目录
Step 1/4 数据制作 记录
Step 2/4 模型构建（该教程有计算过，就直接贴）
Step 3/4 训练（将数据、模型和损失函数优化器融合）
Step 1/4 数据制作 记录
（每步都会有整体代码附在节后面）
PetImages/
train/
Dog/
1.jpg
2.jpg
...
Cat/
1.jpg
2.jpg
...
from tqdm import tqdm # display Tqdm 是一个快速，可扩展的Python进度条
import cv2 # working with, mainly resizing, images
import numpy as np # dealing with arrays
import os # dealing with directories
TRAIN_DATA_DIR = r'./PetImages/train'
IMG_SIZE = 224
# one-hot 编码
def label_img(img):
word_label = img.split('.')[-3]
if word_label == 'cat':
return [1,0]
elif word_label == 'dog':
return [0,1]
def create_train_dataset(path=TRAIN_DATA_DIR):
training_data = []
for img in tqdm(os.listdir(TRAIN_DATA_DIR)): # os.listdir突然不灵了，不能返回子层文件检索
print(img)
label = label_img(img) # one-hot #label
path = os.path.join(TRAIN_DATA_DIR, img)
img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) #img
training_data.append([np.array(img), label])
shuffle(training_data)
np.save('train_data.npy', training_data) # training_data 包含数据+标签，需要改动就自行在one-hot改
return training_data
然而 os.listdir(path)出现问题，并没有返回子树的文件索引，重新弄了一个
TRAIN_DATA_DIR = r'./PetImages/train'
IMG_SIZE = 224
'''
for parent, dirnames_list, filenames_list in os.walk(TRAIN_DATA_DIR): # 3个信息
print(parent) # 父节点路径+子节点路径
for dirname_list in dirnames_list:
print(dirname_list) # 子文件名
for filename_list in filenames_list:
print(filename_list) # 子文件下的文件
'''
for img in tqdm(os.listdir(TRAIN_DATA_DIR)): # os.listdir突然不灵了，不能返回子层文件检索
path = os.path.join(TRAIN_DATA_DIR, img) # 多加此行
for path_list in os.listdir(path):
label = label_data(img)
path_list = os.path.join(path,path_list)
src = cv2.imread(path_list, cv2.IMREAD_GRAYSCALE)
cv2.imshow("src",src)
cv2.waitKey(0)
pass
pass
Step 2/4 模型构建（该教程有计算过，就直接贴）
from keras.layers import *
from keras.models import *
from keras.optimizers import *
height = width = 224
def build_vgg(input_shape=(height, width, 3), num_classes=1000):
model = Sequential()
# Block 1, 2层
model.add(Conv2D(64, 3, 3, activation='relu', border_mode='same', input_shape=input_shape))
model.add(Conv2D(64, 3, 3, activation='relu',border_mode='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Block 2, 2层
model.add(Conv2D(128, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(128, 3, 3, activation='relu',border_mode='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Block 3, 3层
model.add(Conv2D(256, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(256, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(256, 3, 3, activation='relu',border_mode='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Block 4, 3层
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Block 5, 3层
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Classification block, 全连接3层
# 扁平层-化为一行，行接行
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
return model
if __name__ == "__main__":
# reference：keras中文手册: https://keras.io/zh/models/model/
# 模型结构
model = build_vgg()
# 配置器
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.0002), metrics=['accuracy'])
# 模型打印
print(model.summary())
pass
Step 3/4 训练（将数据、模型和损失函数优化器融合）
from __future__ import absolute_import, division, print_function
from tqdm import tqdm # display Tqdm 是一个快速，可扩展的Python进度条
import cv2 # working with, mainly resizing, images
import numpy as np # dealing with arrays
import os # dealing with directories
from random import shuffle
TRAIN_DATA_DIR = r'./PetImages/train'
img_width = img_height = 224
# one-hot 编码
def label_data(label): # word_label = img.split('.')[-3]
if label == 'cat':
return [1,0]
elif label == 'dog':
return [0,1]
def create_train_dataset(path=TRAIN_DATA_DIR):
training_data = []
for img in tqdm(os.listdir(TRAIN_DATA_DIR)): # os.listdir突然不灵了，不能返回子层文件检索,只返回['cat','dog']
path = os.path.join(TRAIN_DATA_DIR, img) # 多加此行 返回 */cat/ 和 */dog/
for path_list in os.listdir(path):
label = label_data(img) #
