tensorflow04 《TensorFlow实战Google深度学习框架》笔记-04-02自定义损失函数

# 《TensorFlow实战Google深度学习框架》04 深层神经网络# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:ts04.02.py 自定义损失函数# 不同的损失函数会对训练模型产生重要影响import tensorflow as tffrom numpy.random import RandomState# 1. 定义神经网络的相关参数和变量batch_size = 8x = 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))y = tf.matmul(x, w1)# 2. 设置自定义的损失函数# 定义损失函数使得预测少了的损失大，于是模型应该偏向多的方向预测。loss_less = 10loss_more = 1loss = 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)# 3. 生成模拟数据集rdm = RandomState(1)X = rdm.rand(128,2)Y = [[x1+x2+(rdm.rand()/10.0-0.05)] for (x1, x2) in X]# 4. 训练模型with tf.Session() as sess:    init_op = tf.global_variables_initializer()    sess.run(init_op)    STEPS = 5000    for i in range(STEPS):        start = (i*batch_size) % 128        end = (i*batch_size) % 128 + batch_size        sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})        if i % 1000 == 0:            print("After %d training step(s), w1 is: " % (i))            print(sess.run(w1), "\n")    print("[loss_less=10 loss_more=1] Final w1 is: \n", sess.run(w1))'''After 0 training step(s), w1 is: [[-0.81031823] [ 1.4855988 ]] After 1000 training step(s), w1 is: [[ 0.01247113] [ 2.13854504]] After 2000 training step(s), w1 is: [[ 0.45567426] [ 2.17060685]] After 3000 training step(s), w1 is: [[ 0.69968736] [ 1.84653103]] After 4000 training step(s), w1 is: [[ 0.89886677] [ 1.29736042]] [loss_less=10 loss_more=1] Final w1 is:  [[ 1.01934707] [ 1.04280913]] '''# 5. 重新定义损失函数，使得预测多了的损失大，于是模型应该偏向少的方向预测loss_less = 1loss_more = 10loss = 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)with tf.Session() as sess:    init_op = tf.global_variables_initializer()    sess.run(init_op)    STEPS = 5000    for i in range(STEPS):        start = (i*batch_size) % 128        end = (i*batch_size) % 128 + batch_size        sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})        if i % 1000 == 0:            print("After %d training step(s), w1 is: " % (i))            print(sess.run(w1), "\n")    print("[loss_less=1 loss_more=10] Final w1 is: \n", sess.run(w1))'''After 0 training step(s), w1 is: [[-0.81231821] [ 1.48359871]] After 1000 training step(s), w1 is: [[ 0.18643527] [ 1.07393336]] After 2000 training step(s), w1 is: [[ 0.95444274] [ 0.98088616]] After 3000 training step(s), w1 is: [[ 0.95574027] [ 0.9806633 ]] After 4000 training step(s), w1 is: [[ 0.95466018] [ 0.98135227]] [loss_less=1 loss_more=10] Final w1 is:  [[ 0.95525807] [ 0.9813394 ]]'''# 6. 定义损失函数为MSEloss = tf.losses.mean_squared_error(y, y_)train_step = tf.train.AdamOptimizer(0.001).minimize(loss)with tf.Session() as sess:    init_op = tf.global_variables_initializer()    sess.run(init_op)    STEPS = 5000    for i in range(STEPS):        start = (i*batch_size) % 128        end = (i*batch_size) % 128 + batch_size        sess.run(train_step, feed_dict={x: X[start:end], y_: Y[start:end]})        if i % 1000 == 0:            print("After %d training step(s), w1 is: " % (i))            print(sess.run(w1), "\n")    print("[losses.mean_squared_error]Final w1 is: \n", sess.run(w1))'''After 0 training step(s), w1 is: [[-0.81031823] [ 1.4855988 ]] After 1000 training step(s), w1 is: [[-0.13337614] [ 1.81309223]] After 2000 training step(s), w1 is: [[ 0.32190299] [ 1.52463484]] After 3000 training step(s), w1 is: [[ 0.67850214] [ 1.25297272]] After 4000 training step(s), w1 is: [[ 0.89473999] [ 1.08598232]] [losses.mean_squared_error]Final w1 is:  [[ 0.97437561] [ 1.0243336 ]]'''