tencent_2.1_linear_regression_model

 linear_regression_model.py

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())

    def weight_variable(self, shape):
        initial = tf.truncated_normal(shape, stddev = 0.1)
        return tf.Variable(initial)

    def bias_variable(self, shape):
        initial = tf.constant(0.1, shape = shape)
        return tf.Variable(initial)

    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.bias_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.train_step = tf.train.AdamOptimizer(0.1).minimize(self.loss)

    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 train(self, x_train, y_train):
        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("step %d,test_loss %f" % (i, train_loss))

    def predict_batch(self,arr,batch_size):
        for i in range(0,len(arr),batch_size):
            yield arr[i:i+batch_size]

    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, feed_dict={self.x:x_test_batch})
            pred_list.append(pred)
        return np.vstack(pred_list)

 

run.py

from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from linear_regression_model import linearRegressionModel as lrm

if __name__ == '__main__':
    x, y = make_regression(7000)
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.5)
    y_lrm_train = y_train.reshape(-1, 1)
    y_lrm_test = y_test.reshape(-1, 1)

    linear = lrm(x.shape[1])
    linear.train(x_train, y_lrm_train)
    y_predict = linear.predict(x_test)
    print("Tensorflow R2: ", r2_score(y_predict.ravel(), y_lrm_test.ravel()))

    lr = LinearRegression()
    y_predict = lr.fit(x_train, y_train).predict(x_test)
    print("Sklearn R2: ", r2_score(y_predict, y_test))

 

结果：

ubuntu@VM-12-146-ubuntu:~$ python run.py
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step 10,test_loss 53487.046875
step 20,test_loss 36099.449219
step 30,test_loss 50567.339844
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step 50,test_loss 40298.109375
step 60,test_loss 40552.335938
step 70,test_loss 33812.503906
step 80,test_loss 39265.847656
step 90,test_loss 36639.019531
step 100,test_loss 38088.800781
step 110,test_loss 34145.976562
step 120,test_loss 34928.343750
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('Tensorflow R2: ', 0.99999761462739589)
('Sklearn R2: ', 1.0)
ubuntu@VM-12-146-ubuntu:~$