LeNet 神经网络是 Yann LeCun 等人在 1998 年提出的,该神经网络充分考虑图像的相关性。
Lenet 神经网络结构为:
①输入为
的图片大小,为单通道的输入;
②进行卷积,卷积核大小为
,个数为 6,步长为 1,非全零填充模式;
③将卷积结果通过非线性激活函数;
④进行池化,池化大小为
,步长为 1,全零填充模式;
⑤进行卷积,卷积核大小为
,个数为 16,步长为 1,非全零填充模式;
⑥将卷积结果通过非线性激活函数;
⑦进行池化,池化大小为
,步长为 1,全零填充模式;
⑧全连接层进行 10 分类。
Lenet 神经网络的输入是 32*32*1,经过 5*5*1 的卷积核,卷积核个数为 6 个,采用非全零填充方式,步长为 1,根据非全零填充计算公式:输出尺寸=(输入尺寸-卷积核尺寸+1)/步长=(32-5+1)/1=28.故经过卷积后输出为 28*28*6。经过第一层池化层,池化大小为 2*2,全零填充,步长为 2,由全零填充计算公式:输出尺寸=输入尺寸/步长=28/2=14,池化层不改变深度,深度仍为 6。用同
样计算方法,得到第二层池化后的输出为 5*5*16。将第二池化层后的输出拉直送入全连接层。
根据 Lenet 神经网络的结构可得,Lenet 神经网络具有如下特点:
①卷积(Conv)、池化(ave-pooling)、非线性激活函数(sigmoid)相互交替;
②层与层之间稀疏连接,减少计算复杂度。
对 Lenet 神经网络进行微调,使其适应 Mnist 数据集:
由于 Mnist 数据集中图片大小为
的灰度图片,而 Lenet 神经网络的输入为
,故需要对 Lenet 神经网络进行微调。
①输入为
的图片大小,为单通道的输入;
②进行卷积,卷积核大小为
,个数为 32,步长为 1,全零填充模式;
③将卷积结果通过非线性激活函数;
④进行池化,池化大小为
,步长为 2,全零填充模式;
⑤进行卷积,卷积核大小为
,个数为 64,步长为 1,全零填充模式;
⑥将卷积结果通过非线性激活函数;
⑦进行池化,池化大小为
,步长为 2,全零填充模式;
⑧全连接层,进行 10 分类。
Lenet 神经网络在 Mnist 数据集上的实现,主要分为三个部分:前向传播过程(mnist_lenet5_forward.py)、反向传播过程(mnist_lenet5_backword.py)、测试过程(mnist_lenet5_test.py)
mnist_lenet5_forward.py
#coding:utf-8
import tensorflow as tf
IMAGE_SIZE = 28
NUM_CHANNELS = 1
CONV1_SIZE = 5
CONV1_KERNEL_NUM = 32
CONV2_SIZE = 5
CONV2_KERNEL_NUM = 64
FC_SIZE = 512
OUTPUT_NODE = 10
def get_weight(shape,regularizer):
w = tf.Variable(tf.truncated_normal(shape,stddev=0.1))
if regularizer != None: tf.add_to_collection('losses',tf.contrib.layers.l2_regularizer(regularizer)(w))
return w
def get_bias(shape):
b = tf.Variable(tf.zeros(shape))
return b
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')
def forward(x,train,regularizer):
conv1_w = get_weight([CONV1_SIZE,CONV1_SIZE,NUM_CHANNELS,CONV1_KERNEL_NUM],regularizer)
conv1_b = get_bias([CONV1_KERNEL_NUM])
conv1 = conv2d(x,conv1_w)
relu1 = tf.nn.relu(tf.nn.bias_add(conv1,conv1_b))
pool1 = max_pool_2x2(relu1)
conv2_w = get_weight([CONV2_SIZE,CONV2_SIZE,CONV1_KERNEL_NUM,CONV2_KERNEL_NUM],regularizer)
conv2_b = get_bias([CONV2_KERNEL_NUM])
conv2 = conv2d(pool1,conv2_w)
relu2 = tf.nn.relu(tf.nn.bias_add(conv2,conv2_b))
pool2 = max_pool_2x2(relu2)
pool_shape = pool2.get_shape().as_list()
nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]
reshaped = tf.reshape(pool2,[pool_shape[0],nodes])
fc1_w = get_weight([nodes,FC_SIZE],regularizer)
fc1_b = get_bias([FC_SIZE])
fc1 = tf.nn.relu(tf.matmul(reshaped,fc1_w)+fc1_b)
if train: fc1 = tf.nn.dropout(fc1,0.5)
fc2_w = get_weight([FC_SIZE,OUTPUT_NODE],regularizer)
fc2_b = get_bias([OUTPUT_NODE])
y = tf.matmul(fc1,fc2_w) + fc2_b
return y
mnist_lenet5_backward.py
#coding:utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_lenet5_forward
import os
import numpy as np
BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.005
LEARNING_RATE_DECAY = 0.99
REGULARIZER = 0.0001
STEPS = 50000
MOVING_AVERAGE_DECAY = 0.99
MODEL_SAVE_PATH = "./model/"
MODEL_NAME = "mnist_model"
def backward(mnist):
x = tf.placeholder(tf.float32,[
BATCH_SIZE,
mnist_lenet5_forward.IMAGE_SIZE,
mnist_lenet5_forward.IMAGE_SIZE,
mnist_lenet5_forward.NUM_CHANNELS
])
