CS285 2019 Tensorflow Tutorial（train a neural network）

训练一个简单回归问题的神经网络。 创建输入、变量、神经网络操作、均方误差损失、梯度下降优化器，并使用小批量数据运行优化器。

# -*- coding: utf-8 -*-

import os
import warnings
os.environ['TF_CPP_MIN_LOG_LEVEL']='3'
warnings.filterwarnings('ignore')

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.patches as mpatches

def tf_reset():
    try:
        sess.close()
    except:
        pass
    tf.reset_default_graph()
  
    return tf.Session()

#4. How to train a neural network for a simple regression problem
inputs = np.linspace(-2*np.pi, 2*np.pi, 10000)[:, None]
outputs = np.sin(inputs) + 0.05 * np.random.normal(size=[len(inputs),1])
plt.scatter(inputs[:, 0], outputs[:, 0], s=0.1, color='k', marker='o')    

sess =tf_reset()
def create_model():
    #create inputs
    input_ph=tf.placeholder(dtype=tf.float32,shape=[None,1])
    output_ph =tf.pl