[实战]200类鸟类细粒度图像分类_细粒度图像分类项目

[实战]200类鸟类细粒度分类识别

我又来了！！！！

一、图像分类

这次进行实战项目，鸟类细粒度分类识别实战。再讲细粒度分类之前，让我们先回顾一下图像分类吧。

图像分类是计算机视觉的最基础的一个任务，从最开始的入门级的mnist手写数字识别、猫狗图像二分类到后来的imagenet任务。图像分类模型随着数据集的增长，一步步提升到了今天的水平。计算机的图像分类水准已经超过了人类。

在这里我把图像分类任务分为了两种，一种是单标签的图像分类任务，一种是多标签的图像分类任务。

多标签的图像分类任务，更加符合人们的认知习惯。因为现实生活中的图片往往会包含多个类别物体。

而在单标签的图像分类任务中又可以分为三类：一种是跨物种语义级别的图像分类，即在不同物种的层次上识别不同类别对象，比如我们常见的猫狗分类。
一种是实例级图像分类即区分不同的个体，最典型的任务那就是人脸识别。 而还剩下最后一种就是细粒度分类，那么什么是细粒度分类呢？

二、图像细粒度分类

而细粒度图像分类，相比较我们前面所说 的跨物种的图像分类，级别更低一些。但相比较实例级的图像分类，级别稍高一些。

概念上的说法 是对同一大类中的子类的分类，
通俗来讲，其主要是解决 我们在日常生活中 可能看到一只狗，确分不清是哪种狗。

如下图所示，我们知道下图中哪一只是阿拉斯加 哪一只是哈士奇，左边是哈士奇 右边是阿拉斯加
这里可以当做可判别性部分是 阿拉斯加犬的鼻梁是与黑色毛色是相连的，这就是discriminative part 即可判别性模块。

三、图像细粒度分类目前的挑战

然后再讲讲细粒度分类实验目前所遇到的挑战吧。

细粒度分类的挑战，但如今面临着如下三大问题：类内差异大、类间差异小，以及有限的数据集。

由于光线，物体的姿势，视角、遮挡、背景干扰等等问题，
类内差异大，像这里的黑脚信天翁，由于光照，背景，姿势的干扰，从肉眼上很难看出属于同一个子类

类间差异小 不同个体归属于不同子类可能是由于一些微小的不同，如鸟的翅膀的颜色 以及鸟喙颜色的不同

以及有限的数据集的问题，数据集的标注 通常需要专业的知识以及耗费大量的标注时间。

由于上述挑战问题，我们很难根据现有的粗粒度神经网络模型得到精准的分类结果。

四、图像细粒度分类的研究现状

那么目前的研究现状是如何呢？

目前细粒度分类主要是通过寻找可判别性的特征来进行分类的，研究方法目前主要是可分为强监督学习和弱监督学习。

强监督学习是指通常使用边界框和局部标注信息，来获取目标的位置、大小，从而提高分类精度。 即给出了图片标注中物体的某些显著特征，即discriminative。

而弱监督学习是指仅利用图像的类别标注信息，不使用额外的标注，

目前弱监督学习的主要思路是定位出判别性的部位，取得判别性的特征做辅助来分类。

其实这很符合人类辨别细粒度物体的流程，先看全局信息知道大类，然后根据经验把注意力放在一些关键部位来做出判断，而这些部位就是弱监督网络所要找的discriminative parts。

目前的强监督学习方法有part-based r-cnn基于r-cnn算法完成了局部区域的检测，利用约束条件对r-cnn提取到的区域信息进行修正之后提取卷积特征，并将不同区域的特征进行拼接，构成最好的特征表示，然后通过SVM分类器进行分类训练。 Posed-normalized Cnn对每一张图片进行位置检测，然后将检测框内的图像进行裁剪，从而提取不同层次、不同位置的图像，再对提取到的图像块进行姿态对其送入CNN，将得到的特征拼接后利用SVM分类器进行分类。 Multi-proposal Net通过Edge Box Crop方法获取图像块，并引入关键点及视觉特征的输出层，进一步强化了局部特征与全部信息直接的位置关联。

弱监督方法，有图像过滤，仅借助于图像的类别信息过滤图片中与物体无关的模块，其中最有代表性的是Two-level算法。two attention level利用物体级和局部级的信息，通过Search Selective算法过滤掉无关背景，然后将过滤掉的背景送入CNN网络进行训练，得到物体级的分类结果，随后通过聚类算法将不同位置的特征继续区分，并将不同区域的特征拼接后送入svm分类器进行训练。

人在认知物体和事物时，往往需要完成对其特征的理解及类别名称的记忆，B-CNN根据大脑工作时同认知类别和关注显著特征的方法，构建了两个线性网络，协调完成局部特征提取和分类的任务。

到这里，前期的基础知识差不多就完成了，下面准备进入正题。

五、200类鸟类细粒度图像分类实战

1.CUB200-2011数据集

首先还是一如既往先介绍我们的驱动力----数据。

不对，放错图了，应该是下面这张。

本次细粒度分类所采取的数据集CUB200-2011，该数据集是由加州理工学院在2010年提出的细粒度数据集，也是目前细粒度分类识别研究的基准图像数据集，该数据集共有117888张鸟类图像，包含了200类鸟类子类，其中训练数据集有5994张图像，测试集有5794张图像，每张图像均提供了图像类标注信息，图像中鸟的bounding box，鸟的关键part信息，以及鸟的属性信息。

评判标准就是以准确率了。

好了，准备上模型了！

2.VGG16模型

先用VGG16来投石问路

在此之前准备好我们的微调模型

# fine-tune 模型
def fine_tune_model(model, optimizer, batch_size, epochs, freeze_num):
    '''
    discription: 对指定预训练模型进行fine-tune，并保存为.hdf5格式
    
    MODEL：传入的模型，VGG16， ResNet50, ...

