import tensorflow as tf from numpy.random import RandomState def test(): print("test start") #定义训练数据batch的大小 batch_size = 8 #定义神经网络参数 w1 = tf.Variable(tf.random_normal([2,3],stddev = 1 , seed = 1)) w2 = tf.Variable(tf.random_normal([3,1],stddev = 1 , seed = 1)) x = tf.placeholder(tf.float32,shape = (None,2),name="x-input") y_ = tf.placeholder(tf.float32,shape = (None,1),name="y_input") #定义神经网络向前传播的过程 a = tf.matmul(x,w1) y = tf.matmul(a,w2) #定义损失函数(没看懂) cross_entropy = -tf.reduce_mean( y_ * tf.log( tf.clip_by_value( y , 1e-10 , 1.0 ))) train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy) rdm = RandomState(1) dataset_size = 128 X = rdm.rand(dataset_size,2) print(X) Y = [ [int( x1 + x2 < 1)] for (x1, x2) in X] print(Y) #创建会话 with tf.Session() as sess: #初始化变量; init_op = tf.initialize_all_variables() sess.run(init_op) print( sess.run(w1) ) print( sess.run(w2) ) #设定训练次数; step = 5000 for i in range(step): start = (i * batch_size) % dataset_size end = min(start+batch_size,dataset_size) sess.run( train_step , feed_dict = { x : X[ start : end ] , y_ : Y [start : end ]}) if i % 1000 == 0: total_cross_entropy = sess.run( cross_entropy,feed_dict = { x : X ,y_: Y}) print ("After %d tranining setp(s),cross entropy on all data is %g"% ( i , total_cross_entropy) ) print( sess.run(w1) ) print( sess.run(w2) ) if __name__=="__main__": test()
Tensorflow Test1
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转载自blog.csdn.net/d710055071/article/details/80470380
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