TensorFlow入门示例教程

本部分的代码目前都是基于GitHub大佬非常详细的TensorFlow的教程上，首先给出链接：

https://github.com/aymericdamien/TensorFlow-Examples/

本人对其中部分代码做了注释和中文翻译，会持续更新，目前包括：

　　1. 传统多层神经网络用语MNIST数据集分类（代码讲解，翻译）

1. 传统多层神经网络用语MNIST数据集分类（代码讲解，翻译）

 

  1 """ Neural Network.
  2
  3 A 2-Hidden Layers Fully Connected Neural Network (a.k.a Multilayer Perceptron)
  4 implementation with TensorFlow. This example is using the MNIST database
  5 of handwritten digits (http://yann.lecun.com/exdb/mnist/).
  6
  7 Links:
  8     [MNIST Dataset](http://yann.lecun.com/exdb/mnist/).
  9
 10 Author: Aymeric Damien
 11 Project: https://github.com/aymericdamien/TensorFlow-Examples/
 12 """
 13
 14 from __future__ import print_function
 15
 16 # Import MNIST data
 17 # 导入mnist数据集
 18 from tensorflow.examples.tutorials.mnist import input_data
 19 mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
 20
 21 # 导入tf
 22 import tensorflow as tf
 23
 24 # Parameters
 25 # 设定各种超参数
 26 learning_rate = 0.1 # 学习率
 27 num_steps = 500   # 训练500次
 28 batch_size = 128  # 每批次取128个样本训练
 29 display_step = 100  # 每训练100步显示一次
 30
 31 # Network Parameters
 32 # 设定网络的超参数
 33 n_hidden_1 = 256 # 1st layer number of neurons
 34 n_hidden_2 = 256 # 2nd layer number of neurons
 35 num_input = 784 # MNIST data input (img shape: 28*28)
 36 num_classes = 10 # MNIST total classes (0-9 digits)
 37
 38 # tf Graph input
 39 # tf图的输入，因为不知道到底输入大小是多少，因此设定占位符
 40 X = tf.placeholder("float", [None, num_input])
 41 Y = tf.placeholder("float", [None, num_classes])
 42
 43 # Store layers weight & bias
 44 # 初始化w和b
 45 weights = {
 46     'h1': tf.Variable(tf.random_normal([num_input, n_hidden_1])),
 47     'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
 48     'out': tf.Variable(tf.random_normal([n_hidden_2, num_classes]))
 49 }
 50 biases = {
 51     'b1': tf.Variable(tf.random_normal([n_hidden_1])),
 52     'b2': tf.Variable(tf.random_normal([n_hidden_2])),
 53     'out': tf.Variable(tf.random_normal([num_classes]))
 54 }
 55
 56
 57 # Create model
 58 # 创建模型
 59 def neural_net(x):
 60     # Hidden fully connected layer with 256 neurons
 61     # 隐藏层1，全连接了256个神经元
 62     layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
 63     # Hidden fully connected layer with 256 neurons
 64     # 隐藏层2，全连接了256个神经元
 65     layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
 66     # Output fully connected layer with a neuron for each class
 67     # 最后作为输出的全连接层，对每一分类连接一个神经元
 68     out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
 69     return out_layer
 70
 71 # Construct model
 72 # 开启模型
 73 # 输入数据X，得到得分向量logits
 74 logits = neural_net(X)
 75 # 用softmax分类器将得分向量转变成概率向量
 76 prediction = tf.nn.softmax(logits)
 77
 78 # Define loss and optimizer
 79 # 定义损失和优化器
 80 # 交叉熵损失, 求均值得到---->loss_op
 81 loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
 82     logits=logits, labels=Y))
 83 # 优化器使用的是Adam算法优化器
 84 optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
 85 # 最小化损失得到---->可以训练的train_op
 86 train_op = optimizer.minimize(loss_op)
 87
 88 # Evaluate model
 89 # 评估模型
 90 # tf.equal() 逐个元素进行判断，如果相等就是True，不相等，就是False。
 91 correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
 92 # tf.cast() 数据类型转换----> tf.reduce_mean() 再求均值
 93 accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
 94
 95 # Initialize the variables (i.e. assign their default value)
 96 # 初始化这些变量（作用比如说，给他们分配随机默认值）
 97 init = tf.global_variables_initializer()
 98
 99 # Start training
100 # 现在开始训练啦！
101 with tf.Session() as sess:
102
103     # Run the initializer
104     # 运行初始化器
105     sess.run(init)
106
107     for step in range(1, num_steps+1):
108         # 每批次128个训练，取出这128个对应的data：x；标签：y
109         batch_x, batch_y = mnist.train.next_batch(batch_size)
110         # Run optimization op (backprop)
111         # train_op是优化器得到的可以训练的op，通过反向传播优化模型
112         sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
113         # 每100步打印一次训练的成果
114         if step % display_step == 0 or step == 1:
115             # Calculate batch loss and accuracy
116             # 计算每批次的是损失和准确度
117             loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,
118                                                                  Y: batch_y})
119             print("Step " + str(step) + ", Minibatch Loss= " + \
120                   "{:.4f}".format(loss) + ", Training Accuracy= " + \
121                   "{:.3f}".format(acc))
122
123     print("Optimization Finished!")
124
125     # Calculate accuracy for MNIST test images
126     # 看看在测试集上，我们的模型表现如何
127     print("Testing Accuracy:", \
128         sess.run(accuracy, feed_dict={X: mnist.test.images,
129                                       Y: mnist.test.labels}))