tensorflow 曲線擬合

Python代碼：

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
# from tensorflow.examples.tutorials.mnist import input_data

# creating data
mu,sigma=0, 0.1
data_size = 300
x_data = np.linspace(-1, 1,data_size)[:, np.newaxis]
# noise = np.random.normal(0,0.05, x_data.shape)
y_data = np.sign(x_data)

# mnist data
# mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# x_data, y_data = mnist.train.next_batch(300)

# input layer
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])

# layer function
def layer(data_in, size, func = None):
    w = tf.Variable(tf.random_normal(size))
    b = tf.Variable(tf.zeros([1, size[1]]))
    z = tf.matmul(data_in, w) + b
    if(func):
        data_out = func(z)
    else:
        data_out = z
    return data_out

# hidden layer
output1 = layer(xs, [1, 10], tf.nn.relu)
output2 = layer(output1, [10, 20], tf.nn.softmax)
output3 = layer(output2, [20, 20], tf.nn.relu)
output4 = layer(output3, [20, 10], tf.nn.softmax)
output5 = layer(output4, [10, 10], tf.nn.relu)

# output layer
out = layer(output5, [10, 1])

# loss function
# loss = tf.reduce_sum(ys * tf.log(out))
loss = tf.reduce_mean(tf.reduce_sum(tf.square((out - ys))))

# trainning method
# train = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
train = tf.train.AdamOptimizer().minimize(loss)

# init all variables
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

# print loss value for every 50 times loop
print_step = 50
# loop less than 50 * 1000
loop_max_count = 1000
while True:
    print_step -= 1
    _,loss_value = sess.run([train,loss],feed_dict={xs:x_data,ys:y_data})
    if(print_step == 0):
        print(loss_value)
        print_step = 50
        loop_max_count -= 1 
    if(loss_value < .00001 or loop_max_count <= 0):
        break

# print loop times and show the output
print("loop_count = ", (1000 - loop_max_count) * 50)
y_out = sess.run(out, feed_dict={xs:x_data})
plt.plot(x_data, y_out, label="out")
plt.plot(x_data, y_data, label="in")
plt.show()
 

可以用來看看不同數目的隱含層和不同的激活函數對曲線擬合的訓練性能和訓練結果有何影響。

tensorflow 曲線擬合