import random
import numpy as np
from handle_image import get_file
from sklearn.model_selection import train_test_split
from keras.utils import np_utils
from keras.models import Sequential,load_model
from keras.layers import Dense,Activation,Convolution2D,MaxPooling2D,Flatten,Dropout
from keras.optimizers import Adam
#建立数据
class DataSet(object):
def __init__(self):
self.nb_classes = None
self.X_train = None
self.X_test = None
self.Y_train = None
self.Y_test = None
self.img_size = 128
def extract_data(self,train_path):
imgs, labels, counter = get_file(train_path)
print(labels)
# 避免过拟合，采用交叉验证，验证集占训练集30%，固定随机种子（random_state)
X_train, X_test, y_train, y_test = train_test_split(imgs, labels, test_size=0.3,
random_state=random.randint(0, 100))
#数据预处理 keras backend 用的TensorFlow 黑白图片 channel 1
X_train = X_train.reshape(X_train.shape[0], 1, self.img_size, self.img_size) / 255.
X_test = X_test.reshape(X_test.shape[0], 1, self.img_size, self.img_size) / 255.
#label 转为 one-hot 数据
Y_train = np_utils.to_categorical(y_train, num_classes=counter)
Y_test = np_utils.to_categorical(y_test, num_classes=counter)
self.X_train = X_train
self.X_test = X_test
self.Y_train = Y_train
self.Y_test = Y_test
self.nb_classes = counter
#建立model 使用CNN（卷积神经网络）
class Model(object):
FILE_PATH = "store/model.h5"
IMAGE_SIZE = 128
def __init__(self):
self.model = None
def build_model(self,dataset):
self.model = Sequential()
#进行一层卷积 输出 shape (32,128,128)
self.model.add(Convolution2D(filters=32,kernel_size=5,strides=1, padding='same',data_format='channels_first', input_shape=dataset.X_train.shape[1:]))
#使用relu激励函数
self.model.add(Activation('relu'))
#池化，输出为shape (32,64,64)
self.model.add(MaxPooling2D(pool_size=2,strides=2,padding='same',data_format='channels_first'))
#dropout 防止过拟合
self.model.add(Dropout(0.25))
#进行一层卷积 输出为shape (64,32,32)
self.model.add(Convolution2D(64, 5, strides=1, padding='same', data_format='channels_first'))
# 使用relu激励函数
self.model.add(Activation('relu'))
# 池化，输出为原来的一半 shape (64,32,32)
self.model.add(MaxPooling2D(2, 2, 'same', data_format='channels_first'))
# dropout 防止过拟合
self.model.add(Dropout(0.25))
#全连接层
self.model.add(Flatten())
self.model.add(Dense(512))
self.model.add(Activation('relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(dataset.nb_classes))
self.model.add(Activation('softmax'))
self.model.summary()
def train(self,dataset):
adam = Adam(lr=1e-4)
self.model.compile(optimizer=adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
# epochs 循环次数 batch_size 批处理大小
self.model.fit(dataset.X_train, dataset.Y_train, epochs=25, batch_size=32, )
def save(self, file_path=FILE_PATH):
print('Model 保存.')
self.model.save(file_path)
def load(self, file_path=FILE_PATH):
print('Model 读取.')
self.model = load_model(file_path)
#预测
def predict(self,img):
img = img.reshape((1, 1, self.IMAGE_SIZE, self.IMAGE_SIZE))
img = img.astype('float32')
img = img/255.0
result = self.model.predict_proba(img) #预测图像结果
max_index = np.argmax(result) #取平局值最大
print("begin")
print(result)
print(max_index)
print(result[0][max_index])
print("end")
return max_index,result[0][max_index] #第一个参数为概率最高的label的index,第二个参数为对应概率
def evaluate(self, dataset):
loss,score = self.model.evaluate(dataset.X_test, dataset.Y_test, verbose=0)
# print("%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100))
print('\ntest loss: ', loss)
print('\ntest accuracy: ', score)
if __name__ == '__main__':
dataset = DataSet()
dataset.extract_data('gender_image')
model = Model()
model.build_model(dataset)
model.train(dataset)
model.save()
model = Model()
model.load()
model.evaluate(dataset)
