diff --git a/numpy-train-study2.py b/numpy-train-study2.py
deleted file mode 100644
index 6465a13..0000000
--- a/numpy-train-study2.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#简单神经网络测试实例
-import numpy as np
-
-def tanh(x):  #双曲函数
-    return np.tanh(x)
-
-def tanh_deriv(x):#更新权重时，需要用到双曲函数的倒数
-    return 1.0 - np.tanh(x)*np.tanh(x)
-
-def logistic(x):#构建逻辑函数
-    return 1/(1 + np.exp(-x))
-
-def logistic_derivatic(x):  #逻辑函数的倒数
-    return logistic(x)*(1 - logistic(x))
-
-class NeuralNetwork:
-    def __init__(self,layer,activation='tanh'):
-        '''
-        :param layer:A list containing the number of unit in each layer.
-        Should be at least two values.每层包含的神经元数目
-        :param activation: the activation function to be used.Can be
-        "logistic" or "tanh"
-        '''
-        if activation == 'logistic':
-            self.activation = logistic
-            self.activation_deriv = logistic_derivatic
-        elif activation == 'tanh':
-            self.activation = tanh
-            self.activation_deriv = tanh_deriv
-
-        self.weights = []
-        for i in range(1,len(layer) - 1):#权重的设置
-            self.weights.append((2*np.random.random((layer[i - 1] + 1,layer[i] + 1))-1)*0.25)
-            self.weights.append((2*np.random.random((layer[i] + 1,layer[i+1]))-1)*0.25)
-    '''训练神经网络，通过传入的数据，不断更新权重weights'''
-    def fit(self,X,y,learning_rate=0.2,epochs=10000):
-        '''
-        :param X: 数据集
-        :param y: 数据输出结果，分类标记
-        :param learning_rate: 学习率
-        :param epochs: 随机抽取的数据的训练次数
-        :return:
-        '''
-        X = np.atleast_2d(X) #转化X为np数据类型，试数据类型至少是两维的
-        temp = np.ones([X.shape[0],X.shape[1]+1])
-        temp[:,0:-1] = X
-        X = temp
-        y = np.array(y)
-
-        for k in range(epochs):
-            i = np.random.randint(X.shape[0])  #随机抽取的行
-            a = [X[i]]
-
-            for I in range(len(self.weights)):#完成正向所有的更新
-                a.append(self.activation(np.dot(a[I],self.weights[I])))#dot():对应位相乘后相加
-            error = y[i] - a[-1]
-            deltas = [error * self.activation_deriv(a[-1])]#*self.activation_deriv(a[I])#输出层误差
-            # 反向更新
-            for I in range(len(a) -2,0,-1):
-                deltas.append(deltas[-1].dot(self.weights[I].T)*self.activation_deriv(a[I]))
-            deltas.reverse()
-            for i in range(len(self.weights)):
-                layer = np.atleast_2d(a[i])
-                delta = np.atleast_2d(deltas[i])
-                self.weights[i] += learning_rate*layer.T.dot(delta)
-
-    def predict(self,x):
-        x = np.array(x)
-        temp = np.ones(x.shape[0] + 1)
-        temp[0:-1] = x
-        a = temp
-        for I in range(0,len(self.weights)):
-            a = self.activation(np.dot(a,self.weights[I]))
-        return a  #只需要保存最后的值，就是预测出来的值
-nn = NeuralNetwork([2,2,1], 'tanh')
-X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
-y = np.array([0, 1, 1, 0])
-nn.fit(X, y)
-for i in [[0, 0], [0, 1], [1, 0], [1,1]]:
-    print(i, nn.predict(i))
-
-#from sklearn.datasets import load_digits #导入数据集
-#from sklearn.metrics import confusion_matrix,classification_report   #对结果的预测的包
-#from sklearn.preprocessing import LabelBinarizer  #把数据转化为二维的数字类型
-#from sklearn.cross_validation import train_test_split   #可以把数据拆分成训练集与数据集
-
-#digits = load_digits()  #把数据改成0到1之间
-#X = digits.data
-#y = digits.target
-#X -= X.min()
-#X /= X.max()
-
-#nn = NeuralNetwork([64,100,10],'logistic')
-#X_train,X_test,y_train,y_test = train_test_split(X,y)
-#labels_train = LabelBinarizer().fit_transform(y_train)
-#labels_test = LabelBinarizer().fit_transform(y_test)
-#print("start fitting")
-#nn.fit(X_train,labels_train,epochs=3000)
-#predictions = []
-#for i in range(X_test.shape[0]):
-#    o = nn.predict(X_test[i])
-#    predictions.append(np.argmax(o))
-#print(confusion_matrix(y_test,predictions))
-#print(classification_report(y_test,predictions))
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