学习进度笔记13

观看Tensorflow案例实战视频课程13 模型的保存和读取

import tensorflow as tf

v1=tf.Variable(tf.random_normal([1,2]),name="v1")
v2=tf.Variable(tf.random_normal([2,3]),name="v2")
init_op=tf.global_variables_initializer()
saver=tf.train.Saver()
with tf.Session() as sess:
    sess.run(init_op)
    print("V1:",sess.run(v1))
    print("V2:",sess.run(v2))
    saver_path=saver.save(sess,"save/model.ckpt")
    print("Model saved in file：",saver_path)

import tensorflow as tf
v1=tf.Variable(tf.random_normal([1,2]),name="v1")
v2=tf.Variable(tf.random_normal([2,3]),name="v2")
saver=tf.train.Saver()

with tf.Session() as sess:
    saver.restore(sess,"save/model.ckpt")
    print("V1:",sess.run(v1))
    print("V2:",sess.run(v2))
    print("Model restored")

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import input_data

mnist=input_data.read_data_sets('data/',one_hot=True)
trainimg=mnist.train.images
trainlabel=mnist.train.lables
testimg=mnist.test.images
testlabel=mnist.test.labels
print("MNIST ready")

n_input=784
n_output=10
weights={
    'wc1':tf.Variable(tf.random_normal([3,3,1,64],stddev=0.1)),
    'wc2':tf.Variable(tf.random_normal([3,3,64,128],stddev=0.1)),
    'wd1':tf.Variable(tf.random_normal([7*7*128,1024],stddev=0.1)),
    'wd2':tf.Variable(tf.random_normal([1024,n_output],stddev=0.1))
    }
biases={
    'bc1':tf.Variable(tf.random_normal([64],stddev=0.1)),
    'bc2':tf.Variable(tf.random_normal([128],stddev=0.1)),
    'bd1':tf.Variable(tf.random_normal([1024],stddev=0.1)),
    'bd2':tf.Variable(tf.random_normal([n_output],stddev=0.1))
    }

def conv_basic(_input,_w,_b,_keepratio):
    #INPUT
    _input_r=tf.reshape(_input,shape=[-1,28,28,1])
    #CONV LAYER 1
    _conv1=tf.nn.conv2d(_input_r,_w['wc1'],strides=[1,1,1,1],padding='SAME')
    #_mean,_var=tf.nn.moments(_conv1,[0,1,2])
    #_conv1=tf.nn.batch_normalization(_conv1,_mean,_var,0,1,0.0001)
    _conv1=tf.nn.relu(tf.nn.bias_add(_conv1,_b['bc1']))
    _pool1=tf.nn.max_pool(_conv1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
    _pool_dr1=tf.nn.dropout(_pool1,_keepratio)
    #CONV LEYER 2
    _conv2=tf.nn.conv2d(_pool_dr1,_w['wc2'],strides=[1,1,1,1],padding='SAME')
    #_mean,_var=tf.nn.moments(_conv2,[0,1,2])
    #_conv2=tf.nn.batch_normalization(_conv2,_mean,_var,0,1,0.0001)
    _conv2=tf.nn.relu(tf.nn.bias_add(_conv2,_b['bc2']))
    _pool2=tf.nn.max_pool(_conv2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
    _pool_dr2=tf.nn.dropout(_pool2,_keepratio)
    #VECTORIZE
    _densel=tf.reshape(_pool_dr2,[-1,_w['wd1'].get_shape().as_list()[0]])
    #FULLY CONNECTED LAVER 1
    _fc1=tf.nn.relu(tf.add(tf.matmul(_densel,_w['wd1']),_b['bd1']))
    _fc_dr1=tf.nn.dropout(_fc1,_keepratio)
    #FULLY CONNECTED LAVER 2
    _out=tf.add(tf.matmul(_fc_dr1,_w['wd2']),_b['bd2'])
    #RETURN
    out={'input_r':_input_r,'conv1':_conv1,'pool1':_pool1,'pool1_dr1':_pool_dr1,
        'conv2':_conv2,'pool2':_pool2,'pool_dr2':_pool_dr2,'densel':_densel,
        'fc1':_fc1,'fc_dr1':_fc_dr1,'out':_out 
    }
    return out
print("CNN READY")

x=tf.placeholder(tf.float32,[None,n_input])
y=tf.placeholder(tf.float32,[None,n_output])
keepratio=tf.placeholder(tf.float32)

#FUNCTIONS

_pred=conv_basic(x,weights,biases,keepratio)['out']
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred,y))
optm=tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
_corr=tf.equal(tf.argmax(_pred,1),tf.argmax(y,1))
accr=tf.reduce_mean(tf.cast(_corr,tf.float32))
init=tf.global_variables_initializer()

#SAVER
save_step=1
saver=tf.train.Saver(max_to_keep=3)

print("GRAPH READY")

#do_train=1
do_train=0
sess=tf.Session()
sess.run(init)

training_epochs=15
batch_size=16
display_step=1
if do_train==1:
    for epoch in range(training_epochs):
        avg_cost=0
        #total_batch=int(mnist.train.num_examples/batch_size)
        total_batch=10
        #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
            sess.run(optm,feed_dict={x:batch_xs,y:batch_ys,keepratio:0.7})
            #Compute average loss
            avg_cost+=sess.run(cost,feed_dict={x:batch_xs,y:batch_ys,keepratio:1.})/total_batch
    
        #Display logs per epoch step
        if epoch % display_step==0:
            print("Epoch:%03d/%03d cost:%.9f" % (epoch,training_epochs,avg_cost))
            train_acc=sess.run(accr,feed_dict={x:batch_xs,y:batch_ys,keepratio:1.})
            print("Training accuracy:%.3f" % (train_acc))
            #test_acc=sess.run(accr,feed_dict={x:testimg,y:testlabel,keepratio:1.})
            #print("Test accuracy:%.3f" % (test_acc))
        
        #Save Net
        if epoch % save_step==0:
            saver.save(sess,"save/nets/cnn_mnist_basic.ckpt-"+str(epoch))

print("OPTIMIZATION FINISHED")

if do_train==0:
    epoch=training_epochs-1
    saver.restore(sess,"save/nets/cnn_mnist_basic.ckpt-"+str(epoch))
    
    test_acc=sess.run(accr,feed_dict={x:testimg,y:testlabel,keepratio:1.})
    print("TEST ACCURACY:%.3f" % (test_acc))