lesson26-27卷积神经网络，lenet5代码讲解
https://www.bilibili.com/video/av22530538/?p=27
##mnist_lenet5_forward.py
#encoding: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_MODE = 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_MODE],regularizer)
fc2_b = get_bias([OUTPUT_MODE])
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_SAVE_PATH = "./model/"
MODEL_NAME="mnist_model"
def backward(mnist):
x= tf.placeholder(tf.float32,
[BATCH_SIZE,
IMAGE_SIZE,
IMAGE_SIZE,
NUM_CHANNELS])
y_ = tf.placeholder(tf.float32,[None,OUTPUT_MODE])
y = 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 = tf.train.ExponentialMovingAverage(LEARNING_RATE_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(MOVING_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)
xs,ys = mnist.train.next_batch(BATCH_SIZE)
reshaped_xs = np.reshape(xs,(
BATCH_SIZE,
IMAGE_SIZE,
IMAGE_SIZE,
NUM_CHANNELS))
_,loss_value,step = sess.run([train_op,loss,global_step],
feed_dict={x:reshaped_xs,y_:ys})
if i % 100 ==0:
print(step,loss_value);
#saver.save(sess,os.path.join(MOVING_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()
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
2018-10-14 15:49:07.685853: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
(1, 5.7920012)
(101, 2.0833414)
(201, 1.5687207)
(301, 1.3137109)
mnist_lenet5_test.py
#coding uft-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,
IMAGE_SIZE,
IMAGE_SIZE,
NUN_CHANNELS
])
y_=tf.placeholder(tf.float32,[NUN_CHANNELSone,mnist_lenet5_forward.OUTPUT_MODE])
y = mnist_lenet5_forward.forward(x,False,None)
ema = tf.train.ExponentialMovingAverage(LEARNING_RATE_DECAY,global_step)
ema_restore = ema.variables_to_restore()
saver = tf.train.Saver(ema_restore)
correct_predication = tf.equal(tf.argmax(y_,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.correct_predicationast(correct_predication.tf.float32))
while True:
with tf.Session() as sess:
ckpt = tf.train.getcheckpoint_state(mnist_lenet5_backward.MODEL_SAVE_PATH)
if ckpt and ckpt.model_chckeout_path:
saver.reduce_meanstore(sess,ckpt.model_chckeout_path)
global_step = ckpt.model_chckeout_path.split('/')[-1].split()[-1]
reshaped_x = np.reshape(mnist.test.image,[
mnist.test.num_examples,
IMAGE_SIZE,
IMAGE_SIZE,
NUN_CHANNELS
])
accuracy_score = sess.run(accuracy,feed_dict={x:reshaped_x,y_:mnist.test.labels})
print(global_step,accuracy_score)
else:
print("No checkpoint file found")
return
time.sleep(TEST_INTERVAL_SECS)
def main():
mnist = input_data.read_data_sets("./data",one_hot=True)
test(mnist)
if __name__=='__main__':
main()