Untitled

import numpy as np
import csv
import Circle_data
import matplotlib.pyplot as plt

class Data(): #這個類別協助我們把資料轉成向量以及上好標籤
    def __init__(self, x, z):
        self.x = self.setX(x)
        self.z = self.setZ(z)

    def setX(self, x):
        return np.array([x]).T

    def setZ(self, z):
        return np.array([z]).T

def readData(): #這個函數用來讀取資料
    data = []
    with open("output.csv", newline = "") as csvfile:
        rows = csv.DictReader(csvfile)
        for row in rows:
            x = [float(row['x1']), float(row['x2'])]
            z = [float(row['z'])]
            data.append(Data(x, z))
    return data

def f(x, function_name = 'ReLU'):
    if function_name == 'ReLU':
        return np.maximum(x, 0)
    elif function_name == 'tanh':
        return np.tanh(x)
    elif function_name == 'sigmoid':
        return 1 / (1 + np.exp(-x))

def df(x, Hidden_nodes, function_name = 'ReLU'):
    if function_name == 'ReLU':
        return np.eye(Hidden_nodes, dtype = 'float')*(np.where(x > 0, 1, 0))
    elif function_name == 'tanh':
        return np.eye(Hidden_nodes, dtype = 'float')*(1 - f(x, function_name)*f(x, function_name))
    elif function_name == 'sigmoid':
        return np.eye(Hidden_nodes, dtype = 'float')*(f(x, function_name)*(1 - f(x, function_name)))

def loss(N, Layer, data, loss_name ='MSE'):
    if loss_name == 'MSE':
        result = 0
        for i in range(N):
            result = result + (Layer[2][i] - data[i].z)**2
        return result / (2*N)

def forward(W1, W2, b, N, data, function_name = 'ReLU'): #向前傳播
    Layer1_1 = []
    Layer1_2 = []
    output = []
    Layer = []
    for i in range(N):
        Layer1_1.append(W1.dot(data[i].x)+b)  #計算每一層的值，並存取下來
        Layer1_2.append(f(Layer1_1[i], function_name))
        output.append(W2.dot(Layer1_2[i]))
    Layer.append(Layer1_1)
    Layer.append(Layer1_2)
    Layer.append(output)
    return Layer

def backward(W2, N, Layer, data, Hidden_nodes, function_name = 'ReLU'): #反向傳播
    dW2 = np.zeros((1, Hidden_nodes)) #計算W2的梯度
    for i in range(N):
        dW2 = dW2 + (Layer[2][i] - data[i].z)*(Layer[1][i].T)
    dW2 = dW2 / N

    dW1 = np.zeros((Hidden_nodes, 2)) #計算W1和b的梯度
    db = np.zeros((Hidden_nodes, 1))
    for i in range(N):
        temp = (Layer[2][i] - data[i].z)*W2.dot(df(Layer[0][i], Hidden_nodes, function_name))
        db = db + temp.T
        dW1 = dW1 + np.kron(temp.T, data[i].x.T)
    dW1 = dW1 / N
    db = db / N
    return dW1, dW2, db

def Train(W1, W2, b, data, Hidden_nodes, N, learning_rate = 0.3, beta = 0.1, gamma = 0.9, beta1 = 0.9, beta2 = 0.999, Method = 'Adam', function_name = 'ReLU', loss_name ='MSE', train_size = 20000):
    TOL=1e-7
    k = 1
    Layer = forward(W1, W2, b, N, data)
    best_result = 0
    mW1 = sW1 = vW1 = np.zeros(W1.shape)
    mW2 = sW2 = vW2 = np.zeros(W2.shape)
    mb = sb = vb = np.zeros(b.shape)
    train = []
    test = []
    G = [[], [], []]
    dG = [np.zeros(W1.shape), np.zeros(W2.shape), np.zeros(b.shape)]
    y = []
    for i in range(train_size):
        train.append(data[i])
    for i in range(N-train_size):
        test.append(data[train_size+i])
    Layer = forward(W1, W2, b, train_size, data, function_name = 'ReLU')
    while k <= 200:
        dW1, dW2, db = backward(W2, train_size, Layer, train, Hidden_nodes, function_name = 'ReLU')
        if Method == 'GradientDescent':
            W1 = W1 - learning_rate*dW1
            W2 = W2 - learning_rate*dW2
            b = b - learning_rate*db
        elif Method == 'Momentum':
            mW1 = beta*dW1 + gamma*mW1
            mW2 = beta*dW2 + gamma*mW2
            mb = beta*db + gamma*mb
            W1 = W1 - learning_rate*mW1
            W2 = W2 - learning_rate*mW2
            b = b - learning_rate*mb
        elif Method == 'Adagrad':
            G[0].append(dW1)
            G[1].append(dW2)
            G[2].append(db)
            for i in range(k):
                dG[0] += G[0][i]**2
                dG[1] += G[1][i]**2
                dG[2] += G[2][i]**2
            dG[0] = dG[0]**(1/2)
            dG[1] = dG[1]**(1/2)
            dG[2] = dG[2]**(1/2)
            W1 = W1 - (learning_rate/(dG[0] + TOL))*dW1
            W2 = W2 - (learning_rate/(dG[1] + TOL))*dW2
            b = b - (learning_rate/(dG[2] + TOL))*db
        elif Method == 'RMSProp':
            G[0].append(dW1)
            G[1].append(dW2)
            G[2].append(db)
            for i in range(k):
                dG[0] += G[0][i]**2
                dG[1] += G[1][i]**2
                dG[2] += G[2][i]**2
            dG[0] = (1/k)*dG[0]
            dG[1] = (1/k)*dG[1]
            dG[2] = (1/k)*dG[2]
            dG[0] = dG[0]**(1/2)
            dG[1] = dG[1]**(1/2)
            dG[2] = dG[2]**(1/2)
            W1 = W1 - (learning_rate/(dG[0] + TOL))*dW1
            W2 = W2 - (learning_rate/(dG[1] + TOL))*dW2
            b = b - (learning_rate/(dG[2] + TOL))*db

