less IS more
664 lines
20 KiB
Plaintext
664 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 读取图像数据"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"import re\n",
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"import numpy as np\n",
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"import os\n",
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"import cv2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def getimgnames(path=None):\n",
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" \"\"\"\n",
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" 获取指定文件夹中的JPG图片名称（含路径）\n",
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" :param path: 指定文件夹\n",
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" :return: path中的所有JPG图片名称（含路径，例如：./path/image1.jpg）\n",
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" \"\"\"\n",
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" imgnames = []\n",
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" filenames = os.listdir(path) # 获取path中的所有文件名\n",
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" for i in filenames:\n",
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" if re.findall('^\\d+\\.jpg$', i) != []: # 在所有文件名中找出JPG图片名称\n",
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" imgnames.append(os.path.join(path, i)) # 将图片名称和路径合并、保存\n",
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" return imgnames"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"class GetImgData:\n",
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"\n",
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" def __init__(self, dir='data//gray_face_images'):\n",
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" self.dir = dir\n",
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"\n",
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" def onehot(self, indexlist=None):\n",
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" b = np.zeros([len(indexlist), max(indexlist) + 1])\n",
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" b[np.arange(len(indexlist)), indexlist] = 1\n",
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" return b.tolist()\n",
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"\n",
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" def getfileandlabel(self):\n",
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" dicdir = {name: os.path.join(self.dir, name) for name in os.listdir(self.dir) if os.path.isdir(os.path.join(self.dir, name))}\n",
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" ## 生成字典，key为文件夹名称，value为文件夹路径\n",
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" # ditcir = {}\n",
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" # for name in os.listdir(self.dir):\n",
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" # if os.path.isdir(os.path.join(self.dir, name)):\n",
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" # dicdir[name] = os.path.join(self.dir, name)\n",
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" namelist, pathlist = dicdir.keys(), dicdir.values()\n",
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" indexlist = list(range(len(namelist)))\n",
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" return list(zip(pathlist, self.onehot(indexlist))), dict(zip(indexlist, namelist))\n",
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"\n",
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" def readimg(self):\n",
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" dir_label, num_name = self.getfileandlabel()\n",
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" imgs = []\n",
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" labels = []\n",
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" for path, label in dir_label:\n",
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" for imgname in getimgnames(path):\n",
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" img = cv2.imread(imgname)[:, :, 0:1]\n",
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" imgs.append(img)\n",
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" labels.append(label)\n",
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" data = np.array(imgs, dtype=np.float32) / 255\n",
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" y = np.array(labels, dtype=np.float32)\n",
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" return data, y, num_name"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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]
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}
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],
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"source": [
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"if __name__ == '__main__':\n",
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" getimgdata = GetImgData()\n",
