学习了tensorflow的线性回归。
首先是一个sklearn中makeregression数据集，对其进行线性回归训练的例子。来自腾讯云实验室
import tensorflow as tf import numpy as np class linearRegressionModel: def __init__(self,x_dimen): self.x_dimen=x_dimen self._index_in_epoch=0 self.constructModel() self.sess=tf.Session() self.sess.run(tf.global_variables_initializer()) #权重初始化 def weight_variable(self,shape): initial=tf.truncated_normal(shape,stddev=0.1) return tf.Variable(initial) #偏置项初始化 def bais_variable(self,shape): initial=tf.constant(0.1,shape=shape) return tf.Variable(initial) #获取数据块，每次选100个样本，如果选完，则重新打乱 def next_batch(self,batch_size): start=self._index_in_epoch self._index_in_epoch+=batch_size if self._index_in_epoch>self._num_datas: perm=np.arange(self._num_datas) np.random.shuffle(perm) self._datas=self._datas[perm] self._labels=self._labels[perm] start=0 self._index_in_epoch=batch_size assert batch_size<=self._num_datas end=self._index_in_epoch return self._datas[start:end],self._labels[start:end] def constructModel(self): self.x=tf.placeholder(tf.float32,[None,self.x_dimen]) self.y=tf.placeholder(tf.float32,[None,1]) self.w=self.weight_variable([self.x_dimen,1]) self.b=self.bais_variable([1]) self.y_prec=tf.nn.bias_add(tf.matmul(self.x,self.w),self.b) mse=tf.reduce_mean(tf.squared_difference(self.y_prec,self.y)) l2=tf.reduce_mean(tf.square(self.w)) #self.loss=mse+0.15*l2 self.loss=mse self.train_step=tf.train.AdamOptimizer(0.1).minimize(self.loss) def train(self,x_train,y_train,x_test,y_test): self._datas=x_train self._labels=y_train self._num_datas=x_train.shape[0] for i in range(5000): batch=self.next_batch(100) self.sess.run(self.train_step, feed_dict={ self.x:batch[0], self.y:batch[1] }) if i%10==0: train_loss=self.sess.run(self.loss,feed_dict={ self.x:batch[0], self.y:batch[1] }) print("setp %d,test_loss %f"%(i,train_loss)) def predict_batch(self,arr,batchsize): for i in range(0,len(arr),batchsize): yield arr[i:i+batchsize] def predict(self,x_predict): pred_list=[] for x_test_batch in self.predict_batch(x_predict,100): pred =self.sess.run(self.y_prec,{self.x:x_test_batch}) pred_list.append(pred) return np.vstack(pred_list)