import tensorflow as tf
import numpy as np
def add_layer(inputs,in_size,out_size,n_layer,activation_function=None):
# add one more layer and return the output of this layer
layer_name = 'layer%s' % n_layer
with tf.name_scope('layer'):
with tf.name_scope('weights'):
Weights = tf.Variable(tf.random_normal([in_size,out_size]),name='W')
tf.summary.histogram(layer_name+'/weights',Weights)
with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros([1,out_size]) + 0.1,name='b')
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.add(tf.matmul(inputs,Weights),biases)
tf.summary.histogram(layer_name+'/biases',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
# make up some real data
x_data =np.linspace(-1,1,300)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape)
y_data = np.square(x_data)-0.5+noise
with tf.name_scope('inputs'):
xs = tf.placeholder(tf.float32,[None,1],name='x_input')
ys = tf.placeholder(tf.float32,[None,1],name='y_input')
# create hidden layer
l1 = add_layer(xs,1,10,1,activation_function=tf.nn.relu)
# create output layer
prediction = add_layer(l1,10,1,2,activation_function=None)
# the error between prediction adn real data
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1]))
tf.summary.scalar('loss',loss)
with tf.name_scope('train'):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
sess = tf.Session()
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("logs/",sess.graph)
# import step
sess.run(tf.global_variables_initializer())
for i in range(1000):
sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
if i%50 == 0:
result = sess.run(merged,feed_dict={xs:x_data,ys:y_data})
writer.add_summary(result,i)