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  • 基于 Tensorflow 实现 Mobilenet V1 并基于 CFAR-10 数据训练

    基于 Tensorflow 实现 Mobilenet V1 并基于 CFAR-10 数据训练

    论文:MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

    image-20200323144654547

    深度可分离卷积

    将标准卷积分解为深度卷积和逐点卷积

    传统卷积计算量

    D_k^2MD_f^2N
    

    深度可分离卷积计算量

    D_k^2MD_f^2+D_f^2MN
    

    image-20200323144610736

    • 轻量级卷积神经网络
    • 更少的参数、更小的计算量却拥有不俗的性能
    • 空间可分离卷积
    模型搭建
    def conv_block(
            inputs,
            filters,
            kernel_size=(3, 3),
            strides=(1, 1)
    ):
        x = tf.keras.layers.Conv2D(filters, kernel_size=kernel_size, strides=strides, padding='same', use_bias=False)(
            inputs)
        tf.keras.layers.BatchNormalization()(x)
        return tf.keras.layers.ReLU(6.0)(x)
    
    
    def depthwise_conv_block(
            inputs,
            pointwise_conv_filters,
            strides=(1, 1)
    ):
        x = tf.keras.layers.DepthwiseConv2D((3, 3), padding='same', strides=strides, use_bias=False)(inputs)
        x = tf.keras.layers.BatchNormalization()(x)
        x = tf.keras.layers.ReLU(6.0)(x)
    
        x = tf.keras.layers.Conv2D(pointwise_conv_filters, kernel_size=(1, 1), padding='same', use_bias=False)(x)
        x = tf.keras.layers.BatchNormalization()(x)
    
        return tf.keras.layers.ReLU(6.0)(x)
    
    
    def mobilenet_v1(
            inputs,
            classes
    ):
        x = conv_block(inputs, 32, strides=(2, 2))
        x = depthwise_conv_block(x, 64)
        x = depthwise_conv_block(x, 128, strides=(2, 2))
        x = depthwise_conv_block(x, 128)
        x = depthwise_conv_block(x, 256, strides=(2, 2))
        x = depthwise_conv_block(x, 256)
        x = depthwise_conv_block(x, 512, strides=(2, 2))
        x = depthwise_conv_block(x, 512)
        x = depthwise_conv_block(x, 512)
        x = depthwise_conv_block(x, 512)
        x = depthwise_conv_block(x, 512)
        x = depthwise_conv_block(x, 512)
        x = depthwise_conv_block(x, 1024, strides=(2, 2))
        x = depthwise_conv_block(x, 1024)
    
        x = tf.keras.layers.GlobalAveragePooling2D()(x)
    
        x = tf.keras.layers.Dense(classes, activation='softmax')(x)
    
        return x
    
    
    inputs = tf.keras.Input(shape=(32, 32, 3))
    model = tf.keras.Model(inputs=inputs, outputs=mobilenet_v1(inputs, 10))
    model.summary()
    
