第一步：准备好需要的库
- tensorflow-gpu 1.8.0
- opencv-python 3.3.1
- numpy
- skimage
- os
- pillow
第二步：准备数据集：
链接：https://pan.baidu.com/s/1Kbz_UaRhAfhlweFY28R8Sw 密码：iym3
本次使用了花朵分类的数据集，总共有5类
每类里面有不同形态的同一类花朵
在下载完数据集之后，我们对数据集进行预处理：
from skimage import io, transform
import os
import numpy as np
# 将所有的图片resize成100*100
w = 100
h = 100
c = 3
# 读取图片
def read_img(path):
imgs = []
labels = []
classs = os.listdir(path)
for idx, folder in enumerate(classs):
cate = os.path.join(path, folder)
for im in os.listdir(cate):
img_path =os.path.join(cate, im)
# print(‘reading the images:%s‘ % (img_path))
img = io.imread(img_path)
img = transform.resize(img, (w, h))
# with open(‘tests.txt‘, ‘a‘) as f:
# f.write(img_path+‘_‘+str(idx)+‘\n‘)
imgs.append(img)
labels.append(idx)
return np.asarray(imgs, np.float32), np.asarray(labels, np.int32)
def suffer(data, label):
# 打乱顺序
num_example = data.shape[0]
arr = np.arange(num_example)
np.random.shuffle(arr)
data = data[arr]
label = label[arr]
# 将所有数据分为训练集和验证集
ratio = 0.8
s = np.int(num_example * ratio)
x_train = data[:s]
y_train = label[:s]
x_val = data[s:]
y_val = label[s:]
return x_train,y_train,x_val,y_val
def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
if shuffle:
excerpt = indices[start_idx:start_idx + batch_size]
else:
excerpt = slice(start_idx, start_idx + batch_size)
yield inputs[excerpt], targets[excerpt]
我们将图片统一设为100×100的大小，然后对每一个文件夹标号，作为标签。为了检验我们是否将标签与图片对齐，我预留了一个写文件路径+标签的一个文件。
写出来是这样的
在做处理好标签和图片之后我们将其设定为 np.asarray(imgs, np.float32)的格式。
然后将这些图片随机打乱顺序。以8：2的比例划分训练集和验证集。
接着我们来生成minibatch：将数据切分成batch_size的大小送入网络。
在预处理完数据之后，我们开始进行网络的构建
import tensorflow as tf
def batch_norm(x, momentum=0.9, epsilon=1e-5, train=True, name=‘bn‘):
return tf.layers.batch_normalization(x,
momentum=momentum,
epsilon=epsilon,
scale=True,
training=train,
name=name)
def simple_cnn(x):
# 第一个卷积层（100——>50)
conv1 = tf.layers.conv2d(
inputs=x,
filters=32,
kernel_size=[3, 3],
padding="same",
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
conv1 = batch_norm(conv1, name=‘pw_bn1‘)
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
# 第二个卷积层(50->25)
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[3, 3],
padding="same",
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
conv2 = batch_norm(conv2, name=‘pw_bn2‘)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
# 第三个卷积层(25->12)
conv3 = tf.layers.conv2d(
inputs=pool2,
filters=128,
kernel_size=[3, 3],
padding="same",
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
conv3 = batch_norm(conv3, name=‘pw_bn3‘)
pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2, 2], strides=2)
# 第四个卷积层(12->6)
conv4 = tf.layers.conv2d(
inputs=pool3,
filters=128,
kernel_size=[3, 3],
padding="same",
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01))
conv4 = batch_norm(conv4, name=‘pw_bn4‘)
pool4 = tf.layers.max_pooling2d(inputs=conv4, pool_size=[2, 2], strides=2)
re1 = tf.reshape(pool4, [-1, 6 * 6 * 128])
# 全连接层
dense1 = tf.layers.dense(inputs=re1,
units=1024,
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
dense2 = tf.layers.dense(inputs=dense1,
units=512,
activation=tf.nn.relu,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
logits = tf.layers.dense(inputs=dense2,
units=5,
activation=None,
kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),
kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
pred = tf.nn.softmax(logits, name=‘prob‘)
return logits, pred
我们的网络由4个卷积层，两个全连接层，一个softmax层组成。在每一层的卷积后面加入了batch_normalization，relu和池化。
batch_normalization层很好用，加上它之后，有效的预防了梯度消逝和爆炸，还加速了收敛。
