importosimportrandomimportcv2importnumpy as npfrom tensorflow.contrib.keras.api.keras.preprocessing.image importImageDataGenerator,img_to_arrayfrom tensorflow.contrib.keras.api.keras.models importSequentialfrom tensorflow.contrib.keras.api.keras.layers importDense, Dropout, Activation, Flattenfrom tensorflow.contrib.keras.api.keras.layers importConv2D, MaxPooling2Dfrom tensorflow.contrib.keras.api.keras.optimizers importSGD
IMAGE_SIZE= 182
#训练图片大小
epochs = 150#原来是50#遍历次数
batch_size = 32
#批量大小
nb_train_samples = 512*2
#训练样本总数
nb_validation_samples = 128*2
#测试样本总数
train_data_dir = 'D:\\code\\learn\\data_sex\\train_data\\'validation_data_dir= 'D:\\data_sex\\test_data\\'
#样本图片所在路径
FILE_PATH = 'Gender_new.h5'
#模型存放路径
classDataset(object):def __init__(self):
self.train=None
self.valid=Nonedef read(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE):
train_datagen=ImageDataGenerator(
rescale=1. / 255,
horizontal_flip=True)
test_datagen= ImageDataGenerator(rescale=1. / 255)
train_generator=train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_rows, img_cols),
batch_size=batch_size,
class_mode='binary')
validation_generator=test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_rows, img_cols),
batch_size=batch_size,
class_mode='binary')
self.train=train_generator
self.valid=validation_generatorclassModel(object):def __init__(self):
self.model=Sequential()
self.model.add(Conv2D(32, (3, 3), input_shape=(IMAGE_SIZE,IMAGE_SIZE,3)))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Conv2D(32, (3, 3)))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Conv2D(64, (3, 3)))
self.model.add(Activation('relu'))
self.model.add(MaxPooling2D(pool_size=(2, 2)))
self.model.add(Flatten())
self.model.add(Dense(64))
self.model.add(Activation('relu'))
self.model.add(Dropout(0.5))
self.model.add(Dense(1))
self.model.add(Activation('sigmoid'))def train(self, dataset, batch_size=batch_size, nb_epoch=epochs):
self.model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
self.model.fit_generator(dataset.train,
steps_per_epoch=nb_train_samples //batch_size,
epochs=epochs,
validation_data=dataset.valid,
validation_steps=nb_validation_samples//batch_size)def save(self, file_path=FILE_PATH):print('Model Saved.')
self.model.save_weights(file_path)def load(self, file_path=FILE_PATH):print('Model Loaded.')
self.model.load_weights(file_path)defpredict(self, image):#预测样本分类
img = image.resize((1, IMAGE_SIZE, IMAGE_SIZE, 3))
img= image.astype('float32')
img/= 255
#归一化
result =self.model.predict(img)print(result)#概率
result =self.model.predict_classes(img)print(result)#0/1
returnresult[0]defevaluate(self, dataset):#测试样本准确率
score = self.model.evaluate_generator(dataset.valid,steps=2)print("样本准确率%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100))if __name__ == '__main__':
dataset=Dataset()
dataset.read()
model=Model()
model.load()
model.train(dataset)
model.evaluate(dataset)
model.save()