    optimizer: fine-tune all layers 的优化器, first part默认用adadelta
    batch_size: 每一批的尺寸，建议32/64/128
    epochs: fine-tune all layers的代数
    freeze_num: first part冻结卷积层的数量
    '''

    # datagen = ImageDataGenerator(
    #     rescale=1.255,
    #     # shear_range=0.2,
    #     # zoom_range=0.2,
    #     # horizontal_flip=True,
    #     # vertical_flip=True,
    #     # fill_mode="nearest"
    #   )
    
    # datagen.fit(X_train)
    
    
    # first: 仅训练全连接层（权重随机初始化的）
    # 冻结所有卷积层
    
    for layer in model.layers[:freeze_num]:
        layer.trainable = False
    
    model.compile(optimizer=optimizer, 
                  loss="categorical_crossentropy",
                  metrics=["accuracy"])

    # model.fit_generator(datagen.flow(x_train,y_train,batch_size=batch_size),
    #                     steps_per_epoch=len(x_train)/32,
    #                     epochs=3,
    #                     shuffle=True,
    #                     verbose=1,
    #                     datagen.flow(x_valid, y_valid))
    model.fit(x_train,
         y_train,
         batch_size=batch_size,
         epochs=3,
         shuffle=True,
         verbose=1,
         validation_data=(x_valid,y_valid)
        )
    print('Finish step_1')
    
    
    # second: fine-tune all layers
    for layer in model.layers[:]:
        layer.trainable = True
    
    rc = ReduceLROnPlateau(monitor="val_acc",
                factor=0.2,
                patience=4,
                verbose=1,
                mode='max')

    model_name = model.name  + ".hdf5"
    mc = ModelCheckpoint(model_name, 
               monitor="val_acc", 
               save_best_only=True,
               verbose=1,
               mode='max')
    el = EarlyStopping(monitor="val_acc",
              min_delta=0,
              patience=5,
              verbose=1,
              restore_best_weights=True)
    
    model.compile(optimizer=optimizer, 
           loss='categorical_crossentropy', 
           metrics=["accuracy"])

    # history_fit = model.fit_generator(datagen.flow(x_train,y_train,batch_size=32),
    #                                  steps_per_epoch=len(x_train)/32,
    #                                  epochs=epochs,
    #                                  shuffle=True,
    #                                  verbose=1,
    #                                  callbacks=[mc,rc,el],
    #                                  datagen.flow(x_valid, y_valid))
    history_fit = model.fit(x_train,
                 y_train,
                 batch_size=batch_size,
                 epochs=epochs,
                 shuffle=True,
                 verbose=1,
                 validation_data=(x_valid,y_valid),
                 callbacks=[mc,rc,el])
    
    print('Finish fine-tune')
    return history_fit
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1.VGG16模型

# 定义一个VGG16的模型
def vgg16_model(img_rows,img_cols):
  x = Input(shape=(img_rows, img_cols, 3))
  x = Lambda(imagenet_utils.preprocess_input)(x)
  base_model = VGG16(input_tensor=x,weights="imagenet",include_top=False, pooling='avg')
  x = base_model.output
  x = Dense(1024,activation="relu",name="fc1")(x)
  x = Dropout(0.5)(x)
  predictions = Dense(n_classes,activation="softmax",name="predictions")(x)

  vgg16_model = Model(inputs=base_model.input,outputs=predictions,name="vgg16")
  
  return vgg16_model
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# 创建VGG16模型
img_rows, img_cols = 300, 300
vgg16_model = vgg16_model(img_rows,img_cols)
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for i,layer in enumerate(vgg16_model.layers):
  print(i,layer.name)
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0 input_3
1 lambda_3
2 block1_conv1
3 block1_conv2
4 block1_pool
5 block2_conv1
6 block2_conv2
7 block2_pool
8 block3_conv1
9 block3_conv2
10 block3_conv3
11 block3_pool
12 block4_conv1
13 block4_conv2
14 block4_conv3
15 block4_pool
16 block5_conv1
17 block5_conv2
18 block5_conv3
19 block5_pool
20 global_average_pooling2d_3
21 fc1
22 dropout_3
23 predictions
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optimizer = optimizers.Adam(lr=0.0001)
batch_size = 32
epochs = 30
freeze_num = 21


%time vgg16_history = fine_tune_model(vgg16_model,optimizer,batch_size,epochs,freeze_num)
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WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.    
Train on 7013 samples, validate on 3006 samples
Epoch 1/3
7013/7013 [==============================] - 53s 8ms/step - loss: 7.1095 - acc: 0.0211 - val_loss: 4.4823 - val_acc: 0.0915
Epoch 2/3
7013/7013 [==============================] - 46s 7ms/step - loss: 4.4798 - acc: 0.0914 - val_loss: 3.6892 - val_acc: 0.2239
Epoch 3/3
7013/7013 [==============================] - 46s 7ms/step - loss: 3.6436 - acc: 0.1925 - val_loss: 3.0040 - val_acc: 0.3440
Finish step_1
Train on 7013 samples, validate on 3006 samples
Epoch 1/30
7013/7013 [==============================] - 47s 7ms/step - loss: 2.9171 - acc: 0.3087 - val_loss: 2.1821 - val_acc: 0.4667

Epoch 00001: val_loss improved from inf to 2.18212, saving model to vgg16.hdf5
Epoch 2/30
7013/7013 [==============================] - 46s 7ms/step - loss: 1.9944 - acc: 0.4840 - val_loss: 1.8748 - val_acc: 0.5226

Epoch 00002: val_loss improved from 2.18212 to 1.87480, saving model to vgg16.hdf5
Epoch 3/30
7013/7013 [==============================] - 46s 7ms/step - loss: 1.6493 - acc: 0.5551 - val_loss: 1.7540 - val_acc: 0.5492

