TensorFlow搭建简单的神经网络
本篇博客主要介绍使用TensorFlow来搭建简单的神经网络,需要用到python中TensorFlow和numpy模块,下面是示例代码。
# encoding:utf-8
import tensorflow as tf
import numpy as np
# 添加层
def add_layer(inputs, in_size, out_size, activation_function=None):
W = tf.Variable(tf.random_normal([in_size, out_size]))
b = tf.Variable(tf.zeros([1, out_size]) + 0.1)
Wx_plus_b = tf.matmul(inputs, W) + b
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs
# 生成输入数据、噪点和输出数据
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
xs = tf.placeholder(tf.float32, [None, 1])
ys = tf.placeholder(tf.float32, [None, 1])
# 隐藏层和输出层
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
prediction = add_layer(l1, 10, 1, activation_function=None)
# 损失值
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
reduction_indices=[1]))
# 用梯度下降更新loss
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# 初始化所有参数
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
# 训练1000次
for i in range(1000):
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}))