简单CNN 测试例

1.训练数据：

import tensorflow as tf
import cv2
import os
import numpy as np
import time
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, classification_report

drop_prob = 0.4
input_imgs = tf.placeholder(dtype=tf.float32,shape=[None,128,64,3],name='input_imgs')
input_label = tf.placeholder(dtype=tf.float32,shape=[None,2],name='input_label')

# 初始化权重（卷积核）
def weight_init(shape):
    weight = tf.truncated_normal(shape, stddev=0.1, dtype=tf.float32)
    return tf.Variable(weight)


# 初始化偏置项
def bias_init(shape):
    bias = tf.random_normal(shape, dtype=tf.float32)
    return tf.Variable(bias)

# 全连接层
def fch_init(layer1, layer2, const=1):
    min = -const * (6.0 / (layer1 + layer2))
    max = -min
    weight = tf.random_uniform([layer1, layer2], minval=min, maxval=max, dtype=tf.float32)
    return tf.Variable(weight)


# 卷积层
def conv2d(images, weight):
    return tf.nn.conv2d(images, weight, strides=[1, 1, 1, 1], padding='SAME')


# 最大池化层
def max_pool2x2(images, tname):
    return tf.nn.max_pool(images, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=tname)

# 卷积核3*3*3 16个     第一层卷积
w1 = weight_init([3, 3, 3, 16])
b1 = bias_init([16])
# 结果 NHWC  N H W C
conv_1 = conv2d(input_imgs, w1) + b1
relu_1 = tf.nn.relu(conv_1, name='relu_1')
max_pool_1 = max_pool2x2(relu_1, 'max_pool_1')

# 卷积核3*3*16  32个  第二层卷积
w2 = weight_init([3, 3, 16, 32])
b2 = bias_init([32])
conv_2 = conv2d(max_pool_1, w2) + b2
# 激活层
relu_2 = tf.nn.relu(conv_2, name='relu_2')
# 亚采样层
max_pool_2 = max_pool2x2(relu_2, 'max_pool_2')

w3 = weight_init([3, 3, 32, 64])
b3 = bias_init([64])
conv_3 = conv2d(max_pool_2, w3) + b3
relu_3 = tf.nn.relu(conv_3, name='relu_3')
max_pool_3 = max_pool2x2(relu_3, 'max_pool_3')

print(max_pool_3.shape, '-------扁平-------',max_pool_3.shape[1]*max_pool_3.shape[2]*max_pool_3.shape[3])
f_input = tf.reshape(max_pool_3, [-1, max_pool_3.shape[1]*max_pool_3.shape[2]*max_pool_3.shape[3]])
print('-=--=', f_input.shape)

# 全连接第一层 31*31*32,512
f_w1 = fch_init(8192, 512)
f_b1 = bias_init([512])
f_r1 = tf.matmul(f_input, f_w1) + f_b1
f_relu_r1 = tf.nn.relu(f_r1)
# 抛弃一部分神经元，防止过拟合
f_dropout_r1 = tf.nn.dropout(f_relu_r1, drop_prob)
print('f_dropout_r1.shape-----------', f_dropout_r1.shape)

#第二层
f_w2 = fch_init(512, 128)
f_b2 = bias_init([128])
f_r2 = tf.matmul(f_dropout_r1, f_w2) + f_b2
f_relu_r2 = tf.nn.relu(f_r2)
f_dropout_r2 = tf.nn.dropout(f_relu_r2, drop_prob)

# 全连接第三层 512,2
f_w3 = fch_init(128, 2)
f_b3 = bias_init([2])
f_r3 = tf.matmul(f_dropout_r2, f_w3) + f_b3
# print(f_r3.shape, '-=-===============')
f_softmax = tf.nn.softmax(f_r3, name='f_softmax')

# 定义交叉熵
cross_entry = tf.reduce_mean(tf.reduce_sum(-input_label * tf.log(f_softmax)))
optimizer = tf.train.AdamOptimizer(0.0001).minimize(cross_entry)

# 计算准确率
arg1 = tf.argmax(input_label, 1)
arg2 = tf.argmax(f_softmax, 1)
cos = tf.equal(arg1, arg2)
acc = tf.reduce_mean(tf.cast(cos, dtype=tf.float32))

sess = tf.Session()
sess.run(tf.global_variables_initializer())

train_img = []
train_labels = []
test_img = []
test_labels = []

images = []
labels = []
# for root, dirs, files in os.walk('../img/pos/'):
#     images.append(os.path.join('../img.pos',))
for fileName in os.listdir('../img/pos'):
    images.append([os.path.join('../img/pos', fileName)])
    labels.append([1,0])

for fileName in os.listdir('../img/neg'):
    images.append([os.path.join('../img/neg', fileName)])
    labels.append([0,1])


images = np.array(images)
labels = np.array(labels)
permutation = np.random.permutation(labels.shape[0])
images = images[permutation,:]
labels = labels[permutation,:]
#获取训练数据或者测试数据
def get_train_data(batch,isTrain=True):
    global test_labels,test_img
    if isTrain:
        train_num = int(labels.shape[0]*0.8)
        train_img = images[:train_num,:]
        train_labels = labels[:train_num,:]
        test_img = images[train_num:,:]
        test_labels = labels[train_num:,:]
        # print(train_img[batch:batch+20], train_labels[batch:batch+20])
        return train_img[batch*20:(batch+1)*20], train_labels[batch*20:(batch+1)*20]
    else:
        return test_img[batch*20:(batch+1)*20], test_labels[batch*20:(batch+1)*20]

# get_train_data(20)

def read_img(train_img):
    # print(train_img)
    imgs = []
    for i in range(20):
        img = cv2.imread(train_img[i][0])
        imgs.append(img)
        # cv2.imshow('12',img)
        # cv2.waitKey(0)
    imgs = np.array(imgs)
    return imgs
# print(imgs)


Cost = []
Accuracy=[]

start_time = time.time()
for i in range(100):
    train_img, train_labels = get_train_data(i)
    imgs = read_img(train_img)

    result,acc1,cross_entry_r,cos1,f_softmax1,relu_1_r= sess.run([optimizer,acc,cross_entry,cos,f_softmax,relu_1],feed_dict={input_imgs:imgs,input_label:train_labels})
    print("rpoch:  {}, accurate: {} , cross_loss:{}".format(i,acc1,cross_entry_r))
    Cost.append(cross_entry_r)
    Accuracy.append(acc1)


print('total  time：%d'%(time.time()-start_time))
# 代价函数曲线
fig1,ax1 = plt.subplots(figsize=(10,7))
plt.plot(Cost)
print('---------cost-----------',Cost)
ax1.set_xlabel('Epochs')
ax1.set_ylabel('Cost')
plt.title('Cross Loss')
plt.grid()
plt.show()

# 准确率曲线
fig7,ax7 = plt.subplots(figsize=(10,7))
plt.plot(Accuracy)
ax7.set_xlabel('Epochs')
ax7.set_ylabel('Accuracy Rate')
plt.title('Train Accuracy Rate')
plt.grid()
plt.show()


#测试
test_img,test_labels = get_train_data(1,False)
test_img = read_img(test_img)
arg2_r = sess.run(arg2,feed_dict={input_imgs:test_img,input_label:test_labels})
arg1_r = sess.run(arg1,feed_dict={input_imgs:test_img,input_label:test_labels})

print (classification_report(arg1_r, arg2_r))

#保存模型  global_step:训练模型的命名
saver = tf.train.Saver()
saver.save(sess, './model/my-gender-v1.0',global_step=123)

2. 从保存的模型中读取数据

import tensorflow as tf
import numpy as np
import cv2
import  matplotlib.pyplot as plt
import  os

#取一张图片

# img/pos/758.jpg
img = cv2.imread('../img/pos/760.jpg')
# labels = train_data.labels[0:1]
fig2,ax2 = plt.subplots(figsize=(2,2))
ax2.imshow(img)
plt.show()
img = np.reshape(img,[1,128,64,3])

sess = tf.Session()
graph_path=os.path.abspath('./model/my-gender-v1.0-123.meta')
model=os.path.abspath('./model/')

server = tf.train.import_meta_graph(graph_path)
server.restore(sess,tf.train.latest_checkpoint(model))

graph = tf.get_default_graph()