Epoch 00003: val_loss improved from 1.87480 to 1.75400, saving model to vgg16.hdf5
Epoch 4/30
7013/7013 [==============================] - 46s 7ms/step - loss: 1.4144 - acc: 0.6144 - val_loss: 1.6711 - val_acc: 0.5655

Epoch 00004: val_loss improved from 1.75400 to 1.67106, saving model to vgg16.hdf5
Epoch 5/30
7013/7013 [==============================] - 46s 7ms/step - loss: 1.2055 - acc: 0.6628 - val_loss: 1.6020 - val_acc: 0.5749

Epoch 00005: val_loss improved from 1.67106 to 1.60200, saving model to vgg16.hdf5


Epoch 00026: val_loss improved from 1.32242 to 1.32005, saving model to vgg16.hdf5
Epoch 27/30
7013/7013 [==============================] - 46s 7ms/step - loss: 0.1979 - acc: 0.9511 - val_loss: 1.3209 - val_acc: 0.6517

Epoch 00027: val_loss did not improve from 1.32005
Epoch 28/30
7013/7013 [==============================] - 46s 7ms/step - loss: 0.1996 - acc: 0.9528 - val_loss: 1.3206 - val_acc: 0.6514

Epoch 00028: val_loss did not improve from 1.32005
Epoch 29/30
7013/7013 [==============================] - 46s 7ms/step - loss: 0.1956 - acc: 0.9555 - val_loss: 1.3216 - val_acc: 0.6517

Epoch 00029: val_loss did not improve from 1.32005

Epoch 00029: ReduceLROnPlateau reducing learning rate to 3.999999898951501e-06.
Epoch 30/30
7013/7013 [==============================] - 46s 7ms/step - loss: 0.1884 - acc: 0.9558 - val_loss: 1.3194 - val_acc: 0.6514

Epoch 00030: val_loss improved from 1.32005 to 1.31935, saving model to vgg16.hdf5
Finish fine-tune
CPU times: user 10min, sys: 3min 58s, total: 13min 58s
Wall time: 25min 37s
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history_plot(vgg16_history)
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进过上面的一系列操作，我们可以看到VGG16的分类效果，并不是很好呀，只能刚刚及格。

那么下面有请我们的二号选手EfficientNet

2.EfficientNetB4

咚咚咚，它来了，它来了，它踩着七彩祥云来了！！！

好了，不多说了，直接上代码来搭建EfficientNet网络架构。

# 定义一个EfficientNet模型
def efficient_model(img_rows,img_cols):
  K.clear_session()
  x = Input(shape=(img_rows,img_cols,3))
  x = Lambda(imagenet_utils.preprocess_input)(x)
  
  base_model = EfficientNetB4(input_tensor=x,weights="imagenet",include_top=False,pooling="avg")
  x = base_model.output
  x = Dense(1024,activation="relu",name="fc1")(x)
  x = Dropout(0.5)(x)
  predictions = Dense(n_classes,activation="softmax",name="predictions")(x)

  eB_model = Model(inputs=base_model.input,outputs=predictions,name="eB4")

  return eB_model
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# 创建Efficient模型
img_rows,img_cols=224,224
eB_model = efficient_model(img_rows,img_cols)
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optimizer = optimizers.Adam(lr=0.0001)
batch_size = 32
epochs = 30
freeze_num = 469
eB_model_history  = fine_tune_model(eB_model,optimizer,batch_size,epochs,freeze_num)
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Train on 8251 samples, validate on 1768 samples
Epoch 1/3
8251/8251 [==============================] - 49s 6ms/step - loss: 9.3405 - acc: 0.0053 - val_loss: 5.5664 - val_acc: 0.0051
Epoch 2/3
8251/8251 [==============================] - 38s 5ms/step - loss: 6.8968 - acc: 0.0052 - val_loss: 5.3289 - val_acc: 0.0040
Epoch 3/3
8251/8251 [==============================] - 39s 5ms/step - loss: 5.8723 - acc: 0.0061 - val_loss: 5.3021 - val_acc: 0.0040
Finish step_1
Train on 8251 samples, validate on 1768 samples
Epoch 1/30
8251/8251 [==============================] - 261s 32ms/step - loss: 4.4794 - acc: 0.0980 - val_loss: 2.7448 - val_acc: 0.3399

Epoch 00001: val_loss improved from inf to 2.74482, saving model to eB4.hdf5
Epoch 2/30
8251/8251 [==============================] - 155s 19ms/step - loss: 2.2635 - acc: 0.4157 - val_loss: 1.4371 - val_acc: 0.5973

Epoch 00002: val_loss improved from 2.74482 to 1.43707, saving model to eB4.hdf5
Epoch 3/30
8251/8251 [==============================] - 155s 19ms/step - loss: 1.3465 - acc: 0.6244 - val_loss: 1.1637 - val_acc: 0.6719

Epoch 00003: val_loss improved from 1.43707 to 1.16373, saving model to eB4.hdf5
Epoch 4/30
8251/8251 [==============================] - 154s 19ms/step - loss: 0.8824 - acc: 0.7488 - val_loss: 0.9904 - val_acc: 0.7110


Epoch 00016: val_loss did not improve from 0.89365
Epoch 17/30
8251/8251 [==============================] - 154s 19ms/step - loss: 0.0718 - acc: 0.9867 - val_loss: 0.8993 - val_acc: 0.7749

Epoch 00017: val_loss did not improve from 0.89365
Restoring model weights from the end of the best epoch
Epoch 00017: early stopping
Finish fine-tune
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history_plot(eB_model_history)
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效果很不错呀，EfficientNet不愧是谷歌出品的，必是精品。那么既然EfficientNet的效果已经这么好了，你是不是就不想接着看了，你是不是已经迫不及待想尝试EfficientNet的效果了呢。

