二、进阶
Question：
After you train a model in Tensorflow:
1. How do you save the trained model?
2. How do you later restore this saved model?
在入门过程中是使用了saver = tf.train.import_meta_graph('保存的模型文件')
saver.restore(sess,tf.train.latest_checkpoint('指定CKPT文件')) 方法来保存并恢复图中的变量。
程序设计目标
下面我们会将MNIST--手写数字识别为例,回答上述的连个问题。
首先是构建CNN模型，（如何构建，请参考[专题1]TensorFlow构造神经网络(1)）
然后再使用另一个文件去恢复保存的图以及模型,并在另一个文件里使用保存的图来做预测.
Train and Save
#-*-coding:UTF-8-*-
import tensorflow as tf
'''
@todo: train and save MNIST model
@author: lee
@Date: 2017 12/11
'''
from tensorflow.examples.tutorials.mnist import input_data
with tf.name_scope('dropout'):
keep_prob = tf.placeholder(tf.float32,name='keep_prob')
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
with tf.name_scope('fc2'):
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.add(tf.matmul(h_fc1_drop, W_fc2), b_fc2, name='output')
return y_conv, keep_prob
# define basic ops
def con2d(x,W):
'''
conv2D return a 2D convolution layer with full stride
'''
return tf.nn.conv2d(x,W,stride=[1,1,1,1],padding='SAME')
def max_pool2x2(x): # max_pool2x2
'''
max pool ops, with classical strides
'''
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1, 2, 2, 1],padding='SAME')
def weight_variable(shape):
'''
weight variable generates a weight variable of a given shape
'''
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shapes):
'''
'''
initial = tf.constant(0.1,shape=shapes)
return tf.Variable(initial)
def conv2d(x,W):
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
def pool2d(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
# define training model
def model(x):
'''
args:
x : an input tensor with dimensions(N_samples,784), where 784 is the number of pixels in a standard MNIST image
return a tupls(y, keep_prob) , where y is a tensor of shape(N_samples,10), with values equals to the logits of classifiing the digit into one of 10 classes(0-9), keep_prob is a scalar placeholder for probability od droupout.
'''
# 1st Layer
with tf.name_scope('reshape'):
x_image= tf.reshape(x,[-1,28,28,1]) #[bathes,hight,width,channels]
with tf.name_scope('conv1'):
W_conv1=weight_variable([5,5,1,32]) #[conv_W, conv_H,conv_Deep(channel bbefore conv),connal after conv]
b_conv1=bias_variable([32])
h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
with tf.name_scope('pool1'):
h_pool1 = max_pool2x2(h_conv1)
with tf.name_scope('conv2'):
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)
with tf.name_scope('pool2'):
h_pool2 = max_pool2x2(h_conv2)
with tf.name_scope('fc1'):
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)
# impoty data
# /home/lee/data
mnist=input_data.read_data_sets('/home/lee/data/', one_hot=True)
x=tf.placeholder(tf.float32, [None, 784], name='input_x')
y_=tf.placeholder(tf.float32, [None, 10], name='input_y_')
# Build the graph for the deep net
y_conv, keep_prob = model(x)
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y_conv)
cross_entropy = tf.reduce_mean(cross_entropy)
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
with tf.name_scope('accuracy'):
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
correct_prediction = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
graph_location = '/tmp/graph'
print('Saving graph to: %s' % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
saver=tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(300): #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))
save_path='/tmp/tf_model/model_'+'%d'%i
print('%s' % save_path)
saver.save(sess, save_path)
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
以上程序片段为将训练好的模型保存下来。保存的位置是可以通过自己写的输出看出来的:
Restory
模型在/tmp/tf_model/ 目录下, 接下来在入门的基础上,进行修改:
导入tensorfow 工具库; 导入手写体使用工具库.
#*-*coding:UTF-8-*-
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('/home/lee/data/',one_hot=True)
恢复文件:
sess1 = tf.Session()
saver1 = tf.train.import_meta_graph('/tmp/tf_model/model_200.meta') # load graph
saver1.restore(sess1, '/tmp/tf_model/model_200') # load variables (remember no extension of file)
# saver1.restore(sess1, tf.train.latest_checkpoint('G:/tf_model/')) # or this
graph = tf.get_default_graph() # get graph
恢复输入和输出所需要的tensor
x = graph.get_tensor_by_name('input_x:0') # input image
y_ = graph.get_tensor_by_name('input_y_:0') # input label
y_conv = graph.get_tensor_by_name('fc2/output:0') # output result from deepNN (predict label)
keep_prob_ = graph.get_tensor_by_name('dropout/keep_prob:0') # keep probability
测试所需要的操作:
# accuracy calculation
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) # compare predict result and label
correct_prediction = tf.cast(correct_prediction, tf.float32) # convert bool to float
accuracy1 = tf.reduce_mean(correct_prediction) # calculate mean accuracy
for j in range(10): testSample_start = j * 50 # start num of test sample of one batch testSample_end = (j + 1) * 50 # end num of test sample of one batchprint('test %d accuracy %g' % (j, accuracy1.eval(session=sess1, feed_dict={x:mnist.test.images[testSample_start:testSample_end], y_: mnist.test.labels[testSample_start:testSample_end], keep_prob_: 1.0}))) # feed data for test
输出:
