高斯核函数SVM TensorFlow实现

import matplotlib.pyplot as pltimport numpy as npimport tensorflow as tffrom sklearn import datasetsfrom tensorflow.python.framework import ops(x_vals, y_vals) = datasets.make_circles(n_samples=350, factor=.5, noise=.1)y_vals = np.array([1 if y==1 else -1 for y in y_vals])#plot pointsclass1_x = [x[0] for i,x in enumerate(x_vals) if y_vals[i]==1]class1_y = [x[1] for i,x in enumerate(x_vals) if y_vals[i]==1]class2_x = [x[0] for i,x in enumerate(x_vals) if y_vals[i]==-1]class2_y = [x[1] for i,x in enumerate(x_vals) if y_vals[i]==-1]%matplotlib inlineplt.plot(class1_x, class1_y, 'ro', label='Class 1')plt.plot(class2_x, class2_y, 'kx', label='Class -1')plt.title('distrubtion')plt.xlabel('x')plt.ylabel('y')plt.ylim([-1.5, 1.5])plt.xlim([-1.5, 1.5])plt.show()#data processingx = np.transpose(x_vals)     #shape=(features,examples)y = y_vals.reshape((1,-1))   #shape=(targets,examples)# Split data into train/test setstrain_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]def model(x_train,y_train,x_test,y_test,learning_rate=0.01):    ops.reset_default_graph()        X_place = tf.placeholder(tf.float32,[2,None])    Y_place = tf.placeholder(tf.float32,[1,None])    w = tf.Variable(tf.random_normal(shape=[1,x_train.shape[1]]))    b = tf.Variable(tf.random_normal(shape=[1,1]))        #compute Gaussian Kernel     gamma = tf.constant(-10.0)    dist = tf.reduce_sum(tf.square(tf.transpose(X_place)), 1)    dist = tf.reshape(dist, [-1,1])    sq_dists = tf.add(tf.subtract(dist, tf.multiply(2., tf.matmul(tf.transpose(X_place),X_place))), tf.transpose(dist))    my_kernel = tf.exp(tf.multiply(gamma, sq_dists))    output = tf.add(tf.matmul(w,my_kernel),b)        loss = tf.reduce_mean(tf.maximum(0.,tf.subtract(1.,tf.multiply(output,Y_place)))) + tf.matmul(w,tf.transpose(w))    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 = []                for epoch in range(1000):            _,train_cost = sess.run([optimizer,loss],feed_dict={X_place:x_train,Y_place:y_train})            train_loss.append(train_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)                x_trainfloat = tf.to_float(x_train)        x_testfloat = tf.to_float(x_test)                dist = tf.reduce_sum(tf.square(tf.transpose(x_trainfloat)), 1)        dist = tf.reshape(dist, [-1,1])        sq_dists = tf.add(tf.subtract(dist, tf.multiply(2., tf.matmul(tf.transpose(x_trainfloat),x_trainfloat))), tf.transpose(dist))        my_kernel = tf.exp(tf.multiply(gamma, sq_dists))        predict_train = tf.add(tf.matmul(w_new,my_kernel),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))                j = 0.        for i in range(x_test.shape[1]):            k = np.array([[x_test[0,i]],[x_test[1,i]]])            a = np.add(np.dot(np.exp(np.sum(np.square(x_train - k),0)* (-10)),np.transpose(w_new)),b_new)            if a*y_test[0,i]>0:                 j = j+1.        test_accuracy = j/x_test.shape[1]        print('test_accuracy:' + str(test_accuracy))    return train_loss    train_loss = model(x_train,y_train,x_test,y_test)