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)
仿照这个代码，联系使用线性回归的方法对mnist进行训练。开始选择学习率为0.1，结果训练失败，调节学习率为0.01.正确率在0.91左右
给出训练类：
import tensorflow as tf import numpy as np class myLinearModel: def __init__(self,x_dimen): self.x_dimen=x_dimen self.epoch=0 self._num_datas=0 self.datas=None self.lables=None self.constructModel() def get_weiInit(self,shape): weiInit=tf.truncated_normal(shape) return tf.Variable(weiInit) def get_biasInit(self,shape): biasInit=tf.constant(0.1,shape=shape) return tf.Variable(biasInit) def constructModel(self): self.x = tf.placeholder(dtype=tf.float32,shape=[None,self.x_dimen]) self.y=tf.placeholder(dtype=tf.float32,shape=[None,10]) self.weight=self.get_weiInit([self.x_dimen,10]) self.bias=self.get_biasInit([10]) self.y_pre=tf.nn.softmax(tf.matmul(self.x,self.weight)+self.bias) self.correct_mat=tf.equal(tf.argmax(self.y_pre,1),tf.argmax(self.y,1)) #self.loss=tf.reduce_mean(tf.squared_difference(self.y_pre,self.y)) self.loss=-tf.reduce_sum(self.y*tf.log(self.y_pre)) self.train_step = tf.train.GradientDescentOptimizer(0.01).minimize(self.loss) self.accuracy=tf.reduce_mean(tf.cast(self.correct_mat,"float")) def next_batch(self,batchsize): start=self.epoch self.epoch+=batchsize if self.epoch>self._num_datas: perm=np.arange(self._num_datas) np.random.shuffle(perm) self.datas=self.datas[perm,:] self.lables=self.lables[perm,:] start=0 self.epoch=batchsize end=self.epoch return self.datas[start:end,:],self.lables[start:end,:] def train(self,x_train,y_train,x_test,y_test): self.datas=x_train self.lables=y_train self._num_datas=(self.lables.shape[0]) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(5000): batch=self.next_batch(100) sess.run(self.train_step,feed_dict={ self.x:batch[0], self.y:batch[1] }) if 1: train_loss = sess.run(self.loss, feed_dict={ self.x: batch[0], self.y: batch[1] }) print("setp %d,test_loss %f" % (i, train_loss)) #print("y_pre",sess.run(self.y_pre,feed_dict={ self.x: batch[0], # self.y: batch[1]})) #print("*****************weight********************",sess.run(self.weight)) print(sess.run(self.accuracy,feed_dict={self.x:x_test,self.y:y_test}))
然后是调用方法，包括了对这个mnist数据集的下载
from myTensorflowLinearModle import myLinearModel as mlm import tensorflow.examples.tutorials.mnist.input_data as input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) if __name__=='__main__': x_train,x_test,y_train,y_test=mnist.train.images,mnist.test.images,mnist.train.labels,mnist.test.labels linear = mlm(len(x_train[1])) linear.train(x_train,y_train,x_test,y_test)
下载方法来自tensorflow的官方文档中文版