path_list = os.path.join(path, path_list)
src = cv2.imread(path_list) #cv2.IMREAD_GRAYSCALE
if src is None:
continue
src = cv2.resize(src, (img_width, img_height)) #img
training_data.append([np.array(src), label])
pass
pass
shuffle(training_data)
np.save('train_data.npy', training_data)
return training_data
from keras.layers import *
from keras.models import *
from keras.optimizers import *
def build_vgg(input_shape=(img_width, img_height, 3), num_classes=2):
model = Sequential()
# Block 1, 2层
model.add(Conv2D(64, 3, 3, activation='relu', border_mode='same', input_shape=input_shape))
model.add(Conv2D(64, 3, 3, activation='relu',border_mode='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Block 2, 2层
model.add(Conv2D(128, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(128, 3, 3, activation='relu',border_mode='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Block 3, 3层
model.add(Conv2D(256, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(256, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(256, 3, 3, activation='relu',border_mode='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Block 4, 3层
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Block 5, 3层
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(Conv2D(512, 3, 3, activation='relu',border_mode='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
# Classification block, 全连接3层
# 扁平层-化为一行，行接行
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
return model
from sklearn.model_selection import train_test_split
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping, ReduceLROnPlateau
if __name__ == "__main__":
# reference：keras中文手册: https://keras.io/zh/models/model/
# --------dataset--------
Initdata = False # 如果存在数据npy不需要重新生成
if Initdata == True:
training_data = create_train_dataset()
else:
training_data = np.load('train_data.npy', allow_pickle = True) # 权限allow_pickle = True
# spilt data
train, val = train_test_split(training_data, test_size = 0.25)
X_train = np.array([i[0] for i in train]).reshape(-1,img_width,img_height,3)
Y_train = np.array([i[1] for i in train])
X_val = np.array([i[0] for i in val]).reshape(-1,img_width,img_height,3)
Y_val = np.array([i[1] for i in val])
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=40, # randomly rotate images in the range (degrees, 0 to 180)
shear_range=0.3, #错切变换，效果就是让所有点的x坐标(或者y坐标)保持不变，而对应的y坐标(或者x坐标)则按比例发生平移
zoom_range = 0.2, # Randomly zoom image
width_shift_range=0.2, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.2, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=True # randomly flip images
)
# --------model--------
#
epochs = 1
batch_size = 64
#optimizer = Adam(0.0002)
optimizer = RMSprop(lr=1e-4)
learning_rate_reduction = ReduceLROnPlateau(
monitor='val_loss',
patience=4,
verbose=1,
factor=0.5,
min_lr=5e-8
)
early_stopping = EarlyStopping(
monitor='val_loss',
patience=6,
min_delta=0.0002,
verbose=1,
mode='auto'
)
filepath="./weights/weights.best.hdf5"
if not os.path.exists('./weights'):
os.mkdir('./weights')
checkpoint = ModelCheckpoint(
filepath,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='auto'
)
# 模型结构
model = build_vgg()
# 配置器
model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])
print(model.summary())
print(X_train.shape[0])
model_his = model.fit_generator(
datagen.flow(X_train,Y_train, batch_size=batch_size),
epochs = epochs,
validation_data = (X_val,Y_val),
shuffle=True,
verbose = 1,
steps_per_epoch=X_train.shape[0] // batch_size,
callbacks=[learning_rate_reduction, early_stopping, checkpoint]
)
model.save(filepath)
# draw training ——》》》model_his
pass
'''
# 问题解决 1 ：
AttributeError: module ‘tensorboard' has no attribute 'lazy'（谷歌出来的结果是Make sure that you don't have tb-nightly installed in your env.）
我采用pip uninstall tb-nightly指令发现并没有安装
'''
Step 4/4 测试
网上一大把教程，就不弄了。