不要急，下面还有几个小尝试，首先是在EfficientNet中加入Attention机制，至于Attention机制的话，可以去看我的博客里面有写到，那是在我未解放天性之前，写的可正经了。当然这里更是正经！！！

3.efficientnet-with-attention

# 定义一个加入Attention模块的Efficient网络架构即efficientnet-with-attention

def efficient_attention_model(img_rows,img_cols):
  K.clear_session()
  
  in_lay = Input(shape=(img_rows,img_cols,3))
  base_model = EfficientNetB3(input_shape=(img_rows,img_cols,3),weights="imagenet",include_top=False)

  pt_depth = base_model.get_output_shape_at(0)[-1]

  pt_features = base_model(in_lay)
  bn_features = BatchNormalization()(pt_features)

  # here we do an attention mechanism to turn pixels in the GAP on an off
  atten_layer = Conv2D(64,kernel_size=(1,1),padding="same",activation="relu")(Dropout(0.5)(bn_features))
  atten_layer = Conv2D(16,kernel_size=(1,1),padding="same",activation="relu")(atten_layer)
  atten_layer = Conv2D(8,kernel_size=(1,1),padding="same",activation="relu")(atten_layer)
  atten_layer = Conv2D(1,kernel_size=(1,1),padding="valid",activation="sigmoid")(atten_layer)# H,W,1
  # fan it out to all of the channels
  up_c2_w = np.ones((1,1,1,pt_depth)) #1,1,C
  up_c2 = Conv2D(pt_depth,kernel_size=(1,1),padding="same",activation="linear",use_bias=False,weights=[up_c2_w])
  up_c2.trainable = False
  atten_layer = up_c2(atten_layer)# H,W,C

  mask_features = multiply([atten_layer,bn_features])# H,W,C

  gap_features = GlobalAveragePooling2D()(mask_features)# 1,1,C
  # gap_mask = GlobalAveragePooling2D()(atten_layer)# 1,1,C

  # # to account for missing values from the attention model
  # gap = Lambda(lambda x:x[0]/x[1],name="RescaleGAP")([gap_features,gap_mask])
  gap_dr = Dropout(0.25)(gap_features)
  dr_steps = Dropout(0.25)(Dense(1000,activation="relu")(gap_dr))
  out_layer = Dense(200,activation="softmax")(dr_steps)
  eb_atten_model = Model(inputs=[in_lay],outputs=[out_layer])

  return eb_atten_model
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img_rows,img_cols = 224,224
eB_atten_model = efficient_attention_model(img_rows,img_cols)
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eB_atten_model.save("eb_atten_model.h5")
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for i,layer in enumerate(eB_atten_model.layers):
  print(i,layer.name)
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0 input_1
1 efficientnet-b3
2 batch_normalization_1
3 dropout_1
4 conv2d_1
5 conv2d_2
6 conv2d_3
7 conv2d_4
8 conv2d_5
9 multiply_1
10 global_average_pooling2d_1
11 dropout_2
12 dense_1
13 dropout_3
14 dense_2
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optimizer = optimizers.Adam(lr=0.0001)
batch_size = 32
epochs = 30
freeze_num = 12
eB_atten_model_history  = fine_tune_model(eB_atten_model,optimizer,batch_size,epochs,freeze_num)
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WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.



Train on 8251 samples, validate on 1768 samples
Epoch 1/3
8251/8251 [==============================] - 39s 5ms/step - loss: 5.2083 - acc: 0.0221 - val_loss: 16.0324 - val_acc: 0.0040
Epoch 2/3
8251/8251 [==============================] - 28s 3ms/step - loss: 4.7719 - acc: 0.1130 - val_loss: 16.0147 - val_acc: 0.0057
Epoch 3/3
8251/8251 [==============================] - 28s 3ms/step - loss: 4.3135 - acc: 0.2112 - val_loss: 16.0056 - val_acc: 0.0062
Finish step_1
Train on 8251 samples, validate on 1768 samples
Epoch 1/30
8251/8251 [==============================] - 168s 20ms/step - loss: 2.1612 - acc: 0.4549 - val_loss: 1.1888 - val_acc: 0.6725

Epoch 00001: val_loss improved from inf to 1.18880, saving model to model_1.hdf5
Epoch 2/30
8251/8251 [==============================] - 121s 15ms/step - loss: 0.9003 - acc: 0.7442 - val_loss: 0.9400 - val_acc: 0.7330

Epoch 00002: val_loss improved from 1.18880 to 0.94002, saving model to model_1.hdf5
Epoch 3/30
8251/8251 [==============================] - 121s 15ms/step - loss: 0.5455 - acc: 0.8467 - val_loss: 0.8569 - val_acc: 0.7574


Epoch 00013: val_loss did not improve from 0.78748
Epoch 14/30
8251/8251 [==============================] - 121s 15ms/step - loss: 0.0417 - acc: 0.9924 - val_loss: 0.7958 - val_acc: 0.7924

Epoch 00014: val_loss did not improve from 0.78748

Epoch 00014: ReduceLROnPlateau reducing learning rate to 3.999999898951501e-06.
Epoch 15/30
8251/8251 [==============================] - 121s 15ms/step - loss: 0.0370 - acc: 0.9936 - val_loss: 0.7938 - val_acc: 0.7941

Epoch 00015: val_loss did not improve from 0.78748
Epoch 16/30
8251/8251 [==============================] - 121s 15ms/step - loss: 0.0379 - acc: 0.9933 - val_loss: 0.7932 - val_acc: 0.7952

Epoch 00016: val_loss did not improve from 0.78748
Restoring model weights from the end of the best epoch
Epoch 00016: early stopping
Finish fine-tune
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history_plot(eB_atten_model_history)
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[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-

效果还是提升了一点点的，下面是又尝试了另外一种attention的写法，用到senet和cbam。当然如果你不了解的话，还是老规矩，去看我的博客，卷积神经网络发展史里面有提到。

