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
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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服务器之家。
原文链接：https://blog.csdn.net/sdoddyjm68/article/details/79392230
