目录:
1.非线性回归
2.手写数字训练集
3.T3-3MNIST数据集分类简单版本
1.非线性回归
代码:
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
# 使用numpy生成200个随机点
x_data = np.linspace(-0.5, 0.5, 200)[:, np.newaxis] # 从-0.5到0.5,均匀分布的200个点,,,增加维度
noise = np.random.normal(0, 0.02, x_data.shape)
y_data = np.square(x_data) + noise
# 根据样本定义x,y
x = tf.placeholder(tf.float32, [None, 1])
y = tf.placeholder(tf.float32, [None, 1])
# 定义神经网络中间层
Weights_L1 = tf.Variable(tf.random.normal([1, 10])) # 1行10列,1个输入,10个输出(10个神经网络)
biases_L1 = tf.Variable(tf.zeros([1, 10]))
Wx_plus_b_L1 = tf.matmul(x, Weights_L1) + biases_L1
L1 = tf.nn.tanh(Wx_plus_b_L1)
# 定义输出层
Weights_L2 = tf.Variable(tf.random_normal([10, 1]))
biases_L2 = tf.Variable(tf.zeros([1, 1]))
Wx_plus_b_L2 = tf.matmul(L1, Weights_L2) + biases_L2
prediction = tf.nn.tanh(Wx_plus_b_L2)
# 二次代价函数
loss = tf.reduce_mean(tf.square(y - prediction))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(2000):
sess.run(train_step,feed_dict={x:x_data,y:y_data})
# 获得预测值
prediction_value = sess.run(prediction, feed_dict={x: x_data})
# 画图
plt.figure()
plt.scatter(x_data, y_data)
plt.plot(x_data, prediction_value, 'r-', lw=5)
plt.show()
2.手写数字训练集
数据集下载地址:http://yann.lecun.com/exdb/mnist/
3.T3-3MNIST数据集分类简单版本
扫描二维码关注公众号,回复:
5031100 查看本文章
结果:
代码:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
#每个批次的大小
batch_size = 100
#计算一共有多少个批次
n_batch = mnist.train.num_examples
x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32,[None,10])
#创建一个简单的神经网络
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
prediction = tf.nn.softmax(tf.matmul(x,W)+b)
#二次代价函数
loss = tf.reduce_mean(tf.square(y - prediction))
#使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
#初始化变量
init = tf.global_variables_initializer()
#结果对比(true,false)存放在一个bool型列表中
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置
#求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
with tf.Session() as sess:
sess.run(init)
for epoch in range(21):
for betch in range(n_batch):
batch_xs,batch_ys = mnist.train.next_batch(batch_size)
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
print("Iter"+str(epoch)+",Testing Accuracy"+str(acc))