4.EfficientNetB3 with attention v2

from keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D, Reshape, Dense, multiply, Permute, Concatenate, Conv2D, Add, Activation, Lambda
from keras import backend as K
from keras.activations import sigmoid

def attach_attention_module(net, attention_module):
  if attention_module == 'se_block': # SE_block
    net = se_block(net)
  elif attention_module == 'cbam_block': # CBAM_block
    net = cbam_block(net)
  else:
    raise Exception("'{}' is not supported attention module!".format(attention_module))

  return net

def se_block(input_feature, ratio=8):
	"""Contains the implementation of Squeeze-and-Excitation(SE) block.
	As described in https://arxiv.org/abs/1709.01507.
	"""
	
	channel_axis = 1 if K.image_data_format() == "channels_first" else -1
	channel = input_feature._keras_shape[channel_axis]

	se_feature = GlobalAveragePooling2D()(input_feature)
	se_feature = Reshape((1, 1, channel))(se_feature)
	assert se_feature._keras_shape[1:] == (1,1,channel)
	se_feature = Dense(channel // ratio,
					   activation='relu',
					   kernel_initializer='he_normal',
					   use_bias=True,
					   bias_initializer='zeros')(se_feature)
	assert se_feature._keras_shape[1:] == (1,1,channel//ratio)
	se_feature = Dense(channel,
					   activation='sigmoid',
					   kernel_initializer='he_normal',
					   use_bias=True,
					   bias_initializer='zeros')(se_feature)
	assert se_feature._keras_shape[1:] == (1,1,channel)
	if K.image_data_format() == 'channels_first':
		se_feature = Permute((3, 1, 2))(se_feature)

	se_feature = multiply([input_feature, se_feature])
	return se_feature

def cbam_block(cbam_feature, ratio=8):
	"""Contains the implementation of Convolutional Block Attention Module(CBAM) block.
	As described in https://arxiv.org/abs/1807.06521.
	"""
	
	cbam_feature = channel_attention(cbam_feature, ratio)
	cbam_feature = spatial_attention(cbam_feature)
	return cbam_feature

def channel_attention(input_feature, ratio=8):
	
	channel_axis = 1 if K.image_data_format() == "channels_first" else -1
	channel = input_feature._keras_shape[channel_axis]
	
	shared_layer_one = Dense(channel//ratio,
							 activation='relu',
							 kernel_initializer='he_normal',
							 use_bias=True,
							 bias_initializer='zeros')
	shared_layer_two = Dense(channel,
							 kernel_initializer='he_normal',
							 use_bias=True,
							 bias_initializer='zeros')
	
	avg_pool = GlobalAveragePooling2D()(input_feature)    
	avg_pool = Reshape((1,1,channel))(avg_pool)
	assert avg_pool._keras_shape[1:] == (1,1,channel)
	avg_pool = shared_layer_one(avg_pool)
	assert avg_pool._keras_shape[1:] == (1,1,channel//ratio)
	avg_pool = shared_layer_two(avg_pool)
	assert avg_pool._keras_shape[1:] == (1,1,channel)
	
	max_pool = GlobalMaxPooling2D()(input_feature)
	max_pool = Reshape((1,1,channel))(max_pool)
	assert max_pool._keras_shape[1:] == (1,1,channel)
	max_pool = shared_layer_one(max_pool)
	assert max_pool._keras_shape[1:] == (1,1,channel//ratio)
	max_pool = shared_layer_two(max_pool)
	assert max_pool._keras_shape[1:] == (1,1,channel)
	
	cbam_feature = Add()([avg_pool,max_pool])
	cbam_feature = Activation('sigmoid')(cbam_feature)
	
	if K.image_data_format() == "channels_first":
		cbam_feature = Permute((3, 1, 2))(cbam_feature)
	
	return multiply([input_feature, cbam_feature])

def spatial_attention(input_feature):
	kernel_size = 7
	
	if K.image_data_format() == "channels_first":
		channel = input_feature._keras_shape[1]
		cbam_feature = Permute((2,3,1))(input_feature)
	else:
		channel = input_feature._keras_shape[-1]
		cbam_feature = input_feature
	
	avg_pool = Lambda(lambda x: K.mean(x, axis=3, keepdims=True))(cbam_feature)
	assert avg_pool._keras_shape[-1] == 1
	max_pool = Lambda(lambda x: K.max(x, axis=3, keepdims=True))(cbam_feature)
	assert max_pool._keras_shape[-1] == 1
	concat = Concatenate(axis=3)([avg_pool, max_pool])
	assert concat._keras_shape[-1] == 2
	cbam_feature = Conv2D(filters = 1,
					kernel_size=kernel_size,
					strides=1,
					padding='same',
					activation='sigmoid',
					kernel_initializer='he_normal',
					use_bias=False)(concat)	
	assert cbam_feature._keras_shape[-1] == 1
	
	if K.image_data_format() == "channels_first":
		cbam_feature = Permute((3, 1, 2))(cbam_feature)
		
	return multiply([input_feature, cbam_feature])
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# 定义一个EfficientNet模型
def efficient__atten2_model(img_rows,img_cols):
  K.clear_session()
  
  in_lay = Input(shape=(img_rows,img_cols,3))
  base_model = EfficientNetB3(input_shape=(img_rows,img_cols,3),weights="imagenet",include_top=False)
  pt_features = base_model(in_lay)
  bn_features = BatchNormalization()(pt_features)

  atten_features = attach_attention_module(bn_features,"se_block")
  gap_features = GlobalAveragePooling2D()(atten_features)

  gap_dr = Dropout(0.25)(gap_features)
  dr_steps = Dropout(0.25)(Dense(1000,activation="relu")(gap_dr))
  out_layer = Dense(n_classes,activation="softmax")(dr_steps)
  eb_atten_model = Model(inputs=[in_lay],outputs=[out_layer])