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" data, y, num_name = getimgdata.readimg()\n",
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" print(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow as tf\n",
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"from getimgdata import GetImgData\n",
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"from random import sample\n",
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"from sklearn.model_selection import train_test_split\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"## 兼容v1\n",
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"tf.compat.v1.disable_eager_execution()\n",
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"\n",
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"getimgdata = GetImgData(dir='data//gray_face_images')\n",
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"data, y, num_name = getimgdata.readimg()\n",
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"X_tr, X_te, y_tr, y_te = train_test_split(data, y, test_size=0.2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
||
"# 构建CNN神经网络"
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]
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},
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{
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||
"cell_type": "code",
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"execution_count": 12,
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||
"metadata": {},
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"outputs": [],
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"source": [
|
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"class CnnNet:\n",
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"\n",
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" def __init__(self, imgs=None, labels=None, keep_prob_5=0.5, modeldir=None):\n",
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" tf.compat.v1.reset_default_graph() # 重置计算图\n",
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" self.imgs = imgs\n",
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" self.labels = labels\n",
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" self.modeldir = modeldir\n",
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" self.size = imgs.shape[1]\n",
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" self.keep_prob_5 = keep_prob_5\n",
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" self.outnode = labels.shape[1]\n",
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" self.x = tf.compat.v1.placeholder(tf.float32, [None, self.size, self.size, 1], name='x_data')\n",
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" self.y_ = tf.compat.v1.placeholder(tf.float32, [None, self.outnode])\n",
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"\n",
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" def weightVariable(self, shape): # 权值数组W\n",
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" init = tf.compat.v1.random_normal(shape, stddev=0.01)\n",
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" return tf.compat.v1.Variable(init)\n",
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"\n",
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" def biasVariable(self, shape): # 偏置项数组b\n",
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" init = tf.compat.v1.random_normal(shape)\n",
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" return tf.Variable(init)\n",
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"\n",
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" def conv2d(self, x, W): # 卷积\n",
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" return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n",
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"\n",
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" def maxPool(self, x): # max池化\n",
|
||
" return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n",
|
||
"\n",
|
||
" def dropout(self, x, keep): # 随机让某些权重不更新，保持某个数\n",
|
||
" return tf.nn.dropout(x, keep)\n",
|
||
"\n",
|
||
" def cnnLayer(self):\n",
|
||
" \"\"\"\n",
|
||
" cnn神经网络结构\n",
|
||
" :return:\n",
|
||
" \"\"\"\n",
|
||
" # ===第一次卷积&池化====\n",
|
||
" W1 = self.weightVariable(shape=[3, 3, 1, 32])\n",
|
||
" b1 = self.biasVariable([32])\n",
|
||
" conv1 = tf.nn.relu(self.conv2d(self.x, W1) + b1)\n",
|
||
" pool1 = self.maxPool(conv1)\n",
|
||
" drop1 = self.dropout(pool1, self.keep_prob_5)\n",
|
||
"\n",
|
||
" # ===第二次卷积&池化====\n",
|
||
" W2 = self.weightVariable([3, 3, 32, 64])\n",
|
||
" b2 = self.biasVariable([64])\n",
|
||
" conv2 = tf.nn.relu(self.conv2d(drop1, W2) + b2)\n",
|
||
" pool2 = self.maxPool(conv2)\n",
|
||
" drop2 = self.dropout(pool2, self.keep_prob_5)\n",
|
||
"\n",
|
||
" # ===第三次卷积&池化====\n",
|
||
" W3 = self.weightVariable([3, 3, 64, 64])\n",
