 0.592584, 0.55957127, 0.4810198, 0.54170907, 0.45721605, 0.4861629, 0.47815043, 0.47406054, 0.5145341, 0.5274335, 0.4960587, 0.5038886, 0.49067938, 0.4641376, 0.4407462, 0.4106024, 0.57037085, 0.46025598, 0.49556056, 0.5643725, 0.5280975, 0.40529394, 0.13425972, 0.41815633, 0.41155493, 0.57281905, 0.5307988, 0.5069102, 0.49511212, 0.42535385, 0.37502915, 0.5986997, 0.48285103, 0.460107, 0.4898647, 0.52481973, 0.43407083, 0.41614935, 0.54580295, 0.44849646, 0.5336727, 0.46685833, 0.47474974, 0.51119685, 0.4321609, 0.5395615, 0.4665891, 0.47435015, 0.46760157, 0.5361804, 0.37934268, 0.3835271, 0.5078877, 0.5190532, 0.54406255, 0.46676862, 0.40387785, 0.6250851, 0.51966786, 0.37786567, 0.46604097, 0.53486145, 0.5251416, 0.45409793, 0.71101964, 0.53966427, 0.5880402, 0.43872947, 0.45293194, 0.52760935, 0.44589257, 0.40361294, 0.48375782, 0.48037905, 0.40948582, 0.5751155, 0.46415833, 0.44459867, 0.3685433, 0.43405825, 0.54579693, 0.46874753, 0.4684344, 0.4056989, 0.47416973, 0.60377127, 0.4690062, 0.387227, 0.54879725, 0.43825576, 0.53213954, 0.4947494, 0.47220856, 0.49596685, 0.46774304, 0.50541997, 0.4477366, 0.43932372, 0.47506857, 0.24914782, 0.5312924, 0.34992266, 0.55214524, 0.42691416, 0.44627157, 0.5514715, 0.47724277, 0.5170119, 0.4064551, 0.43190822, 0.4984627, 0.41949147, 0.47475418, 0.5602523, 0.4548239, 0.51944506, 0.48910192, 0.4650565, 0.40071175, 0.4333138, 0.30258888, 0.5175059, 0.6401828, 0.490741, 0.43056378, 0.3942501, 0.3487363, 0.5526842, 0.39916193, 0.42649376, 0.41237122, 0.5137979, 0.44122648, 0.52495295, 0.49675572, 0.534812, 0.4833151, 0.45929223, 0.3457023, 0.4114024, 0.5044356, 0.39966437, 0.49178654, 0.46750122, 0.44641215, 0.44098008, 0.43722478, 0.4133991, 0.42901438, 0.38884348, 0.42256004, 0.43538773, 0.4055733, 0.46310684, 0.534371, 0.49719027, 0.6076163, 0.49585626, 0.47239769, 0.42376804, 0.42787108, 0.37991872, 0.5656345, 0.46227136, 0.44341084, 0.4251207, 0.43204868, 0.41596714, 0.44542187, 0.5520784, 0.44205943, 0.37517774, 0.5151052, 0.43689507, 0.47097063, 0.45149755, 0.34766412, 0.43793747, 0.39888084, 0.48368382, 0.4334615, 0.38665044, 0.48789322, 0.5693704, 0.54786205, 0.43202317, 0.33660236, 0.54152775, 0.42776793, 0.46752995, 0.25182518, 0.44626337, 0.4422034, 0.5204556, 0.41591072, 0.43615553, 0.4098867, 