tensorflow学习（一）

今天开始学习tensorflow框架，从极客学院下载了官方中文教程(15年翻译的），第一天开始学习第一章ng基本流程和原理，作为前奏。然后写了代码，验证一下，准确率确实非常高，非常好用。把代码上传，作为以后备用。

 import tensorflow as tf
 import numpy as np
 import math

 class Model:
     def __init__(self,w = np.empty(None),b = None):
         self.b = b
         self.w = w

     def predict(self, input):
         return np.dot(input,self.w) + self.b

 data = np.float32(np.random.rand(1000,2))
 label = np.dot(data,np.array([[0.100],[0.200]])) + 0.3
 m,n = data.shape

 num_train = int(m * 0.6)
 num_validation = int(m * 0.2)
 num_test = int(m * 0.2)

 data_train = data[:num_train,:]
 data_validation = data[num_train:(num_train+num_validation),:]
 data_test = data[(num_train+num_validation):,:]

 label_train = label[:num_train,:]
 label_validation = label[num_train:(num_train+num_validation),:]
 label_test = label[(num_train+num_validation):,:]

 w = tf.Variable(tf.random_uniform([2,1],-1.0,1.0))
 b = tf.Variable(tf.zeros([1]))

 y_train = tf.matmul(data_train,w) + b
 loss = tf.reduce_mean(tf.square(label_train - y_train))
 '''
 bestModel = Model()
 minRMSE = (1 << 31) -1
 alphas = [0.1,0.3,0.5];
 iters = [100,150,200,250];
 for iter in iters:
     for alpha in alphas:
         optimizer = tf.train.GradientDescentOptimizer(alpha)
         train = optimizer.minimize(loss)
         init_state = tf.global_variables_initializer()
         with tf.Session() as sess:
             sess.run(init_state)
             for step in range(0,iter):
                 sess.run(train)
             model = Model(sess.run(w),sess.run(b))
             p = model.predict(data_validation)
             rmse = np.sqrt(np.mean(np.square(label_validation - p)))
             if rmse < minRMSE:
                 minRMSE = rmse
                 bestModel = model
 np.save("E:\\Python\\models\\weights.npy",bestModel.w)
 np.save("E:\\Python\\models\\b.npy",bestModel.b)
 '''

 weights = np.load("E:\\Python\\models\\weights.npy")
 b = np.load("E:\\Python\\models\\b.npy")

 model = Model(weights,b)
 predicts = model.predict(data_test)
 print(predicts)
 print(label_test)
 print(label_test - predicts)