  return eb_atten_model
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img_rows,img_cols = 224,224
eB_atten2_model = efficient__atten2_model(img_rows,img_cols)
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optimizer = optimizers.Adam(lr=0.0001)
batch_size = 32
epochs = 30
freeze_num = 19
eB_atten2_model_history  = fine_tune_model(eB_atten2_model,optimizer,batch_size,epochs,freeze_num)
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Train on 8251 samples, validate on 1768 samples
Epoch 1/3
8251/8251 [==============================] - 33s 4ms/step - loss: 5.3202 - acc: 0.0061 - val_loss: 16.0269 - val_acc: 0.0057
Epoch 2/3
8251/8251 [==============================] - 26s 3ms/step - loss: 5.3261 - acc: 0.0051 - val_loss: 16.0269 - val_acc: 0.0057
Epoch 3/3
8251/8251 [==============================] - 26s 3ms/step - loss: 5.3248 - acc: 0.0048 - val_loss: 16.0269 - val_acc: 0.0057
Finish step_1
Train on 8251 samples, validate on 1768 samples
Epoch 1/30
8251/8251 [==============================] - 153s 19ms/step - loss: 3.9559 - acc: 0.1742 - val_loss: 2.1066 - val_acc: 0.4712

Epoch 00001: val_loss improved from inf to 2.10657, saving model to model_1.hdf5
Epoch 2/30
8251/8251 [==============================] - 119s 14ms/step - loss: 1.6183 - acc: 0.5708 - val_loss: 1.1768 - val_acc: 0.6618

Epoch 00002: val_loss improved from 2.10657 to 1.17679, saving model to model_1.hdf5
Epoch 3/30
8251/8251 [==============================] - 119s 14ms/step - loss: 0.9172 - acc: 0.7374 - val_loss: 0.9507 - val_acc: 0.7189

Epoch 00003: val_loss improved from 1.17679 to 0.95071, saving model to model_1.hdf5
Epoch 4/30
8251/8251 [==============================] - 119s 14ms/step - loss: 0.5897 - acc: 0.8317 - val_loss: 0.8628 - val_acc: 0.7562

Epoch 00004: val_loss improved from 0.95071 to 0.86283, saving model to model_1.hdf5
Epoch 5/30
8251/8251 [==============================] - 119s 14ms/step - loss: 0.3838 - acc: 0.8956 - val_loss: 0.8359 - val_acc: 0.7636

Epoch 00005: val_loss improved from 0.86283 to 0.83592, saving model to model_1.hdf5
Epoch 6/30
8251/8251 [==============================] - 119s 14ms/step - loss: 0.2797 - acc: 0.9234 - val_loss: 0.8280 - val_acc: 0.7647

Epoch 00006: val_loss improved from 0.83592 to 0.82797, saving model to model_1.hdf5
Epoch 7/30
8251/8251 [==============================] - 119s 14ms/step - loss: 0.1997 - acc: 0.9495 - val_loss: 0.8620 - val_acc: 0.7602

Epoch 00007: val_loss did not improve from 0.82797
Epoch 8/30
8251/8251 [==============================] - 119s 14ms/step - loss: 0.1408 - acc: 0.9667 - val_loss: 0.8602 - val_acc: 0.7800

Epoch 00008: val_loss did not improve from 0.82797
Epoch 9/30
8251/8251 [==============================] - 119s 14ms/step - loss: 0.1103 - acc: 0.9739 - val_loss: 0.9202 - val_acc: 0.7545

Epoch 00009: val_loss did not improve from 0.82797

Epoch 00009: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.
Epoch 10/30
8251/8251 [==============================] - 119s 14ms/step - loss: 0.0803 - acc: 0.9824 - val_loss: 0.8677 - val_acc: 0.7709

Epoch 00010: val_loss did not improve from 0.82797
Epoch 11/30
8251/8251 [==============================] - 119s 14ms/step - loss: 0.0772 - acc: 0.9833 - val_loss: 0.8560 - val_acc: 0.7771

Epoch 00011: val_loss did not improve from 0.82797
Restoring model weights from the end of the best epoch
Epoch 00011: early stopping
Finish fine-tune
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history_plot(eB_atten2_model_history)
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咱也不知道为什么，这个效果比上面那个attention的写法，会提升一点点，这就是炼丹吧。

5.双线性EfficientNet

下面就是尝试了一种双线性的网络架构，这里我还画了图呢！！！

该模型的整体流程是：

• 将图片输入,并对输入图片进行数据增强操作；
• 之后主干网络用19年google提出的efficientnet架构来提取feature maps；
• 之后再结合注意力模块，提取attention maps。
• 然后将attention maps与feature maps逐一相乘，最后在加入全连接层进行分类，从而得到最终的分类结果。

这里也一并给出attention机制的图吧！

# 定义一个双线性EfficientNet Attention模型
def blinear_efficient__atten_model(img_rows,img_cols):
  K.clear_session()
  
  in_lay = Input(shape=(img_rows,img_cols,3))
  base_model = EfficientNetB3(input_shape=(img_rows,img_cols,3),weights="imagenet",include_top=False)
  
  pt_depth = base_model.get_output_shape_at(0)[-1]

  cnn_features_a = base_model(in_lay)
  cnn_bn_features_a = BatchNormalization()(cnn_features_a)
  
