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import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
BATCH_SIZE = 30 #定义一次喂入的数据为30个
seed = 2 #################1.建立数据集，并画出可视化的散点#####################
#基于seed产生随机数
rng = np.random.RandomState(seed)
X = rng.randn(300,2) #300行2列。均值为0，方差为1的正态分布数据。
Y_=[int(x0*x0+x1*x1 < 2) for (x0,x1) in X] #若平方和<2，则标注为1，赋给Y_;否则标注为0，赋给Y_
Y_c =[['red' if y else 'blue']for y in Y_] #遍历Y_，如果不为0，标注为'red'；为0标注为'blue' X = np.vstack(X).reshape(-1,2) #让X是n行两列。
Y_ = np.vstack(Y_).reshape(-1,1) #让Y_是n行一列。 print(X)
print(Y_)
print(Y_c) plt.scatter(X[:,0],X[:,1],c=np.squeeze(Y_c)) #从Y_c中取对应颜色赋给c
plt.show()
##################2. 定义神经网络的输入输出参数，定义前向传播过程########
def get_weight(shape,regularizer): #为了方便，定义了一个生成参数w的函数。参数为shape和正则化权重。
w=tf.Variable(tf.random_normal(shape),dtype=tf.float32)
tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
return w
def get_bias(shape):
b=tf.Variable(tf.constant(0.01,shape=shape))
return b x=tf.placeholder(tf.float32,shape=(None,2))
y_=tf.placeholder(tf.float32,shape=(None,1)) w1=get_weight([2,11],0.01) #w1为2行11列。注意shape是列表的形式给出的。正则化权重为0.01
b1=get_bias([11]) y1=tf.nn.relu(tf.matmul(x,w1)+b1) w2=get_weight([11,1],0.01)
b2=get_bias([1]) y=tf.matmul(y1,w2)+b2 #输出层不过激活函数
##################3. 定义损失函数#############
#定义损失函数
loss_mse = tf.reduce_mean(tf.square(y-y_)) #均方误差损失函数
loss_total = loss_mse + tf.add_n(tf.get_collection('losses')) #均方误差损失函数，加上每一个正则化w的损失
#################4.1 定义反向传播过程：不含正则化。########################
train_step = tf.train.AdamOptimizer(0.0001).minimize(loss_mse) #这个损失函数不包括正则化
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
STEPS = 40000
for i in range(STEPS):
start = (i*BATCH_SIZE) % 300
end = start + BATCH_SIZE
sess.run(train_step,feed_dict={x:X[start:end],y_:Y_[start:end]})
if i%2000 ==0:
loss_mse_v = sess.run(loss_mse,feed_dict={x:X,y_:Y_}) #每2000轮打印一下loss值
print("After %d steps,loss is: %f" %(i,loss_mse_v))
xx,yy = np.mgrid[-3:3:0.01,-3:3:0.01] #生成网格坐标点
grid = np.c_[xx.ravel(),yy.ravel()] #将xx,yy拉直，合并成一个2列的矩阵，得到一个网格坐标点的集合
probs = sess.run(y,feed_dict={x:grid}) #将网格坐标点喂入神经网络，probs为输出
probs = probs.reshape(xx.shape) #probs的shape调整成xx的样子
print("w1:",sess.run(w1))
print("w2:",sess.run(b1))
print("w3:",sess.run(w2))
print("w4:",sess.run(b2))
plt.scatter(X[:,0],X[:,1],c=np.squeeze(Y_c))
plt.contour(xx,yy,probs,levels=[.5]) #给所有值为0.5的点上色
plt.show()
#####################4.2 定义反向传播方法：包含正则化###################
train_step = tf.train.AdamOptimizer(0.0001).minimize(loss_total) #包含正则化
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
STEPS=40000
for i in range(STEPS):
start = (i*BATCH_SIZE)%300
end= start + BATCH_SIZE
sess.run(train_step,feed_dict={x:X[start:end],y_:Y_[start:end]})
if i% 2000 ==0:
loss_v = sess.run(loss_total,feed_dict={x:X,y_:Y_})
print("after %d steps,loss is %f" % (i,loss_v))
xx,yy=np.mgrid[-3:3:0.01,-3:3:0.01]
grid = np.c_[xx.ravel(),yy.ravel()]
probs = sess.run(y,feed_dict={x:grid})
probs = probs.reshape(xx.shape)
print("w1:",sess.run(w1))
print("w2:",sess.run(b1))
print("w3:",sess.run(w2))
print("w4:",sess.run(b2))
plt.scatter(X[:,0],X[:,1],c=np.squeeze(Y_c))
plt.contour(xx,yy,probs,levels=[.5]) #给所有值为0.5的点上色
plt.show() |
