from __future__ import print_function
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def add_layer(inputs, in_size, out_size, activation_function=None):
with tf.name_scope('layer'):
with tf.name_scope('weights'):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
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)
return outputs
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
# plt.scatter(x_data, y_data)
# plt.show()
with tf.name_scope('inputs'):
xs = tf.placeholder(tf.float32, [None, 1],name='x_in')
ys = tf.placeholder(tf.float32, [None, 1],name='y_in')
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
prediction = add_layer(l1, 10, 1, activation_function=None)
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction), reduction_indices=[1]))
with tf.name_scope('train'):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
sess = tf.Session()
writer = tf.summary.FileWriter("logs/", sess.graph)
init = tf.global_variables_initializer()
sess.run(init)
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data, y_data)
plt.ion()
plt.show()
for i in range(1000):
sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
if i % 50 == 0:
try:
ax.lines.remove(lines[0])
except Exception:
pass
prediction_value = sess.run(prediction, feed_dict={xs: x_data})
lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
plt.pause(1)tensorboard –logdir logs
https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/4-1-tensorboard1/
4925
