經(jīng)典卷積神經(jīng)網(wǎng)絡Python，TensorFlow全代碼實現(xiàn)

LeNet

class LeNet5(Model):
    def __init__(self):
        super(LeNet5, self).__init__()
        self.c1 = Conv2D(filters=6, kernel_size=(5, 5),
                         activation='sigmoid')
        self.p1 = MaxPool2D(pool_size=(2, 2), strides=2)

        self.c2 = Conv2D(filters=16, kernel_size=(5, 5),
                         activation='sigmoid')
        self.p2 = MaxPool2D(pool_size=(2, 2), strides=2)

        self.flatten = Flatten()
        self.f1 = Dense(120, activation='sigmoid')
        self.f2 = Dense(84, activation='sigmoid')
        self.f3 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.c1(x)
        x = self.p1(x)

        x = self.c2(x)
        x = self.p2(x)

        x = self.flatten(x)
        x = self.f1(x)
        x = self.f2(x)
        y = self.f3(x)
        return y

#model = LeNet5()
#model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['sparse_categorical_accuracy'])
#model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
#model.summary()

AlexNet

class AlexNet8(Model):
    def __init__(self):
        super(AlexNet8, self).__init__()
        self.c1 = Conv2D(filters=96, kernel_size=(3, 3))
        self.b1 = BatchNormalization()
        self.a1 = Activation('relu')
        self.p1 = MaxPool2D(pool_size=(3, 3), strides=2)

        self.c2 = Conv2D(filters=256, kernel_size=(3, 3))
        self.b2 = BatchNormalization()
        self.a2 = Activation('relu')
        self.p2 = MaxPool2D(pool_size=(3, 3), strides=2)

        self.c3 = Conv2D(filters=384, kernel_size=(3, 3), padding='same',
                         activation='relu')
                         
        self.c4 = Conv2D(filters=384, kernel_size=(3, 3), padding='same',
                         activation='relu')
                         
        self.c5 = Conv2D(filters=256, kernel_size=(3, 3), padding='same',
                         activation='relu')
        self.p3 = MaxPool2D(pool_size=(3, 3), strides=2)

        self.flatten = Flatten()
        self.f1 = Dense(2048, activation='relu')
        self.d1 = Dropout(0.5)
        self.f2 = Dense(2048, activation='relu')
        self.d2 = Dropout(0.5)
        self.f3 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.c1(x)
        x = self.b1(x)
        x = self.a1(x)
        x = self.p1(x)

        x = self.c2(x)
        x = self.b2(x)
        x = self.a2(x)
        x = self.p2(x)

        x = self.c3(x)

        x = self.c4(x)

        x = self.c5(x)
        x = self.p3(x)

        x = self.flatten(x)
        x = self.f1(x)
        x = self.d1(x)
        x = self.f2(x)
        x = self.d2(x)
        y = self.f3(x)
        return y


#model = AlexNet8()
#model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['sparse_categorical_accuracy'])
#model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
#model.summary()

VGGNet

class VGG16(Model):
    def __init__(self):
        super(VGG16, self).__init__()
        self.c1 = Conv2D(filters=64, kernel_size=(3, 3), padding='same')  # 卷積層1
        self.b1 = BatchNormalization()  # BN層1
        self.a1 = Activation('relu')  # 激活層1
        self.c2 = Conv2D(filters=64, kernel_size=(3, 3), padding='same', )
        self.b2 = BatchNormalization()  # BN層1
        self.a2 = Activation('relu')  # 激活層1
        self.p1 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
        self.d1 = Dropout(0.2)  # dropout層

        self.c3 = Conv2D(filters=128, kernel_size=(3, 3), padding='same')
        self.b3 = BatchNormalization()  # BN層1
        self.a3 = Activation('relu')  # 激活層1
        self.c4 = Conv2D(filters=128, kernel_size=(3, 3), padding='same')
        self.b4 = BatchNormalization()  # BN層1
        self.a4 = Activation('relu')  # 激活層1
        self.p2 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
        self.d2 = Dropout(0.2)  # dropout層

        self.c5 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')
        self.b5 = BatchNormalization()  # BN層1
        self.a5 = Activation('relu')  # 激活層1
        self.c6 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')
        self.b6 = BatchNormalization()  # BN層1
        self.a6 = Activation('relu')  # 激活層1
        self.c7 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')
        self.b7 = BatchNormalization()
        self.a7 = Activation('relu')
        self.p3 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
        self.d3 = Dropout(0.2)

        self.c8 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
        self.b8 = BatchNormalization()  # BN層1
        self.a8 = Activation('relu')  # 激活層1
        self.c9 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
        self.b9 = BatchNormalization()  # BN層1
        self.a9 = Activation('relu')  # 激活層1
        self.c10 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
        self.b10 = BatchNormalization()
        self.a10 = Activation('relu')
        self.p4 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
        self.d4 = Dropout(0.2)