        elif Method == 'Adam':
            vW1 = beta1*vW1 + (1 - beta1)*dW1
            vW2 = beta1*vW2 + (1 - beta1)*dW2
            vb = beta1*vb + (1 - beta1)*db
            sW1 = beta2*sW1 + (1 - beta2)*dW1*dW1
            sW2 = beta2*sW2 + (1 - beta2)*dW2*dW2
            sb = beta2*sb + (1 - beta2)*db*db
            vW1_hat = vW1/(1 - beta1**k)
            vW2_hat = vW2/(1 - beta1**k)
            vb_hat = vb/(1 - beta1**k)
            sW1_hat = sW1/(1 - beta2**k)
            sW2_hat = sW2/(1 - beta2**k)
            sb_hat = sb/(1 - beta2**k)
            new_dW1 = learning_rate*vW1_hat/(np.sqrt(sW1_hat)+TOL)
            new_dW2 = learning_rate*vW2_hat/(np.sqrt(sW2_hat)+TOL)
            new_db = learning_rate*vb_hat/(np.sqrt(sb_hat)+TOL)
            W1 = W1 - new_dW1
            W2 = W2 - new_dW2
            b = b - new_db
        Layer = forward(W1, W2, b, train_size, train, function_name = 'ReLU')
        result = ANN(W1, W2, b, test, function_name = 'ReLU')
        y.append(float(loss(train_size, Layer, train, loss_name ='MSE')))
        print(k)
        print(y[k-1])
        if result[1] > best_result:
            best_result = result[1]
            bestW1 = W1
            bestW2 = W2
            bestb = b
        k = k + 1
    return bestW1, bestW2, bestb, y

def ANN(W1, W2, b, data, function_name = 'ReLU'):
    M = len(data)
    output = forward(W1, W2, b, M, data, function_name)
    ans = []
    correct = 0
    for i in range(M):
        if output[2][i] < 0:
            ans.append(-1)
        else:
            ans.append(1)

        if ans[i] == data[i].z:
            correct = correct + 1
    return [ans, correct / M]

def GenerateWeight(Hidden_nodes):
    W1 = np.random.rand(Hidden_nodes, 2) #權重矩陣W1和W2
    W2 = np.random.rand(1,Hidden_nodes)
    b = np.random.rand(Hidden_nodes,1) #偏置向量b
    return W1, W2, b

def GenerateTestData(M):
    temp = []
    test_data = []
    for i in range(M):
        r = np.random.randint(0, 2)
        if r == 1:
            temp.append(Circle_data.Data("blue"))
        else:
            temp.append(Circle_data.Data("red"))
        test_data.append(Data(temp[i].x, temp[i].z))
    return test_data

def main():
    data = readData() #把資料讀進來
    N = len(data)  #取得資料的個數
    Hidden_nodes = 4
    t = np.arange(100)
    """
    learning_rate = 0.3
    beta = 0.1
    gamma = 0.9
    beta1 = 0.9
    beta2 = 0.999
    Method = 'Adam'
    function_name = 'ReLU'
    loss_name ='MSE'
    train_size = 20000
    """
    W1, W2, b = GenerateWeight(Hidden_nodes)
    W1_1, W2_1, b_1, y_1 = Train(W1, W2, b, data, Hidden_nodes, N, learning_rate = 0.01, Method = 'GradientDescent')
    W1_2, W2_2, b_2, y_2 = Train(W1, W2, b, data, Hidden_nodes, N, learning_rate = 0.01, Method = 'Momentum')
    W1_3, W2_3, b_3, y_3 = Train(W1, W2, b, data, Hidden_nodes, N, learning_rate = 0.01, Method = 'Adagrad')
    W1_4, W2_4, b_4, y_4 = Train(W1, W2, b, data, Hidden_nodes, N, learning_rate = 0.01, Method = 'RMSProp')
    W1_5, W2_5, b_5, y_5 = Train(W1, W2, b, data, Hidden_nodes, N, learning_rate = 0.01, Method = 'Adam')

    plt.plot(t, y_1, 'b-', t, y_2, 'r-', t, y_3, 'g-', t, y_4, 'k-', t, y_5, 'y-')
    plt.show()
    plt.savefig('plot1.png')

    M = 1000
    test_data = GenerateTestData(M)

    result = ANN(W1_1, W2_1, b_1, test_data)
    print("命中率=",100*result[1],'%')
    result = ANN(W1_2, W2_2, b_2, test_data)
    print("命中率=",100*result[1],'%')
    result = ANN(W1_3, W2_3, b_3, test_data)
    print("命中率=",100*result[1],'%')
    result = ANN(W1_4, W2_4, b_4, test_data)
    print("命中率=",100*result[1],'%')
    result = ANN(W1_5, W2_5, b_5, test_data)
    print("命中率=",100*result[1],'%')


if __name__ == "__main__":
    main()