    参数信息
    Model: "model_1"
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    input_2 (InputLayer)         [(None, 32, 32, 3)]       0         
    _________________________________________________________________
    conv2d_14 (Conv2D)           (None, 16, 16, 32)        864       
    _________________________________________________________________
    re_lu_27 (ReLU)              (None, 16, 16, 32)        0         
    _________________________________________________________________
    depthwise_conv2d_13 (Depthwi (None, 16, 16, 32)        288       
    _________________________________________________________________
    batch_normalization_28 (Batc (None, 16, 16, 32)        128       
    _________________________________________________________________
    re_lu_28 (ReLU)              (None, 16, 16, 32)        0         
    _________________________________________________________________
    conv2d_15 (Conv2D)           (None, 16, 16, 64)        2048      
    _________________________________________________________________
    batch_normalization_29 (Batc (None, 16, 16, 64)        256       
    _________________________________________________________________
    re_lu_29 (ReLU)              (None, 16, 16, 64)        0         
    _________________________________________________________________
    depthwise_conv2d_14 (Depthwi (None, 8, 8, 64)          576       
    _________________________________________________________________
    batch_normalization_30 (Batc (None, 8, 8, 64)          256       
    _________________________________________________________________
    re_lu_30 (ReLU)              (None, 8, 8, 64)          0         
    _________________________________________________________________
    conv2d_16 (Conv2D)           (None, 8, 8, 128)         8192      
    _________________________________________________________________
    batch_normalization_31 (Batc (None, 8, 8, 128)         512       
    _________________________________________________________________
    re_lu_31 (ReLU)              (None, 8, 8, 128)         0         
    _________________________________________________________________
    depthwise_conv2d_15 (Depthwi (None, 8, 8, 128)         1152      
    _________________________________________________________________
    batch_normalization_32 (Batc (None, 8, 8, 128)         512       
    _________________________________________________________________
    re_lu_32 (ReLU)              (None, 8, 8, 128)         0         
    _________________________________________________________________
    conv2d_17 (Conv2D)           (None, 8, 8, 128)         16384     
    _________________________________________________________________
    batch_normalization_33 (Batc (None, 8, 8, 128)         512       
    _________________________________________________________________
    re_lu_33 (ReLU)              (None, 8, 8, 128)         0         
    _________________________________________________________________
    depthwise_conv2d_16 (Depthwi (None, 4, 4, 128)         1152      
    _________________________________________________________________
    batch_normalization_34 (Batc (None, 4, 4, 128)         512       
    _________________________________________________________________
    re_lu_34 (ReLU)              (None, 4, 4, 128)         0         
    _________________________________________________________________
    conv2d_18 (Conv2D)           (None, 4, 4, 256)         32768     
    _________________________________________________________________
    batch_normalization_35 (Batc (None, 4, 4, 256)         1024      
    _________________________________________________________________
    re_lu_35 (ReLU)              (None, 4, 4, 256)         0         
    _________________________________________________________________
    depthwise_conv2d_17 (Depthwi (None, 4, 4, 256)         2304      
    _________________________________________________________________
    batch_normalization_36 (Batc (None, 4, 4, 256)         1024      
    _________________________________________________________________
    re_lu_36 (ReLU)              (None, 4, 4, 256)         0         
    _________________________________________________________________
    conv2d_19 (Conv2D)           (None, 4, 4, 256)         65536     
    _________________________________________________________________
    batch_normalization_37 (Batc (None, 4, 4, 256)         1024      
    _________________________________________________________________
    re_lu_37 (ReLU)              (None, 4, 4, 256)         0         
    _________________________________________________________________
    depthwise_conv2d_18 (Depthwi (None, 2, 2, 256)         2304      
    _________________________________________________________________
    batch_normalization_38 (Batc (None, 2, 2, 256)         1024      
    _________________________________________________________________
    re_lu_38 (ReLU)              (None, 2, 2, 256)         0         
    _________________________________________________________________
    conv2d_20 (Conv2D)           (None, 2, 2, 512)         131072    
    _________________________________________________________________
    batch_normalization_39 (Batc (None, 2, 2, 512)         2048      
    _________________________________________________________________
    re_lu_39 (ReLU)              (None, 2, 2, 512)         0         
    _________________________________________________________________
    depthwise_conv2d_19 (Depthwi (None, 2, 2, 512)         4608      
    _________________________________________________________________
    batch_normalization_40 (Batc (None, 2, 2, 512)         2048      
    _________________________________________________________________
    re_lu_40 (ReLU)              (None, 2, 2, 512)         0         
    _________________________________________________________________
    conv2d_21 (Conv2D)           (None, 2, 2, 512)         262144    
    _________________________________________________________________
    batch_normalization_41 (Batc (None, 2, 2, 512)         2048      
    _________________________________________________________________
    re_lu_41 (ReLU)              (None, 2, 2, 512)         0         
    _________________________________________________________________
