线性支持向量机TensorFlow实现

#载入库import matplotlib.pyplot as pltimport numpy as npimport tensorflow as tffrom sklearn import datasetsfrom tensorflow.python.framework import ops#获得数据集，并且将方向转为便于计算的形状，横：特征，竖：样本一列一列排起来iris = datasets.load_iris()x_vals = iris.datay_vals = np.array([1 if y==0 else -1 for y in iris.target]) x = np.transpose(x_vals)y = y_vals.reshape((1,-1))# 划分训练集和测试集train_indices = np.random.choice(len(x_vals),round(len(x_vals)*0.8),replace=False)test_indices = np.array(list(set(range(len(x_vals))) - set(train_indices)))x_train = x[:,train_indices]x_test = x[:,test_indices]y_train = y[:,train_indices]y_test = y[:,test_indices]#整个计算结构模型def model(x_train,y_train,x_test,y_test,learning_rate=0.001):    ops.reset_default_graph()#每次运行都对计算流图重置        X_place = tf.placeholder(tf.float32,[4,None]) #设置特征占位空间    Y_place = tf.placeholder(tf.float32,[1,None])#设置target占位空间        w = tf.Variable(tf.random_normal(shape=[1,4]))#定义变量    b = tf.Variable(tf.random_normal(shape=[1,1]))#bias        a = tf.add(tf.matmul(w,X_place),b) #predict output        loss = tf.reduce_mean(tf.maximum(0.,tf.subtract(1.,tf.multiply(a,Y_place))))    optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(loss)        init = tf.global_variables_initializer()        with tf.Session() as sess:        sess.run(init)        train_loss = []        test_loss = []                for epoch in range(500):            _,train_cost = sess.run([optimizer,loss],feed_dict={X_place:x_train,Y_place:y_train})            train_loss.append(train_cost)            test_cost = sess.run(loss,feed_dict={X_place:x_test,Y_place:y_test})            test_loss.append(test_cost)                        if (epoch+1)%50==0:                print('epoch'+str(epoch+1)+':cost'+str(train_cost))                        w_new = sess.run(w)        b_new = sess.run(b)                print('w:'+str(w_new) + '   b:'+str(b_new))                predict_train = tf.add(tf.matmul(w_new,tf.to_float(x_train)),b_new)        predict_test = tf.add(tf.matmul(w_new,tf.to_float(x_test)),b_new)                prediction = tf.sign(predict_train)        accuracy = tf.reduce_mean(tf.cast(tf.equal(prediction, y_train), tf.float32))        train_accuracy = sess.run(accuracy)        print('train_accuracy:' + str(train_accuracy))                prediction = tf.sign(predict_test)        accuracy = tf.reduce_mean(tf.cast(tf.equal(prediction, y_test), tf.float32))        test_accuracy = sess.run(accuracy)        print('test_accuracy:' + str(test_accuracy))            return train_loss,test_loss train_loss,test_loss = model(x_train,y_train,x_test,y_test)%matplotlib inline# Plot loss (MSE) over timeplt.plot(train_loss, 'k-', label='Train Loss')plt.plot(test_loss, 'r--', label='test Loss')plt.title('Loss per Generation')plt.legend(loc='upper right')plt.xlabel('Generation')plt.ylabel('Loss')plt.show()