import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def add_layer(inputs,in_size,out_size,n_layer,activation_function=None):
layer_name = 'layer%s' % n_layer
with tf.name_scope(layer_name):
with tf.name_scope('weights'):
Weights = tf.Variable(tf.random_normal([in_size,out_size]))
tf.summary.histogram(layer_name+'weights',Weights)
with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros([1,out_size])+0.1)
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.matmul(inputs,Weights) + biases
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
tf.summary.histogram(layer_name + 'outputs', outputs)
return outputs
x_data = np.linspace(-1,1,300)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape) #加入方差为0.05和x_data格式相同的噪点
y_data = np.square(x_data)-0.5 + noise
with tf.name_scope('inputs'):
xs = tf.placeholder(tf.float32,[None,1],name='x_inputs')
ys = tf.placeholder(tf.float32,[None,1],name='y_inputs') #传值
l1 = add_layer(xs,1,10,n_layer=1,activation_function=tf.nn.relu) #添加层
predition = add_layer(l1,10,1,n_layer=2,activation_function=None)
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-predition),reduction_indices=[1],name='sum'),name='reduce') #计算loss
tf.summary.scalar('loss',loss)
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) #学习效率为0.1来减少loss的值
init = tf.global_variables_initializer()
sess = tf.Session()
merged = tf.summary.merge_all()
write = tf.summary.FileWriter("logs/",sess.graph)
sess.run(init)
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data,y_data)
plt.ion() #输出图像后不会暂停
for i in range(3000):
sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
if i % 50 == 0:
#print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
result = sess.run(merged,feed_dict={xs:x_data,ys:y_data})
write.add_summary(result,i)
plt.show()
``
生成的loss的图像