  # attention mechanism
  # here we do an attention mechanism to turn pixels in the GAP on an off
  atten_layer = Conv2D(64,kernel_size=(1,1),padding="same",activation="relu")(Dropout(0.5)(cnn_bn_features_a))
  atten_layer = Conv2D(16,kernel_size=(1,1),padding="same",activation="relu")(atten_layer)
  atten_layer = Conv2D(8,kernel_size=(1,1),padding="same",activation="relu")(atten_layer)
  atten_layer = Conv2D(1,kernel_size=(1,1),padding="valid",activation="sigmoid")(atten_layer)# H,W,1
  # fan it out to all of the channels
  up_c2_w = np.ones((1,1,1,pt_depth)) #1,1,C
  up_c2 = Conv2D(pt_depth,kernel_size=(1,1),padding="same",activation="linear",use_bias=False,weights=[up_c2_w])
  up_c2.trainable = True
  atten_layer = up_c2(atten_layer)# H,W,C

  cnn_atten_out_a = multiply([atten_layer,cnn_bn_features_a])# H,W,C

  cnn_atten_out_b = cnn_atten_out_a

  cnn_out_dot = multiply([cnn_atten_out_a,cnn_atten_out_b])
  gap_features = GlobalAveragePooling2D()(cnn_out_dot)
  gap_dr = Dropout(0.25)(gap_features)
  dr_steps = Dropout(0.25)(Dense(1000,activation="relu")(gap_dr))
  out_layer = Dense(200,activation="softmax")(dr_steps)
  
  b_eff_atten_model = Model(inputs=[in_lay],outputs=[out_layer],name="blinear_efficient_atten")

  return b_eff_atten_model
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# 创建双线性EfficientNet Attention模型
img_rows,img_cols = 256,256
befficient_model = blinear_efficient__atten_model(img_rows,img_cols)
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befficient_model.save("befficient_model.h5")
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optimizer = optimizers.Adam(lr=0.0001)
batch_size = 32
epochs = 30
freeze_num = 19
befficient_model_history  = fine_tune_model(befficient_model,optimizer,batch_size,epochs,freeze_num)
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Train on 8251 samples, validate on 1768 samples
Epoch 1/3
8251/8251 [==============================] - 38s 5ms/step - loss: 5.3903 - acc: 0.0052 - val_loss: 14.1897 - val_acc: 0.0040
Epoch 2/3
8251/8251 [==============================] - 33s 4ms/step - loss: 5.3926 - acc: 0.0052 - val_loss: 14.1897 - val_acc: 0.0040
Epoch 3/3
8251/8251 [==============================] - 33s 4ms/step - loss: 5.3948 - acc: 0.0068 - val_loss: 14.1897 - val_acc: 0.0040
Finish step_1
Train on 8251 samples, validate on 1768 samples
Epoch 1/30
8251/8251 [==============================] - 193s 23ms/step - loss: 4.7127 - acc: 0.0749 - val_loss: 2.9079 - val_acc: 0.3060

Epoch 00001: val_acc improved from -inf to 0.30600, saving model to blinear_efficient_atten.hdf5
Epoch 2/30
8251/8251 [==============================] - 148s 18ms/step - loss: 2.1653 - acc: 0.4462 - val_loss: 1.3817 - val_acc: 0.6160

Epoch 00002: val_acc improved from 0.30600 to 0.61595, saving model to blinear_efficient_atten.hdf5
Epoch 3/30
8251/8251 [==============================] - 149s 18ms/step - loss: 1.1834 - acc: 0.6676 - val_loss: 1.0714 - val_acc: 0.7002

Epoch 00003: val_acc improved from 0.61595 to 0.70023, saving model to blinear_efficient_atten.hdf5
Epoch 4/30
8251/8251 [==============================] - 149s 18ms/step - loss: 0.8070 - acc: 0.7666 - val_loss: 0.9743 - val_acc: 0.7342

Epoch 00004: val_acc improved from 0.70023 to 0.73416, saving model to blinear_efficient_atten.hdf5
Epoch 5/30


Epoch 00007: val_acc improved from 0.74830 to 0.75735, saving model to blinear_efficient_atten.hdf5

Epoch 00010: val_acc did not improve from 0.76867
Epoch 11/30
8251/8251 [==============================] - 149s 18ms/step - loss: 0.1421 - acc: 0.9547 - val_loss: 1.1319 - val_acc: 0.7692

Epoch 00011: val_acc improved from 0.76867 to 0.76923, saving model to blinear_efficient_atten.hdf5
Epoch 12/30
8251/8251 [==============================] - 149s 18ms/step - loss: 0.1232 - acc: 0.9622 - val_loss: 1.0809 - val_acc: 0.7704



Epoch 00018: val_acc improved from 0.77489 to 0.78224, saving model to blinear_efficient_atten.hdf5
Epoch 19/30
8251/8251 [==============================] - 149s 18ms/step - loss: 0.0880 - acc: 0.9714 - val_loss: 1.2171 - val_acc: 0.7721



Epoch 00022: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.
Epoch 23/30
8251/8251 [==============================] - 148s 18ms/step - loss: 0.0465 - acc: 0.9859 - val_loss: 1.1591 - val_acc: 0.7930

Epoch 00023: val_acc improved from 0.78224 to 0.79299, saving model to blinear_efficient_atten.hdf5
Epoch 24/30
8251/8251 [==============================] - 148s 18ms/step - loss: 0.0360 - acc: 0.9893 - val_loss: 1.1312 - val_acc: 0.7969

Epoch 00024: val_acc improved from 0.79299 to 0.79695, saving model to blinear_efficient_atten.hdf5
Epoch 25/30
8251/8251 [==============================] - 148s 18ms/step - loss: 0.0275 - acc: 0.9920 - val_loss: 1.1477 - val_acc: 0.8015



Epoch 00028: val_acc did not improve from 0.80147
Epoch 29/30
8251/8251 [==============================] - 148s 18ms/step - loss: 0.0248 - acc: 0.9922 - val_loss: 1.1467 - val_acc: 0.8020

Epoch 00029: val_acc improved from 0.80147 to 0.80204, saving model to blinear_efficient_atten.hdf5
Epoch 30/30
8251/8251 [==============================] - 148s 18ms/step - loss: 0.0232 - acc: 0.9919 - val_loss: 1.1427 - val_acc: 0.8003