0.46181643, 0.45365995, 0.47746557, 0.42986122, 0.45539424, 0.42050657, 0.4182217, 0.37765867, 0.39873967, 0.49930185, 0.45414877, 0.4053116, 0.39393404, 0.42527783, 0.6468828, 0.54118025, 0.3466346, 0.47290182, 0.4630626, 0.4902019, 0.43803772, 0.26436895, 0.3690949, 0.4490692, 0.47700095, 0.37901476, 0.40428257, 0.58409154, 0.3772735, 0.38302577, 0.3944772, 0.4185404, 0.48074067, 0.43941808, 0.39788163, 0.6487834, 0.54870087, 0.45146477, 0.44475263, 0.46085346, 0.41441324, 0.43032873, 0.2823752, 0.5117049, 0.44329354, 0.5097957, 0.3846034, 0.45453128, 0.40568775, 0.47611147, 0.46710742, 0.42636943, 0.37292665, 0.46000922, 0.35734946, 0.46583351, 0.46210814, 0.42307752, 0.38014576, 0.46320885, 0.39425683, 0.4461369, 0.41368735, 0.44825578, 0.41877878, 0.4347666, 0.38897282, 0.36289352, 0.4221335, 0.46724704, 0.5075437, 0.4428653, 0.42906964, 0.48821473, 0.39438012, 0.3784904, 0.43318963, 0.46553785, 0.48084795, 0.3775895, 0.4768946, 0.35743392, 0.474233, 0.40359738, 0.4376046, 0.4622901, 0.4405347, 0.3149084, 0.45024094, 0.35633954, 0.55837, 0.39451253, 0.43876395, 0.37150058, 0.47117344, 0.42361557, 0.36543906, 0.4164846, 0.44319907, 0.37057912, 0.36767802, 0.39058542, 0.48085308, 0.4756124, 0.39752385, 0.34772596, 0.38554507, 0.45969197, 0.46069735, 0.46835482, 0.3946823, 0.29928383, 0.23336858, 0.5941876, 0.4428111, 0.37084046, 0.42133686, 0.37275442, 0.36024278, 0.47988302, 0.3988039, 0.39016768, 0.42254463, 0.43302277, 0.39499047, 0.41414306, 0.39743492, 0.4798998, 0.405642, 0.39892036, 0.40948832, 0.39842778, 0.36535102, 0.28162253, 0.44226214, 0.4569531, 0.39395398, 0.395357, 0.39237326, 0.34715056, 0.3922865, 0.37999845, 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    0.12171410769224167
    0.9800000190734863
    值=42,预测分类:0	预测结果:成功
    值=81,预测分类:1	预测结果:成功
    值=51,预测分类:1	预测结果:成功
    值=11,预测分类:0	预测结果:成功
    值=52,预测分类:1	预测结果:成功
    值=50,预测分类:1	预测结果:成功
    值=50,预测分类:1	预测结果:成功
    值=42,预测分类:0	预测结果:成功
    值=35,预测分类:0	预测结果:成功
    值=65,预测分类:1	预测结果:成功
    值=28,预测分类:0	预测结果:成功
    值=58,预测分类:1	预测结果:成功
    值=22,预测分类:0	预测结果:成功
    值=59,预测分类:1	预测结果:成功
    值=16,预测分类:0	预测结果:成功
    值=5,预测分类:0	预测结果:成功
    值=86,预测分类:1	预测结果:成功
    值=48,预测分类:1	预测结果:失败！！！！！
    值=74,预测分类:1	预测结果:成功
    值=47,预测分类:1	预测结果:失败！！！！！
    值=18,预测分类:0	预测结果:成功