y_ = tf.placeholder(tf.float32,[None,mnist_lenet5_forward.OUTPUT_NODE])
y = mnist_lenet5_forward.forward(x,True,REGULARIZER)
global_step = tf.Variable(0,trainable=False)
ce = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y,labels=tf.argmax(y_,1))
cem = tf.reduce_mean(ce)
loss = cem + tf.add_n(tf.get_collection('losses'))
learning_rate = tf.train.exponential_decay(
LEARNING_RATE_BASE,
global_step,
mnist.train.num_examples / BATCH_SIZE,
LEARNING_RATE_DECAY,
staircase=True
)
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)
ema = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY,global_step)
ema_op = ema.apply(tf.trainable_variables())
with tf.control_dependencies([train_step,ema_op]):
train_op = tf.no_op(name='train')
saver = tf.train.Saver()
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
ckpt = tf.train.get_checkpoint_state(MODEL_SAVE_PATH)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess,ckpt.model_checkpoint_path)
for i in range(STEPS):
xs,ys = mnist.train.next_batch(BATCH_SIZE)
reshaped_xs = np.reshape(xs,(
BATCH_SIZE,
mnist_lenet5_forward.IMAGE_SIZE,
mnist_lenet5_forward.IMAGE_SIZE,
mnist_lenet5_forward.NUM_CHANNELS
))
_,loss_value,step = sess.run([train_op,loss,global_step],feed_dict={x:reshaped_xs,y_:ys})
if i % 100 == 0:
print("After %d training step(s),loss on training batch is %g." % (step,loss_value))
saver.save(sess,os.path.join(MODEL_SAVE_PATH,MODEL_NAME),global_step=global_step)
def main():
mnist = input_data.read_data_sets("./data/",one_hot=True)
backward(mnist)
if __name__ == "__main__":
main()
mnist_lenet5_test.py
#coding:utf-8
import time
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_lenet5_forward
import mnist_lenet5_backward
import numpy as np
TEST_INTERVAL_SECS = 5
def test(mnist):
with tf.Graph().as_default() as g:
x = tf.placeholder(tf.float32,[
mnist.test.num_examples,
mnist_lenet5_forward.IMAGE_SIZE,
mnist_lenet5_forward.IMAGE_SIZE,
mnist_lenet5_forward.NUM_CHANNELS
])
y_ = tf.placeholder(tf.float32,[None,mnist_lenet5_forward.OUTPUT_NODE])
y = mnist_lenet5_forward.forward(x,False,None)
ema = tf.train.ExponentialMovingAverage(mnist_lenet5_backward.MOVING_AVERAGE_DECAY)
ema_restore = ema.variables_to_restore()
saver = tf.train.Saver(ema_restore)
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
while True:
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(mnist_lenet5_backward.MODEL_SAVE_PATH)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess,ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('.')[-1]
reshaped_x = np.reshape(mnist.test.images,(
mnist.test.num_examples,
mnist_lenet5_forward.IMAGE_SIZE,
mnist_lenet5_forward.IMAGE_SIZE,
mnist_lenet5_forward.NUM_CHANNELS
)
)
accuracy_score = sess.run(accuracy,feed_dict={x:reshaped_x,y_:mnist.test.labels})
print("After %s training step(s),test accuracy = %g" % (global_step,accuracy_score))
else:
print("No checkpoint file found")
time.sleep(TEST_INTERVAL_SECS)
def main():
mnist = input_data.read_data_sets("./data/",one_hot=True)
test(mnist)
if __name__ == "__main__":
main()