# https://www.w3cschool.cn/tensorflow_python/tensorflow_python-bm7y28si.html import tensorflow as tf import numpy as np #生成随机数据,共100个点 x_data = np.float32(np.random.rand(2,100)) #随机输入,[0,1)之间 ,randn--正态分布 y_data = np.dot([0.100,0.200],x_data) + 0.300 #shape--[1*100] , <class 'numpy.ndarray'> #构造线性模型 b = tf.Variable(tf.zeros([1])) #创建变量 W = tf.Variable(tf.random_uniform([1,2],-1.0,1.0)) #均匀分布,在[-1,1]之间 y = tf.matmul(W,x_data) + b #最小化方差 loss = tf.reduce_mean(tf.square(y - y_data)) optimizer = tf.train.GradientDescentOptimizer(0.5) train = optimizer.minimize(loss) #常量-constant input1 = tf.constant(3.) input2 = tf.constant(2.) add = tf.add(input1,input2) mul = tf.multiply(input1,input2) #Feed-使用一个 tensor 值临时替换一个操作的输出结果 input3 = tf.placeholder(tf.float32) input4 = tf.placeholder(tf.float32) output = tf.multiply(input3,input4) #初始化变量 init = tf.global_variables_initializer() #新形式 #启动图 sess = tf.Session() sess.run(init) # with tf.device("/gpu:1"): #指定GPU Y = sess.run([output], feed_dict={input3:[6.],input4:[8.]}) Z = sess.run([mul,add]) # print(Y) for step in range(0,201): sess.run(train) if step%20 ==0: print(step, sess.run(W), sess.run(b)) sess.close() #释放源:source op
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