从网上找到的一张图，很生动形象。
第一段代码：
import tensorflow as tf
import numpy as np
# 使用 NumPy 生成假数据(phony data), 总共 100 个点.
x_data = np.float32(np.random.rand(2, 100)) # 随机输入，float64->float32
y_data = np.dot([0.100, 0.200], x_data) + 0.300
#print(y_data)
# 构造一个线性模型
b = tf.Variable(tf.zeros([1]))
W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
y = tf.matmul(W, x_data) + b
# 最小化方差
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5) #学习率
train = optimizer.minimize(loss)
# 初始化变量
init = tf.initialize_all_variables()
# 启动图 (graph)
sess = tf.Session()
sess.run(init)
# 拟合平面
for step in range(0, 201):
sess.run(train)
if step % 20 == 0:
print(step, sess.run(W), sess.run(b))
# 得到最佳拟合结果 W: [[0.100 0.200]], b: [0.300]
一段图像识别算法：
原理图：
用数学语言描述：
argmax是指最大值所对应的下标
#coding:utf-8
import tensorflow as tf
import numpy as np
import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
#tf.placeholder形参
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
#参数设置
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
#y是推出每个y[0,10]的概率，所以是一个数组
y = tf.nn.softmax(tf.matmul(x,W) + b)
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)#梯度下降
#建立网络
sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
#训练网络
for i in range(1000):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})
#计算准确率
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
sess.close()
多层神经网络：加入卷积层与池化层
#coding:utf-8
import tensorflow as tf
import numpy as np
import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
sess = tf.InteractiveSession()
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)#正态分布，有正有负
return tf.Variable(initial)
def bias_variable(shape):#偏置项
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
#卷积与池化？？
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.initialize_all_variables())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))