    值=33,预测分类:0	预测结果:成功
    值=39,预测分类:0	预测结果:成功
    值=45,预测分类:0	预测结果:成功
    值=77,预测分类:1	预测结果:成功
    值=74,预测分类:1	预测结果:成功
    值=87,预测分类:1	预测结果:成功
    值=33,预测分类:0	预测结果:成功
    值=41,预测分类:0	预测结果:成功
    值=55,预测分类:1	预测结果:成功
    值=56,预测分类:1	预测结果:成功
    值=83,预测分类:1	预测结果:成功
    值=55,预测分类:1	预测结果:成功
    值=3,预测分类:0	预测结果:成功
    值=7,预测分类:0	预测结果:成功
    值=84,预测分类:1	预测结果:成功
    值=23,预测分类:0	预测结果:成功
    值=96,预测分类:1	预测结果:成功
    值=81,预测分类:1	预测结果:成功
    值=80,预测分类:1	预测结果:成功
    值=78,预测分类:1	预测结果:成功
    值=48,预测分类:1	预测结果:失败！！！！！
    值=80,预测分类:1	预测结果:成功
    值=93,预测分类:1	预测结果:成功
    值=16,预测分类:0	预测结果:成功
    值=76,预测分类:1	预测结果:成功
    值=73,预测分类:1	预测结果:成功
    值=5,预测分类:0	预测结果:成功
    值=66,预测分类:1	预测结果:成功
    值=96,预测分类:1	预测结果:成功
    值=11,预测分类:0	预测结果:成功
    值=24,预测分类:0	预测结果:成功
    值=32,预测分类:0	预测结果:成功
    值=91,预测分类:1	预测结果:成功
    值=46,预测分类:0	预测结果:成功
    值=63,预测分类:1	预测结果:成功
    值=58,预测分类:1	预测结果:成功
    值=59,预测分类:1	预测结果:成功
    值=54,预测分类:1	预测结果:成功
    值=12,预测分类:0	预测结果:成功
    值=45,预测分类:0	预测结果:成功
    值=38,预测分类:0	预测结果:成功
    值=47,预测分类:1	预测结果:失败！！！！！
    值=73,预测分类:1	预测结果:成功
    值=3,预测分类:0	预测结果:成功
    值=23,预测分类:0	预测结果:成功
    值=63,预测分类:1	预测结果:成功
    值=32,预测分类:0	预测结果:成功
    值=33,预测分类:0	预测结果:成功
    值=97,预测分类:1	预测结果:成功
    值=27,预测分类:0	预测结果:成功
    值=6,预测分类:0	预测结果:成功
    值=63,预测分类:1	预测结果:成功
    值=29,预测分类:0	预测结果:成功
    值=33,预测分类:0	预测结果:成功
    值=22,预测分类:0	预测结果:成功
    值=31,预测分类:0	预测结果:成功
    值=4,预测分类:0	预测结果:成功
    值=11,预测分类:0	预测结果:成功
    值=6,预测分类:0	预测结果:成功
    值=8,预测分类:0	预测结果:成功
    值=6,预测分类:0	预测结果:成功
    值=80,预测分类:1	预测结果:成功
    值=89,预测分类:1	预测结果:成功
    值=29,预测分类:0	预测结果:成功
    值=78,预测分类:1	预测结果:成功
    值=78,预测分类:1	预测结果:成功
    值=54,预测分类:1	预测结果:成功
    值=90,预测分类:1	预测结果:成功
    值=76,预测分类:1	预测结果:成功
    值=50,预测分类:1	预测结果:成功
    值=86,预测分类:1	预测结果:成功
    值=96,预测分类:1	预测结果:成功
    值=62,预测分类:1	预测结果:成功
    值=53,预测分类:1	预测结果:成功
    值=60,预测分类:1	预测结果:成功
    值=51,预测分类:1	预测结果:成功
    值=13,预测分类:0	预测结果:成功
    值=14,预测分类:0	预测结果:成功
    值=44,预测分类:0	预测结果:成功
    值=74,预测分类:1	预测结果:成功
    值=3,预测分类:0	预测结果:成功
    值=5,预测分类:0	预测结果:成功
    值=25,预测分类:0	预测结果:成功
    值=10,预测分类:0	预测结果:成功
    值=29,预测分类:0	预测结果:成功
    值=47,预测分类:1	预测结果:失败！！！！！
    值=30,预测分类:0	预测结果:成功
    值=50,预测分类:1	预测结果:成功
    值=18,预测分类:0	预测结果:成功
    值=18,预测分类:0	预测结果:成功
    值=54,预测分类:1	预测结果:成功
    值=6,预测分类:0	预测结果:成功
    值=71,预测分类:1	预测结果:成功
    值=52,预测分类:1	预测结果:成功
    值=21,预测分类:0	预测结果:成功
    值=3,预测分类:0	预测结果:成功