|
||
" b3 = self.biasVariable([64])\n",
|
||
" conv3 = tf.nn.relu(self.conv2d(drop2, W3) + b3)\n",
|
||
" pool3 = self.maxPool(conv3)\n",
|
||
" drop3 = self.dropout(pool3, self.keep_prob_5)\n",
|
||
"\n",
|
||
" # ===全连接层1====\n",
|
||
" Wf = self.weightVariable([8*8*64, 512])\n",
|
||
" bf = self.biasVariable([512])\n",
|
||
" drop3_flat = tf.reshape(drop3, [-1, 8*8*64])\n",
|
||
" dense = tf.nn.relu(tf.matmul(drop3_flat, Wf) + bf)\n",
|
||
" dropf = self.dropout(dense, 0.75)\n",
|
||
"\n",
|
||
" # ===输出层====\n",
|
||
" W_out = self.weightVariable([512, self.outnode])\n",
|
||
" b_out = self.biasVariable([self.outnode])\n",
|
||
" out = tf.add(tf.matmul(dropf, W_out), b_out, name='out')\n",
|
||
" return out\n",
|
||
" \n",
|
||
" def cnnTrain(self, maxiter=1000, accu=0.99, batch_size=100):\n",
|
||
" \"\"\"\n",
|
||
" 依据训练样本的模型输出与样本实际值进行模型训练\n",
|
||
" :param maxiter: 最大迭代次数\n",
|
||
" :param accu: 精度阈值，当训练精度大于accu时则停止训练\n",
|
||
" :param batch_size: 每轮训练的样本数\n",
|
||
" :return: 无返回，但是当模型精度满足要求后会将模型保存\n",
|
||
" \"\"\"\n",
|
||
" out = self.cnnLayer()\n",
|
||
" cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self.y_, logits=out))\n",
|
||
" train_step = tf.compat.v1.train.AdamOptimizer(0.01).minimize(cross_entropy)\n",
|
||
" acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(self.y_, 1)), tf.float32))\n",
|
||
"\n",
|
||
" saver = tf.compat.v1.train.Saver()\n",
|
||
" sess = tf.compat.v1.Session()\n",
|
||
" sess.run(tf.compat.v1.global_variables_initializer()) # 执行变量初始化操作\n",
|
||
" for n in range(maxiter):\n",
|
||
" ind = sample(range(len(self.imgs)), batch_size)\n",
|
||
" batch_x = self.imgs[ind]\n",
|
||
" batch_y = self.labels[ind]\n",
|
||
" sess.run(train_step, feed_dict={self.x: batch_x, self.y_: batch_y})\n",
|
||
" if n%100 == 0:\n",
|
||
" acc_tr = sess.run(acc, feed_dict={self.x: batch_x, self.y_: batch_y})\n",
|
||
" print('轮数：', n, ' the train accuracy is :', acc_tr)\n",
|
||
" if (acc_tr > accu) and (n >= 499):\n",
|
||
" saver.save(sess, self.modeldir)\n",
|
||
" break\n",
|
||
" elif n == (maxiter-1):\n",
|
||
" saver.save(sess, self.modeldir)\n",
|
||
" sess.close()\n",
|
||
"\n",
|
||
" def predict(self, test_x=None):\n",
|
||
" \"\"\"\n",
|
||
" 预测函数，导入已训练好的模型后再将新样本数据放入，进行模型预测\n",
|
||
" :param test_x: 测试样本的自变量\n",
|
||
" :return: 模型对测试样本的预测结果\n",
|
||
" 1: 预测结果（数字标签：0,1,2,3,4,5,...）\n",
|
||
" pre: 样本属于各类别的概率，形如：[[0.1, 0.1, 0.0, 0.0, 0.0, 0.8]]\n",
|
||
" \"\"\"\n",
|
||
" out = self.cnnLayer()\n",
|
||
" with tf.compat.v1.Session() as sess:\n",
|
||
" saver = tf.compat.v1.train.Saver()\n",
|
||
" saver.restore(sess, self.modeldir)\n",
|
||
" graph = tf.get_default_graph()\n",
|
||
" x = graph.get_tensor_by_name('x_data:0')\n",
|
||
" pre = sess.run(out, feed_dict={x: test_x})\n",
|
||
" acc_te = sum(np.argmax(pre, 1) == np.argmax(y_te, 1)) / len(pre)\n",
|
||
" return np.argmax(pre, 1), acc_te"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 模型训练"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"轮数： 0 the train accuracy is : 0.19\n",
|
||
"轮数： 100 the train accuracy is : 0.88\n",
|
||
"轮数： 200 the train accuracy is : 0.97\n",
|
||
"轮数： 300 the train accuracy is : 0.97\n",
|
||
"轮数： 400 the train accuracy is : 0.99\n",
|
||
"轮数： 500 the train accuracy is : 0.94\n",
|
||
"轮数： 600 the train accuracy is : 0.96\n",
|
||
"轮数： 700 the train accuracy is : 0.97\n",
|
||
"轮数： 800 the train accuracy is : 0.96\n",
|
||
"轮数： 900 the train accuracy is : 1.0\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"cnn_net = CnnNet(imgs=X_tr, labels=y_tr, modeldir='temp/train_model')\n",
|
||
"cross = cnn_net.cnnTrain()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 模型预测"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"WARNING:tensorflow:From D:\\TEMP\\ipykernel_17504\\2843680607.py:26: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
|
||
"\n",
|
||
"WARNING:tensorflow:From D:\\TEMP\\ipykernel_17504\\2843680607.py:29: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
|
||
"Instructions for updating:\n",
|
||
"Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
|
||
"INFO:tensorflow:Restoring parameters from temp/train_model\n",
|
||
"WARNING:tensorflow:From D:\\TEMP\\ipykernel_17504\\2843680607.py:113: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
|
||
"\n",
|
||
"the test accuracy is : 0.9845201238390093\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"cnn_net = CnnNet(imgs=X_tr, labels=y_tr, modeldir='temp/train_model')\n",
|
||
"pre, acc_te = cnn_net.predict(test_x=X_te)\n",
|
||
"print('the test accuracy is :', acc_te)"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3.7.9 ('venv': venv)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.7.9"
|
||
},
|
||
"vscode": {
|
||
"interpreter": {
|
||
"hash": "a82d1522e399fc49d60c7b85358aba15d27930532737449decaece807d60f8db"
|
||
}
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|