Tensorflow 实战google深度学习框架 03

import tensorflow as tf
from numpy.random import RandomState
# 1. 定义神经网络的相关参数和变量。¶
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))
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 = 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)

# 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 ranges(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 steps(s) , w1 is: " %(i))
print sess.run(w1), "\n"
print "Final w1 is: \n", sess.run(w1)

# 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 = 1
loss_more = 10
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)

#6. 定义损失函数为MSE。
loss = 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 "Final w1 is: \n", sess.run(w1)