    值=63,预测分类:1	预测结果:成功
    值=93,预测分类:1	预测结果:成功
    值=0,预测分类:0	预测结果:成功
    值=78,预测分类:1	预测结果:成功
    值=79,预测分类:1	预测结果:成功
    值=56,预测分类:1	预测结果:成功
    值=20,预测分类:0	预测结果:成功
    值=84,预测分类:1	预测结果:成功
    值=35,预测分类:0	预测结果:成功
    值=81,预测分类:1	预测结果:成功
    值=58,预测分类:1	预测结果:成功
    值=77,预测分类:1	预测结果:成功
    值=78,预测分类:1	预测结果:成功
    值=63,预测分类:1	预测结果:成功
    值=46,预测分类:0	预测结果:成功
    值=41,预测分类:0	预测结果:成功
    值=81,预测分类:1	预测结果:成功
    值=94,预测分类:1	预测结果:成功
    值=31,预测分类:0	预测结果:成功
    值=77,预测分类:1	预测结果:成功
    值=88,预测分类:1	预测结果:成功
    值=43,预测分类:0	预测结果:成功
    值=44,预测分类:0	预测结果:成功
    值=85,预测分类:1	预测结果:成功
    值=36,预测分类:0	预测结果:成功
    值=97,预测分类:1	预测结果:成功
    值=99,预测分类:1	预测结果:成功
    值=61,预测分类:1	预测结果:成功
    值=38,预测分类:0	预测结果:成功
    值=8,预测分类:0	预测结果:成功
    值=37,预测分类:0	预测结果:成功
    值=61,预测分类:1	预测结果:成功
    值=59,预测分类:1	预测结果:成功
    值=26,预测分类:0	预测结果:成功
    值=48,预测分类:1	预测结果:失败！！！！！
    值=53,预测分类:1	预测结果:成功
    值=67,预测分类:1	预测结果:成功
    值=96,预测分类:1	预测结果:成功
    值=45,预测分类:0	预测结果:成功
    值=15,预测分类:0	预测结果:成功
    值=81,预测分类:1	预测结果:成功
    值=18,预测分类:0	预测结果:成功
    值=33,预测分类:0	预测结果:成功
    值=51,预测分类:1	预测结果:成功
    值=15,预测分类:0	预测结果:成功
    值=99,预测分类:1	预测结果:成功
    值=38,预测分类:0	预测结果:成功
    值=19,预测分类:0	预测结果:成功
    值=81,预测分类:1	预测结果:成功
    值=56,预测分类:1	预测结果:成功
    值=58,预测分类:1	预测结果:成功
    值=76,预测分类:1	预测结果:成功
    值=84,预测分类:1	预测结果:成功
    值=25,预测分类:0	预测结果:成功
    值=70,预测分类:1	预测结果:成功
    值=78,预测分类:1	预测结果:成功
    值=40,预测分类:0	预测结果:成功
    值=2,预测分类:0	预测结果:成功
    值=3,预测分类:0	预测结果:成功
    值=70,预测分类:1	预测结果:成功
    值=41,预测分类:0	预测结果:成功
    值=95,预测分类:1	预测结果:成功
    值=69,预测分类:1	预测结果:成功
    值=14,预测分类:0	预测结果:成功
    值=32,预测分类:0	预测结果:成功
    值=31,预测分类:0	预测结果:成功
    值=65,预测分类:1	预测结果:成功
    值=75,预测分类:1	预测结果:成功
    值=86,预测分类:1	预测结果:成功
    值=66,预测分类:1	预测结果:成功
    值=8,预测分类:0	预测结果:成功
    值=57,预测分类:1	预测结果:成功
    值=80,预测分类:1	预测结果:成功
    值=89,预测分类:1	预测结果:成功
    值=21,预测分类:0	预测结果:成功
    值=31,预测分类:0	预测结果:成功
    值=42,预测分类:0	预测结果:成功
    值=30,预测分类:0	预测结果:成功
    值=39,预测分类:0	预测结果:成功
    值=89,预测分类:1	预测结果:成功
    值=3,预测分类:0	预测结果:成功
    值=70,预测分类:1	预测结果:成功
    值=21,预测分类:0	预测结果:成功
    #####
    预测准确率:0.97

程序跑起来预测的准确率高于90%，本例是97%，我自己跑神经网络训练出的模型预测准确率好的时候可以达到0.99以上。预测失败的情况集中在对小于50但接近50的值的预测上，比如48，49,50,51。

换句话说，如果随机生成的数据样本有很多的数据集中在50（分类的中间值）两侧（很多个48,49,47,50,51），预测的准确率就会下降；反过来说，如果随机生成的数据包含这类数据较少甚至没有，预测准确率就会大大提高。一个经验，在数据清洗阶段，把数据的方差做大区分度尽量提高，模型训练相对更好些，预测准确率也会提升。