使用TensorFlow实现SVM

技术  /  管理员 发布于 7年前   223

较基础的SVM，后续会加上多分类以及高斯核，供大家参考。

Talk is cheap, show me the code

import tensorflow as tffrom sklearn.base import BaseEstimator, ClassifierMixinimport numpy as npclass TFSVM(BaseEstimator, ClassifierMixin): def __init__(self,   C = 1, kernel = 'linear',   learning_rate = 0.01,   training_epoch = 1000,   display_step = 50,  batch_size = 50,  random_state = 42):  #参数列表  self.svmC = C  self.kernel = kernel  self.learning_rate = learning_rate  self.training_epoch = training_epoch  self.display_step = display_step  self.random_state = random_state  self.batch_size = batch_size def reset_seed(self):  #重置随机数  tf.set_random_seed(self.random_state)  np.random.seed(self.random_state) def random_batch(self, X, y):  #调用随机子集，实现mini-batch gradient descent  indices = np.random.randint(1, X.shape[0], self.batch_size)  X_batch = X[indices]  y_batch = y[indices]  return X_batch, y_batch def _build_graph(self, X_train, y_train):  #创建计算图  self.reset_seed()  n_instances, n_inputs = X_train.shape  X = tf.placeholder(tf.float32, [None, n_inputs], name = 'X')  y = tf.placeholder(tf.float32, [None, 1], name = 'y')  with tf.name_scope('trainable_variables'):   #决策边界的两个变量   W = tf.Variable(tf.truncated_normal(shape = [n_inputs, 1], stddev = 0.1), name = 'weights')   b = tf.Variable(tf.truncated_normal([1]), name = 'bias')  with tf.name_scope('training'):   #算法核心   y_raw = tf.add(tf.matmul(X, W), b)   l2_norm = tf.reduce_sum(tf.square(W))   hinge_loss = tf.reduce_mean(tf.maximum(tf.zeros(self.batch_size, 1), tf.subtract(1., tf.multiply(y_raw, y))))   svm_loss = tf.add(hinge_loss, tf.multiply(self.svmC, l2_norm))   training_op = tf.train.AdamOptimizer(learning_rate = self.learning_rate).minimize(svm_loss)  with tf.name_scope('eval'):   #正确率和预测   prediction_class = tf.sign(y_raw)   correct_prediction = tf.equal(y, prediction_class)   accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))  init = tf.global_variables_initializer()  self._X = X; self._y = y  self._loss = svm_loss; self._training_op = training_op  self._accuracy = accuracy; self.init = init  self._prediction_class = prediction_class  self._W = W; self._b = b def _get_model_params(self):  #获取模型的参数，以便存储  with self._graph.as_default():   gvars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)  return {gvar.op.name: value for gvar, value in zip(gvars, self._session.run(gvars))} def _restore_model_params(self, model_params):  #保存模型的参数  gvar_names = list(model_params.keys())  assign_ops = {gvar_name: self._graph.get_operation_by_name(gvar_name + '/Assign') for gvar_name in gvar_names}  init_values = {gvar_name: assign_op.inputs[1] for gvar_name, assign_op in assign_ops.items()}  feed_dict = {init_values[gvar_name]: model_params[gvar_name] for gvar_name in gvar_names}  self._session.run(assign_ops, feed_dict = feed_dict) def fit(self, X, y, X_val = None, y_val = None):  #fit函数，注意要输入验证集  n_batches = X.shape[0] // self.batch_size  self._graph = tf.Graph()  with self._graph.as_default():   self._build_graph(X, y)  best_loss = np.infty  best_accuracy = 0  best_params = None  checks_without_progress = 0  max_checks_without_progress = 20  self._session = tf.Session(graph = self._graph)  with self._session.as_default() as sess:   self.init.run()   for epoch in range(self.training_epoch):    for batch_index in range(n_batches):     X_batch, y_batch = self.random_batch(X, y)     sess.run(self._training_op, feed_dict = {self._X:X_batch, self._y:y_batch})    loss_val, accuracy_val = sess.run([self._loss, self._accuracy], feed_dict = {self._X: X_val, self._y: y_val})    accuracy_train = self._accuracy.eval(feed_dict = {self._X: X_batch, self._y: y_batch})    if loss_val < best_loss:     best_loss = loss_val     best_params = self._get_model_params()     checks_without_progress = 0    else:     checks_without_progress += 1     if checks_without_progress > max_checks_without_progress:      break    if accuracy_val > best_accuracy:     best_accuracy = accuracy_val     #best_params = self._get_model_params()    if epoch % self.display_step == 0:     print('Epoch: {}\tValidaiton loss: {:.6f}\tValidation Accuracy: {:.4f}\tTraining Accuracy: {:.4f}'      .format(epoch, loss_val, accuracy_val, accuracy_train))   print('Best Accuracy: {:.4f}\tBest Loss: {:.6f}'.format(best_accuracy, best_loss))   if best_params:    self._restore_model_params(best_params)    self._intercept = best_params['trainable_variables/weights']    self._bias = best_params['trainable_variables/bias']   return self def predict(self, X):  with self._session.as_default() as sess:   return self._prediction_class.eval(feed_dict = {self._X: X}) def _intercept(self):  return self._intercept def _bias(self):  return self._bias

实际运行效果如下（以Iris数据集为样本）：

 

画出决策边界来看看：

 

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持。