Tensor flow小案例——03简易神经网络(预测抛物线)

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# 定义神经网络中的隐藏层
def add_layer(inputs, size_inputs, size_outputs, act_fun = None):
    Weight = tf.Variable(tf.random_normal([size_inputs, size_outputs]))
    biases = tf.Variable(tf.zeros([1, size_outputs]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, Weight) + biases
    if act_fun == None:
        output = Wx_plus_b
    else:
        output = act_fun(Wx_plus_b)
    return output

# 定义训练的数据
x_data = np.linspace(-10, 10, 300)[:, np.newaxis]
# 噪音,使得数据更加真实
noise = np.random.normal(0, 5, x_data.shape).astype(np.float32)
# 大体趋势:Y = X^2 + 3
y_data = np.square(x_data) + 3 + noise

# 定义输入变量
xs = tf.placeholder(tf.float32, [None, 1], name = 'x_in')
ys = tf.placeholder(tf.float32, [None, 1], name = 'y_in')

# 实例化隐藏层l1,1个输入值,20个输出值,即有20个节点
l1 = add_layer(xs, 1, 20, act_fun = tf.nn.relu)
# 得到l1的输出数据,并输出最终的数据,20个输入值,1个输出值,即有1个节点
prediction = add_layer(l1, 20, 1)

# 定义代价函数
loss = tf.reduce_mean(tf.reduce_sum(tf.square(prediction - ys), reduction_indices=[1]))
# 定义训练方法:梯度下降;  指定学习率:0.001(一定不要太大,否则会NAN);  训练目的:最小化loss
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss)

# 初始化全部变量
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

# 迭代20,000次
for i in range(20000):
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})

# 测试数据
X = np.arange(-10, 11)
# 将X转置
X = X.reshape([21, 1])
# 得到预测结果Y
Y = sess.run(prediction, feed_dict={xs: X})

# 画出源数据
plt.scatter(x_data, y_data, color = 'b')
# 画出预测直线
plt.plot(X, Y, color = 'r')
# 显示结果
plt.show()

 

 

Tensor flow小案例——03简易神经网络(预测抛物线)_第1张图片

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