Epoch 00030: val_acc did not improve from 0.80204
Finish fine-tune
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history_plot(befficient_model_history)
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可以从图中看到，双线性的结构，准确率还会提升一些。

终于来到故事的结尾处了，最后在尝试一些双线性的VGG16。

6.双线性VGG16模型

# 定义双线性VGG16模型

from keras import backend as K

def batch_dot(cnn_ab):
    return K.batch_dot(cnn_ab[0], cnn_ab[1], axes=[1, 1])

def sign_sqrt(x):
    return K.sign(x) * K.sqrt(K.abs(x) + 1e-10)

def l2_norm(x):
    return K.l2_normalize(x, axis=-1)
 
 
def bilinear_vgg16(img_rows,img_cols):
    input_tensor = Input(shape=(img_rows,img_cols,3))
    input_tensor = Lambda(imagenet_utils.preprocess_input)(input_tensor)

    model_vgg16 = VGG16(include_top=False, weights="imagenet",
                        input_tensor=input_tensor,pooling="avg")
    
    cnn_out_a = model_vgg16.layers[-2].output
    cnn_out_shape = model_vgg16.layers[-2].output_shape
    cnn_out_a = Reshape([cnn_out_shape[1]*cnn_out_shape[2],
                         cnn_out_shape[-1]])(cnn_out_a)

    cnn_out_b = cnn_out_a

    cnn_out_dot = Lambda(batch_dot)([cnn_out_a, cnn_out_b])
    cnn_out_dot = Reshape([cnn_out_shape[-1]*cnn_out_shape[-1]])(cnn_out_dot)
 
    sign_sqrt_out = Lambda(sign_sqrt)(cnn_out_dot)
    l2_norm_out = Lambda(l2_norm)(sign_sqrt_out)
    
    fc1 = Dense(1024,activation="relu",name="fc1")(l2_norm_out)
    dropout = Dropout(0.5)(fc1)
    output = Dense(n_classes, activation="softmax",name="output")(dropout)
    bvgg16_model = Model(inputs=model_vgg16.input, outputs=output,name="bvgg16")

    return bvgg16_model

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# 创建双线性VGG16模型
img_rows,img_cols = 300,300
bvgg16_model = bilinear_vgg16(img_rows,img_cols)
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for i,layer in enumerate(bvgg16_model.layers):
  print(i,layer.name)
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0 input_1
1 lambda_1
2 block1_conv1
3 block1_conv2
4 block1_pool
5 block2_conv1
6 block2_conv2
7 block2_pool
8 block3_conv1
9 block3_conv2
10 block3_conv3
11 block3_pool
12 block4_conv1
13 block4_conv2
14 block4_conv3
15 block4_pool
16 block5_conv1
17 block5_conv2
18 block5_conv3
19 block5_pool
20 reshape_1
21 lambda_2
22 reshape_2
23 lambda_3
24 lambda_4
25 fc1
26 dropout_1
27 output
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optimizer = optimizers.Adam(lr=0.0001)
batch_size = 32
epochs = 100
freeze_num = 25
bvgg16_history = fine_tune_model(bvgg16_model,optimizer,batch_size,epochs,freeze_num)
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WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.



Train on 8251 samples, validate on 1768 samples
Epoch 1/3
8251/8251 [==============================] - 80s 10ms/step - loss: 5.1197 - acc: 0.0572 - val_loss: 4.8534 - val_acc: 0.2002
Epoch 2/3
8251/8251 [==============================] - 71s 9ms/step - loss: 4.4758 - acc: 0.1863 - val_loss: 4.1177 - val_acc: 0.3569
Epoch 3/3
8251/8251 [==============================] - 71s 9ms/step - loss: 3.7386 - acc: 0.2743 - val_loss: 3.4439 - val_acc: 0.4378
Finish step_1
Train on 8251 samples, validate on 1768 samples
Epoch 1/100
8251/8251 [==============================] - 76s 9ms/step - loss: 2.9186 - acc: 0.3475 - val_loss: 2.5064 - val_acc: 0.5334

Epoch 00001: val_loss improved from inf to 2.50638, saving model to bvgg16.hdf5
Epoch 2/100
8251/8251 [==============================] - 70s 9ms/step - loss: 2.3073 - acc: 0.4696 - val_loss: 2.1717 - val_acc: 0.5888

Epoch 00002: val_loss improved from 2.50638 to 2.17170, saving model to bvgg16.hdf5
Epoch 3/100
8251/8251 [==============================] - 70s 9ms/step - loss: 2.0086 - acc: 0.5355 - val_loss: 1.9604 - val_acc: 0.6222


Epoch 00067: val_loss did not improve from 0.89483
Epoch 68/100
8251/8251 [==============================] - 71s 9ms/step - loss: 0.0539 - acc: 0.9971 - val_loss: 0.8984 - val_acc: 0.7590

Epoch 00068: val_loss did not improve from 0.89483

Epoch 00068: ReduceLROnPlateau reducing learning rate to 3.999999898951501e-06.
Epoch 69/100
8251/8251 [==============================] - 71s 9ms/step - loss: 0.0536 - acc: 0.9972 - val_loss: 0.8972 - val_acc: 0.7602

Epoch 00069: val_loss did not improve from 0.89483
Epoch 70/100
8251/8251 [==============================] - 71s 9ms/step - loss: 0.0517 - acc: 0.9973 - val_loss: 0.8968 - val_acc: 0.7630

Epoch 00070: val_loss did not improve from 0.89483
Restoring model weights from the end of the best epoch
Epoch 00070: early stopping
Finish fine-tune
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history_plot(bvgg16_history)
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终于，完成了。至于效果的话，大家就看图感受吧。效果肯定是不如EfficientNet了。

当然大家可以调调图片的分辨率，学习率，batch_size等等，好好练丹吧！