#填充feed_dict
x = graph.get_tensor_by_name('input_imgs:0')
y = graph.get_tensor_by_name('input_label:0')
feed_dict={x:img,y:[[1,0]]}


#第一层卷积+池化
relu_1 = graph.get_tensor_by_name('relu_1:0')
max_pool_1 = graph.get_tensor_by_name('max_pool_1:0')

#第二层卷积+池化
relu_2 = graph.get_tensor_by_name('relu_2:0')
max_pool_2 = graph.get_tensor_by_name('max_pool_2:0')

#第三层卷积+池化
relu_3 = graph.get_tensor_by_name('relu_3:0')
max_pool_3 = graph.get_tensor_by_name('max_pool_3:0')

#全连接最后一层输出
f_softmax = graph.get_tensor_by_name('f_softmax:0')


#relu_1_r,max_pool_1_,relu_2,max_pool_2,relu_3,max_pool_3,f_softmax=sess.run([relu_1,max_pool_1,relu_2,max_pool_2,relu_3,max_pool_3,f_softmax],feed_dict)



#----------------------------------各个层特征可视化-------------------------------




#conv1 特征
r1_relu = sess.run(relu_1,feed_dict)
print('r1_relu',r1_relu.shape)
# 将矩阵转置
r1_tranpose = sess.run(tf.transpose(r1_relu,[3,0,1,2]))
print('r1_tranpose',r1_tranpose.shape)
fig,ax = plt.subplots(nrows=1,ncols=16,figsize=(16,1))
for i in range(16):
    ax[i].imshow(r1_tranpose[i][0])
plt.title('Conv1 16*112*92')
plt.show()

#pool1特征
max_pool_1 = sess.run(max_pool_1,feed_dict)
r1_tranpose = sess.run(tf.transpose(max_pool_1,[3,0,1,2]))
fig,ax = plt.subplots(nrows=1,ncols=16,figsize=(16,1))
for i in range(16):
    ax[i].imshow(r1_tranpose[i][0])
plt.title('Pool1 16*56*46')
plt.show()


#conv2 特征
r2_relu = sess.run(relu_2,feed_dict)
r2_tranpose = sess.run(tf.transpose(r2_relu,[3,0,1,2]))
fig,ax = plt.subplots(nrows=1,ncols=32,figsize=(32,1))
for i in range(32):
    ax[i].imshow(r2_tranpose[i][0])
plt.title('Conv2 32*56*46')
plt.show()

#pool2 特征
max_pool_2 = sess.run(max_pool_2,feed_dict)
tranpose = sess.run(tf.transpose(max_pool_2,[3,0,1,2]))
fig,ax = plt.subplots(nrows=1,ncols=32,figsize=(32,1))
for i in range(32):
    ax[i].imshow(tranpose[i][0])
plt.title('Pool2 32*28*23')
plt.show()


#conv3 特征
r3_relu = sess.run(relu_3,feed_dict)
tranpose = sess.run(tf.transpose(r3_relu,[3,0,1,2]))
fig,ax = plt.subplots(nrows=1,ncols=64,figsize=(32,1))
for i in range(64):
    ax[i].imshow(tranpose[i][0])
plt.title('Conv3 64*28*23')
plt.show()

#pool3 特征
max_pool_3 = sess.run(max_pool_3,feed_dict)
tranpose = sess.run(tf.transpose(max_pool_3,[3,0,1,2]))
fig,ax = plt.subplots(nrows=1,ncols=64,figsize=(32,1))
for i in range(64):
    ax[i].imshow(tranpose[i][0])
plt.title('Pool3 64*14*12')
plt.show()

print(sess.run(f_softmax,feed_dict))

　　

注意：

　　卷积神经网络：conv2d ->pool->relu(softmax二分类）  多层卷积神经网络的使用，注意使用卷积核的个数，步长及大小。