可和这篇文章对比：https://blog.csdn.net/fanzonghao/article/details/81603367
# coding: utf-8
# ## MNIST数据集from __future__ import division, print_function, absolute_importimport tensorflow as tf# Import MNIST data，MNIST数据集导入
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)# In[2]:# Hyper-parameters，超参数
learning_rate = 0.001
num_steps = 500
batch_size = 128
display_step = 10# Network Parameters，网络参数
num_input = 784 # MNIST数据输入 (img shape: 28*28)
num_classes = 10 # MNIST所有类别 (0-9 digits)
dropout = 0.75 # Dropout, probability to keep units，保留神经元相应的概率# tf Graph input，TensorFlow图结构输入
X = tf.placeholder(tf.float32, [None, num_input])
Y = tf.placeholder(tf.float32, [None, num_classes])
keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)，保留i# In[3]:# Create some wrappers for simplicity，创建基础卷积函数，简化写法
def conv2d(x, W, b, strides=1):# Conv2D wrapper, with bias and relu activation，卷积层，包含bias与非线性relu激励x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')x = tf.nn.bias_add(x, b)return tf.nn.relu(x)def maxpool2d(x, k=2):# MaxPool2D wrapper，最大池化层return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME')# Create model，创建模型
def conv_net(x, weights, biases, dropout):# MNIST数据为维度为1，长度为784 (28*28 像素)的# Reshape to match picture format [Height x Width x Channel]# Tensor input become 4-D: [Batch Size, Height, Width, Channel]x = tf.reshape(x, shape=[-1, 28, 28, 1])# Convolution Layer，卷积层conv1 = conv2d(x, weights['wc1'], biases['bc1'])# Max Pooling (down-sampling)，最大池化层／下采样conv1 = maxpool2d(conv1, k=2)# Convolution Layer，卷积层conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])# Max Pooling (down-sampling)，最大池化层／下采样conv2 = maxpool2d(conv2, k=2)# Fully connected layer，全连接网络# Reshape conv2 output to fit fully connected layer input，调整conv2层输出的结果以符合全连接层的需求fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])fc1 = tf.nn.relu(fc1)# Apply Dropout，应用dropoutfc1 = tf.nn.dropout(fc1, dropout)# Output, class prediction，最后输出预测out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])return out# In[4]:# Store layers weight & bias 存储每一层的权值和全差
weights = {# 5x5 conv, 1 input, 32 outputs'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),# 5x5 conv, 32 inputs, 64 outputs'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),# fully connected, 7*7*64 inputs, 1024 outputs'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),# 1024 inputs, 10 outputs (class prediction)'out': tf.Variable(tf.random_normal([1024, num_classes]))
}biases = {'bc1': tf.Variable(tf.random_normal([32])),'bc2': tf.Variable(tf.random_normal([64])),'bd1': tf.Variable(tf.random_normal([1024])),'out': tf.Variable(tf.random_normal([num_classes]))
}# Construct model，构建模型
logits = conv_net(X, weights, biases, keep_prob)
prediction = tf.nn.softmax(logits)# Define loss and optimizer，定义误差函数与优化器
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)# Evaluate model，评估模型
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# Initialize the variables (i.e. assign their default value)，初始化图结构所有变量
init = tf.global_variables_initializer()# In[5]:# Start training，开始训练
with tf.Session() as sess:# Run the initializer，初始化sess.run(init)for step in range(1, num_steps+1):batch_x, batch_y = mnist.train.next_batch(batch_size)# Run optimization op (backprop)，优化sess.run(train_op, feed_dict={X: batch_x, Y: batch_y, keep_prob: dropout})if step % display_step == 0 or step == 1:# Calculate batch loss and accuracyloss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,Y: batch_y,keep_prob: 1.0})print("Step " + str(step) + ", Minibatch Loss= " + "{:.4f}".format(loss) + ", Training Accuracy= " + "{:.3f}".format(acc))print("Optimization Finished!")# Calculate accuracy for 256 MNIST test images，以每256个测试图像为例，print("Testing Accuracy:", sess.run(accuracy, feed_dict={X: mnist.test.images[:256],Y: mnist.test.labels[:256],keep_prob: 1.0}))
