对近5万张按“物种-病害-程度”分成61类的植物叶片照片进行分类
我使用的是Keras，以TensorFlow为后端，手动实现了DenseNet用于图片分类 由于Kaggle现在可以免费使用GPU，所以采用将数据上传至Kaggle的私人Dataset上，在其上创建Kernel进行模型训练 （上传需要翻墙，有梯子最好）
def dense_block(x, blocks, name):
for i in range(blocks):
x = conv_block(x, 32, name=name + '_block' + str(i + 1))
return x
def transition_block(x, reduction, name):
bn_axis = 3
x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
name=name + '_bn')(x)
x = layers.Activation('relu', name=name + '_relu')(x)
x = layers.Conv2D(int(backend.int_shape(x)[bn_axis] * reduction), 1,
use_bias=False,
name=name + '_conv')(x)
x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x)
return x
def conv_block(x, growth_rate, name):
bn_axis = 3
x1 = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name=name + '_0_bn')(x)
x1 = layers.Activation('relu', name=name + '_0_relu')(x1)
x1 = layers.Conv2D(4 * growth_rate, 1,
use_bias=False,
name=name + '_1_conv')(x1)
x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
name=name + '_1_bn')(x1)
x1 = layers.Activation('relu', name=name + '_1_relu')(x1)
x1 = layers.Conv2D(growth_rate, 3,
padding='same',
use_bias=False,
name=name + '_2_conv')(x1)
x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1])
return x
def DenseNet(blocks, input_shape=(150,150,3), classes=61):
img_input = Input(shape=input_shape)
bn_axis = 3
x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
x = layers.BatchNormalization(
axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x)
x = layers.Activation('relu', name='conv1/relu')(x)
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
x = layers.MaxPooling2D(3, strides=2, name='pool1')(x)
x = dense_block(x, blocks[0], name='conv2')
x = transition_block(x, 0.5, name='pool2')
x = dense_block(x, blocks[1], name='conv3')
x = transition_block(x, 0.5, name='pool3')
x = dense_block(x, blocks[2], name='conv4')
x = transition_block(x, 0.5, name='pool4')
x = dense_block(x, blocks[3], name='conv5')
x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
x = Dense(512)(x)
x = BatchNormalization()(x)
x = PReLU()(x)
x = Dropout(0.5)(x)
x = Dense(classes, activation='softmax', name='fc61')(x)
inputs = img_input
model = Model(inputs, x, name='densenet')
return model
调用DenseNet函数即可创建
model = DenseNet(blocks=[6, 12, 48, 32], input_shape=(150,150,3),classes=61)
model.summary()
1、训练集、验证集生产器 这里对图片进行图像预处理，增加图片归一化、适度旋转、随机缩放、上下翻转
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
rotation_range=20,
zoom_range=0.2,
horizontal_flip=True)
val_datagen = ImageDataGenerator(rescale=1. / 255)
2、读取数据 从目录中读取数据
img_width, img_height = 150, 150
train_data_dir = '../input/train/train'
validation_data_dir = '../input/val/val'
batch_size = 64
classes = 61
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical') #多分类
validation_generator = val_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical') #多分类
1、先对模型进行预编译
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0.0001),
metrics=['accuracy'])
2、训练模型 增加自动更新学习率和保存在验证集最后的模型参数
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=3, verbose=1,factor=0.5, min_lr=0.000001)
checkpoint = ModelCheckpoint(model_name, monitor='val_acc', save_best_only=True)
history = model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=30,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size,
callbacks=[checkpoint, learning_rate_reduction])
训练次数由于受Kaggle中Kernel的使用时间受限，只能训练6小时，所以只能暂时训练30，不过可以多次迭代训练。
由于文件夹存放顺序跟window上不一样，所以实际上文件夹在Kaggle上Dataset上的存放顺序如下
rr = [0,
1,10,11,12,13,14,15,16,17,18,19,
2,20,21,22,23,24,25,26,27,28,29,
3,30,31,32,33,34,35,36,37,38,39,
4,40,41,42,43,44,45,46,47,48,49,
5,50,51,52,53,54,55,56,57,58,59,
6,60,
7,
8,
9]
images = os.listdir('../input/ai-challenger-pdr2018/testa/testA')
result = []
for img1 in images:
image_path = '../input/ai-challenger-pdr2018/testa/testA/' + img1
img = image.load_img(image_path, target_size=(150, 150))
x = image.img_to_array(img)/255.0
x = np.expand_dims(x, axis=0)
preds = model.predict(x)
tmp = dict()
tmp['image_id'] = img1
tmp['disease_class']=rr[int(np.argmax(preds))]
result.append(tmp)
最后保存为json
import json
json2 = json.dumps(result)
f = open('result.json','w',encoding='utf-8')
f.write(json2)
f.close()
最终的结果是0.87395的成绩
DenseNet模型训练 plants_disease_detection
如果你觉得我写的不错，请给我一下Star(^_^)，谢谢！