    depthwise_conv2d_20 (Depthwi (None, 2, 2, 512)         4608      
    _________________________________________________________________
    batch_normalization_42 (Batc (None, 2, 2, 512)         2048      
    _________________________________________________________________
    re_lu_42 (ReLU)              (None, 2, 2, 512)         0         
    _________________________________________________________________
    conv2d_22 (Conv2D)           (None, 2, 2, 512)         262144    
    _________________________________________________________________
    batch_normalization_43 (Batc (None, 2, 2, 512)         2048      
    _________________________________________________________________
    re_lu_43 (ReLU)              (None, 2, 2, 512)         0         
    _________________________________________________________________
    depthwise_conv2d_21 (Depthwi (None, 2, 2, 512)         4608      
    _________________________________________________________________
    batch_normalization_44 (Batc (None, 2, 2, 512)         2048      
    _________________________________________________________________
    re_lu_44 (ReLU)              (None, 2, 2, 512)         0         
    _________________________________________________________________
    conv2d_23 (Conv2D)           (None, 2, 2, 512)         262144    
    _________________________________________________________________
    batch_normalization_45 (Batc (None, 2, 2, 512)         2048      
    _________________________________________________________________
    re_lu_45 (ReLU)              (None, 2, 2, 512)         0         
    _________________________________________________________________
    depthwise_conv2d_22 (Depthwi (None, 2, 2, 512)         4608      
    _________________________________________________________________
    batch_normalization_46 (Batc (None, 2, 2, 512)         2048      
    _________________________________________________________________
    re_lu_46 (ReLU)              (None, 2, 2, 512)         0         
    _________________________________________________________________
    conv2d_24 (Conv2D)           (None, 2, 2, 512)         262144    
    _________________________________________________________________
    batch_normalization_47 (Batc (None, 2, 2, 512)         2048      
    _________________________________________________________________
    re_lu_47 (ReLU)              (None, 2, 2, 512)         0         
    _________________________________________________________________
    depthwise_conv2d_23 (Depthwi (None, 2, 2, 512)         4608      
    _________________________________________________________________
    batch_normalization_48 (Batc (None, 2, 2, 512)         2048      
    _________________________________________________________________
    re_lu_48 (ReLU)              (None, 2, 2, 512)         0         
    _________________________________________________________________
    conv2d_25 (Conv2D)           (None, 2, 2, 512)         262144    
    _________________________________________________________________
    batch_normalization_49 (Batc (None, 2, 2, 512)         2048      
    _________________________________________________________________
    re_lu_49 (ReLU)              (None, 2, 2, 512)         0         
    _________________________________________________________________
    depthwise_conv2d_24 (Depthwi (None, 1, 1, 512)         4608      
    _________________________________________________________________
    batch_normalization_50 (Batc (None, 1, 1, 512)         2048      
    _________________________________________________________________
    re_lu_50 (ReLU)              (None, 1, 1, 512)         0         
    _________________________________________________________________
    conv2d_26 (Conv2D)           (None, 1, 1, 1024)        524288    
    _________________________________________________________________
    batch_normalization_51 (Batc (None, 1, 1, 1024)        4096      
    _________________________________________________________________
    re_lu_51 (ReLU)              (None, 1, 1, 1024)        0         
    _________________________________________________________________
    depthwise_conv2d_25 (Depthwi (None, 1, 1, 1024)        9216      
    _________________________________________________________________
    batch_normalization_52 (Batc (None, 1, 1, 1024)        4096      
    _________________________________________________________________
    re_lu_52 (ReLU)              (None, 1, 1, 1024)        0         
    _________________________________________________________________
    conv2d_27 (Conv2D)           (None, 1, 1, 1024)        1048576   
    _________________________________________________________________
    batch_normalization_53 (Batc (None, 1, 1, 1024)        4096      
    _________________________________________________________________
    re_lu_53 (ReLU)              (None, 1, 1, 1024)        0         
    _________________________________________________________________
    global_average_pooling2d_1 ( (None, 1024)              0         
    _________________________________________________________________
    dense_1 (Dense)              (None, 10)                10250     
    =================================================================
    Total params: 3,238,986
    Trainable params: 3,217,162
    Non-trainable params: 21,824
    _________________________________________________________________
    
    数据准备
    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
    x_train = x_train / 255.0
    y_train = tf.keras.utils.to_categorical(y_train, 10)
    
    模型训练
    model.compile(
        loss='categorical_crossentropy',
        optimizer='adam',
        metrics=['categorical_accuracy', 'Recall', 'AUC']
    )
    
    model.fit(x_train, y_train, batch_size=10, epochs=10)
    model.save('mobilenet_v1_cifar10.h5')
    
    模型应用
    import cv2
    
    img = cv2.imread('cat.png', 1) / 225.0
    import numpy as np
    img = np.expand_dims(img, 0)
    pred = model.predict(img)
    
    print(pred)
    

    总结:

    1. Mobilenet V1 网络结构介绍及实现
    2. 模型训练及预测

    作业

    根据所讲内容独立完成本章提及的四个目标

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  • 原文地址:https://www.cnblogs.com/TuringEmmy/p/12555198.html
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