Tensorflow%20实战Google深度学习框架 4.2.2 自定义损失函数源代码-优快云博客

本文介绍使用TensorFlow 4.1版本进行线性模型训练的过程。通过随机生成的数据集，构建了一个包含输入层和输出层的简单模型，并使用Adam优化器最小化损失函数。在5000次迭代中，模型参数w1逐渐调整以优化预测结果。

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import os
import tab
import tensorflow as tf
from numpy.random import RandomState
print "hello tensorflow 4.1"

batch_size = 8


x = tf.placeholder(tf.float32,shape=(None,2),name='x-input')
y_ = tf.placeholder(tf.float32,shape=(None,1),name='y-input')


w1 = tf.Variable(tf.random_normal([2,1],stddev=1,seed=1))
#w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))
y = tf.matmul(x,w1)

#a = tf.matmul(x,w1)
#y = tf.matmul(a,w2)

loss_less = 10
loss_more = 1
loss = tf.reduce_sum(tf.where(tf.greater(y,y_),(y-y_)*loss_more,(y_-y)*loss_less))
train_step = tf.train.AdamOptimizer(0.001).minimize(loss)


rdm = RandomState(1)
dataset_size = 128
X = rdm.rand(dataset_size,2)
Y = [[x1 + x2 +rdm.rand()/10.0-0.05] for (x1 ,x2 ) in X]


with tf.Session() as sess:
    init_op = tf.global_variables_initializer()
    sess.run(init_op)
    print sess.run(w1)
    STEPS = 5000
    for i in range(STEPS):
        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]} )
        print sess.run(w1)


print "end "

转载于:https://www.cnblogs.com/a9999/p/9916545.html