这些天试着玩一下ai
Step1：Tensorflow1.15环境的安装和配置
安装和配置TensorFlow
https://blog.csdn.net/weixin_40925977/article/details/107306079
换成TUNA的源
https://mirrors4.tuna.tsinghua.edu.cn/help/anaconda/
解决安装中的问题(Solving environment: failed with initial frozen solve. Retrying with flexible solve)
https://blog.csdn.net/hhhhhhhhhhwwwwwwwwww/article/details/112726892
Step2：用Tensorflow实战例子
用Tensorflow对MINST识别的训练和测试
https://blog.csdn.net/qq_32674829/article/details/82867900
用PyTorch对MINST识别的训练和测试
https://blog.csdn.net/weixin_44751294/article/details/116240084
MNIST数据集的简介
https://yunyaniu.blog.csdn.net/article/details/79094752
Stanford现实生活中房屋门牌号的数据集
http://ufldl.stanford.edu/housenumbers/
Step3：一些问题的解决
import trace error问题的解决
https://blog.csdn.net/weixin_45953051/article/details/126218041
小括号和中括号运用不当导致的出错以及解决。
https://blog.csdn.net/qq_41112170/article/details/124044453
下面是以上的阶段成果，就是copy了前面文章的例子，稍微改了一下(第一行的import和后面有个中括号改成小括号)能运行通过。
#-*- coding: utf-8 -*- from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf mnist = input_data.read_data_sets("C:/tensorflow/MNIST_data/", one_hot=True) # Parameters learning_rate = 0.01 training_epochs = 25 batch_size = 100 display_step = 1 # tf Graph Input x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784 y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes # Set model weights W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) # Construct model pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax # Minimize error using cross entropy cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) # Gradient Descent optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # Start training with tf.Session() as sess: sess.run(init) # Training cycle for epoch in range(training_epochs): avg_cost = 0. total_batch = int(mnist.train.num_examples/batch_size) # Loop over all batches for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) # Fit training using batch data _, c = sess.run((optimizer, cost), feed_dict={x: batch_xs,y: batch_ys}) # Compute average loss avg_cost += c / total_batch # Display logs per epoch step if (epoch+1) % display_step == 0: print("Epoch:", ' %04d ' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)) print ("Optimization Finished!") # Test model correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy for 3000 examples accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print ("Accuracy:", accuracy.eval({x: mnist.test.images[:3000], y: mnist.test.labels[:3000]}))
Step4：进阶实战
使用最新版本的Tensorflow测试Mnist
使用Stanford数据集测试
对比测试CUDA对训练时间的改善
不用anaconda配置环境
Ubuntu下环境的安装和对比
Step5：PyTorch环境配置和安装
原文地址：http://www.cnblogs.com/shinedream/p/16898255.html
