案例五：通过tensorflow实现一个简单的卷积神经网络
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.labels
testimg = mnist.test.images
testlabel = mnist.test.labels
print ("MNIST ready")
n_input = 784 #像素点是784个
n_output = 10 #10分类
weights = {
'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64], stddev=0.1)), #卷积核的h，卷积核的w， 输入深度，输出特征图个数（输出深度）
'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)), #在第二个pooling层之后得到的是一个7*7*128
'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输入层，对输入格式转换成tensorflow支持的格式即四维 其中-1表示让tensorflow自己推断
_input_r = tf.reshape(_input, shape=[-1, 28, 28, 1])
# CONV LAYER 1 调用tensorflow的nn模块，如果对conv2d不了解，可以print(help(tf.nn.conv2d))
_conv1 = tf.nn.conv2d(_input_r, _w['wc1'], strides=[1, 1, 1, 1], padding='SAME')
_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') #其中ksize的前三个参数分别是batch_size，h, w ,
_pool_dr1 = tf.nn.dropout(_pool1, _keepratio) #随机杀死一些层,keepratio表示保留的比率
# CONV LAYER 2
_conv2 = tf.nn.conv2d(_pool_dr1, _w['wc2'], strides=[1, 1, 1, 1], padding='SAME')
_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 把池化层输出的7*7*128变成list
_dense1 = tf.reshape(_pool_dr2, [-1, _w['wd1'].get_shape().as_list()[0]])
# FULLY CONNECTED LAYER 1
_fc1 = tf.nn.relu(tf.add(tf.matmul(_dense1, _w['wd1']), _b['bd1']))
_fc_dr1 = tf.nn.dropout(_fc1, _keepratio)
# FULLY CONNECTED LAYER 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, 'dense1': _dense1,
'fc1': _fc1, 'fc_dr1': _fc_dr1, 'out': _out
}
return out
print ("CNN READY")
a = tf.Variable(tf.random_normal([3, 3, 1, 64], stddev=0.1))
print (a)
a = tf.print(a, [a], "a: ")
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
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))
# SAVER
save_step=1
saver=tf.train.Saver(max_to_keep=3)
print ("GRAPH READY")
#迭代的过程
do_train=1
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
training_epochs = 15 #迭代15次
batch_size = 16 #每一个batch有16个样本
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 每一个epoch都要保存
if epoch % save_step == 0:
saver.save(sess,"save/nets/cnn_mnist_basic.ckpt-"+str(epoch))
print ("OPTIMIZATION FINISHED")
#下面对模型进行读取以及测试
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:testing,y:testlabel,keepratio:1})
prin("TEST ACCURACY: %.3f" %(test_acc))
案例六：模型的保存
#保存模型
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") #保存在save文件夹里面
print ("Model saved in file: ", saver_path)
#读取模型
import tensorflow as tf
v1 = tf.Variable(tf.random_normal([1,2]), name="v1") #设置v1为一行两列
v2 = tf.Variable(tf.random_normal([2,3]), name="v2") #设置v2为两行三列
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")