TensorFlow - 手写数字识别 (模型的存储与加载)

TensorFlow - 手写数字识别 (模型的存储与加载)

flyfish

目的：解决训练过程的中断，再次训练从上次训练之后结果接着训练

而不是从头开始训练

环境Win10 Python3.6

import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
import os

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])

W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)

def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)

#权重初始化
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')

#第一层卷积
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)

#d第二层卷积
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)

#全连接层
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)

#输出层
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

#训练和评估模型
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.AdagradOptimizer(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"))

ckpt_dir = "./ckpt_dir"
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
#标志变量不参与到训练中
global_step = tf.Variable(0, name='global_step', trainable=False)
saver = tf.train.Saver()

with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(ckpt_dir)
if ckpt and ckpt.model_checkpoint_path:
print(ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path) # restore all variables
else:
tf.global_variables_initializer().run()

start = global_step.eval() # get last global_step
print("Start from:", start)

for i in range(start, 200):#这里原来是20000 接着从上次start的地方训练
batch = mnist.train.next_batch(50)
if i%10 == 0:
train_accuracy = accuracy.eval(session=sess,feed_dict={
x:batch[0], y_: batch[1], keep_prob: 1.0})
print ("step %d, training accuracy %g"%(i, train_accuracy))

train_step.run(session=sess,feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

global_step.assign(i).eval()  #i更新global_step.
saver.save(sess, ckpt_dir + "/model.ckpt", global_step=global_step)

print ("test accuracy %g"%accuracy.eval(session=sess,feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

Start from: 0

step 0, training accuracy 0.08

step 10, training accuracy 0.12

step 20, training accuracy 0.22

step 30, training accuracy 0.2

step 40, training accuracy 0.24

Process finished with exit code 1

中断再次启动之后又接着上次开始训练

Start from: 42

step 50, training accuracy 0.52

模型存储目录

E:\MyWork\venv\ckpt_dir