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