在搭建好网络之后，我们开始编写训练模块
import tensorflow as tf
import cnn
import dataset
# 将所有的图片resize成100*100
w = 100
h = 100
c = 3
path = ‘flowers‘
x = tf.placeholder(tf.float32, shape=[None, w, h, c], name=‘x‘)
y_ = tf.placeholder(tf.int32, shape=[None, ], name=‘y_‘)
logits,pred = cnn.simple_cnn(x)
loss = tf.losses.sparse_softmax_cross_entropy(labels=y_, logits=logits)
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
correct_prediction = tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_)
acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
data, label = dataset.read_img(path)
x_train, y_train,x_val, y_val = dataset.suffer(data, label)
# 训练和测试数据，可将n_epoch设置更大一些
n_epoch = 11
batch_size = 16
def train():
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
for epoch in range(n_epoch):
train_loss, train_acc, n_batch = 0, 0, 0
for x_train_a, y_train_a in dataset.minibatches(x_train, y_train, batch_size, shuffle=True):
_, err, ac = sess.run([train_op, loss, acc], feed_dict={x: x_train_a, y_: y_train_a})
train_loss += err
train_acc += ac
n_batch += 1
print(‘Epoch %d - train loss: %f‘%(epoch, (train_loss / n_batch)))
print(‘Epoch %d - train acc: %f‘%(epoch,train_acc / n_batch))
# validation
val_loss, val_acc, n_batch = 0, 0, 0
for x_val_a, y_val_a in dataset.minibatches(x_val, y_val, batch_size, shuffle=False):
err, ac = sess.run([loss, acc], feed_dict={x: x_val_a, y_: y_val_a})
val_loss += err
val_acc += ac
n_batch += 1
print(‘Epoch %d - Validation loss: %f‘ %(epoch, val_loss / n_batch))
print(‘Epoch %d - Validation Accuracy: %f‘%( epoch,(val_acc / n_batch)))
if epoch % 5 == 0:
saver.save(sess, "./model/save_net.ckpt",epoch)
print(‘Trained Model Saved.‘)
train()
训练时我们首先要定义X，Y作为索引
x = tf.placeholder(tf.float32, shape=[None, w, h, c], name=‘x‘) y_ = tf.placeholder(tf.int32, shape=[None, ], name=‘y_‘) 然后对于刚才构建的网络进行损失的计算，精确度计算以及优化器的选择。接着我们将session初始化
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
然后将定义的X,Y索引与你的真实数据，标签对齐。
使用
_, err, ac = sess.run([train_op, loss, acc], feed_dict={x: x_train_a, y_: y_train_a})
开始运行就可以了。测试同理，不过测试的时候不需要优化器，所以只需要加入参数loss,acc就可以了。我们每隔5次保存一次模型。
在训练结束后，我们对使用之前训练好的模型进行预测：
import numpy as np
import tensorflow as tf
from PIL import Image, ImageDraw, ImageFont
from cnn import simple_cnn
# 将所有的图片resize成100*100
w = 100
h = 100
c = 3
classes = [‘daisy‘,‘dandelion‘,‘roses‘,‘sunflowers‘,‘tulips‘]
image_test = Image.open(‘44079668_34dfee3da1_n.jpg‘)
resized_image = image_test.resize((w, h), Image.BICUBIC)
image_data = np.array(resized_image, dtype=‘float32‘)
imgs_holder = tf.placeholder(tf.float32, shape=[1, w, h, c])
logits,pred = simple_cnn(imgs_holder)
saver = tf.train.Saver()
ckpt_dir = ‘./model/‘
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(ckpt_dir)
saver.restore(sess, ckpt.model_checkpoint_path)
classes_ = sess.run(pred,feed_dict={ imgs_holder: np.reshape(image_data , [1, w, h, c])})
num = np.argmax(classes_)
print(‘class is :‘,classes[int(num)],‘ Probability is :‘,classes_[0][int(num)])
在预测时，因为子还需要输入一张图片就可以了，所以我们只制作图片的索引
imgs_holder = tf.placeholder(tf.float32, shape=[1, w, h, c]) 然后读取刚才保存的参数，只需要输入目录，即可自动读取最后训练的模型。然后运行：
classes_ = sess.run(pred,feed_dict={ imgs_holder: np.reshape(image_data , [1, w, h, c])})
输出每个类的概率值。我们将这个概率最大的值的标号读取出来，对应之前文件夹的标号。
classes = [‘daisy‘,‘dandelion‘,‘roses‘,‘sunflowers‘,‘tulips‘]然后将这个标号对应的概率数标出来。
本次使用了tf.layer进行了简单CNN的构建，并且使用了tensorflow传统的sess.run的方法来运行图，没有使用之前提到的高级API。在这种方法上进行了简单的尝试，接下来会尝试使用slim框架构建网络。
原文地址：https://www.cnblogs.com/ansang/p/9164805.html
时间： 2024-10-13 14:10:17