        self.c11 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
        self.b11 = BatchNormalization()  # BN層1
        self.a11 = Activation('relu')  # 激活層1
        self.c12 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
        self.b12 = BatchNormalization()  # BN層1
        self.a12 = Activation('relu')  # 激活層1
        self.c13 = Conv2D(filters=512, kernel_size=(3, 3), padding='same')
        self.b13 = BatchNormalization()
        self.a13 = Activation('relu')
        self.p5 = MaxPool2D(pool_size=(2, 2), strides=2, padding='same')
        self.d5 = Dropout(0.2)

        self.flatten = Flatten()
        self.f1 = Dense(512, activation='relu')
        self.d6 = Dropout(0.2)
        self.f2 = Dense(512, activation='relu')
        self.d7 = Dropout(0.2)
        self.f3 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.c1(x)
        x = self.b1(x)
        x = self.a1(x)
        x = self.c2(x)
        x = self.b2(x)
        x = self.a2(x)
        x = self.p1(x)
        x = self.d1(x)

        x = self.c3(x)
        x = self.b3(x)
        x = self.a3(x)
        x = self.c4(x)
        x = self.b4(x)
        x = self.a4(x)
        x = self.p2(x)
        x = self.d2(x)

        x = self.c5(x)
        x = self.b5(x)
        x = self.a5(x)
        x = self.c6(x)
        x = self.b6(x)
        x = self.a6(x)
        x = self.c7(x)
        x = self.b7(x)
        x = self.a7(x)
        x = self.p3(x)
        x = self.d3(x)

        x = self.c8(x)
        x = self.b8(x)
        x = self.a8(x)
        x = self.c9(x)
        x = self.b9(x)
        x = self.a9(x)
        x = self.c10(x)
        x = self.b10(x)
        x = self.a10(x)
        x = self.p4(x)
        x = self.d4(x)

        x = self.c11(x)
        x = self.b11(x)
        x = self.a11(x)
        x = self.c12(x)
        x = self.b12(x)
        x = self.a12(x)
        x = self.c13(x)
        x = self.b13(x)
        x = self.a13(x)
        x = self.p5(x)
        x = self.d5(x)

        x = self.flatten(x)
        x = self.f1(x)
        x = self.d6(x)
        x = self.f2(x)
        x = self.d7(x)
        y = self.f3(x)
        return y


#model = VGG16()
#model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['sparse_categorical_accuracy'])
#model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
#model.summary()

InceptionNet (GoogleNet)

class ConvBNRelu(Model):
    def __init__(self, ch, kernelsz=3, strides=1, padding='same'):
        super(ConvBNRelu, self).__init__()
        self.model = tf.keras.models.Sequential([
            Conv2D(ch, kernelsz, strides=strides, padding=padding),
            BatchNormalization(),
            Activation('relu')
        ])

    def call(self, x):
        x = self.model(x, training=False) #在training=False時，BN通過整個訓練集計算均值、方差去做批歸一化，training=True時，通過當前batch的均值、方差去做批歸一化。推理時 training=False效果好
        return x


class InceptionBlk(Model):
    def __init__(self, ch, strides=1):
        super(InceptionBlk, self).__init__()
        self.ch = ch
        self.strides = strides
        self.c1 = ConvBNRelu(ch, kernelsz=1, strides=strides)
        self.c2_1 = ConvBNRelu(ch, kernelsz=1, strides=strides)
        self.c2_2 = ConvBNRelu(ch, kernelsz=3, strides=1)
        self.c3_1 = ConvBNRelu(ch, kernelsz=1, strides=strides)
        self.c3_2 = ConvBNRelu(ch, kernelsz=5, strides=1)
        self.p4_1 = MaxPool2D(3, strides=1, padding='same')
        self.c4_2 = ConvBNRelu(ch, kernelsz=1, strides=strides)

    def call(self, x):
        x1 = self.c1(x)
        x2_1 = self.c2_1(x)
        x2_2 = self.c2_2(x2_1)
        x3_1 = self.c3_1(x)
        x3_2 = self.c3_2(x3_1)
        x4_1 = self.p4_1(x)
        x4_2 = self.c4_2(x4_1)
        # concat along axis=channel
        x = tf.concat([x1, x2_2, x3_2, x4_2], axis=3)
        return x


class Inception10(Model):
    def __init__(self, num_blocks, num_classes, init_ch=16, **kwargs):
        super(Inception10, self).__init__(**kwargs)
        self.in_channels = init_ch
        self.out_channels = init_ch
        self.num_blocks = num_blocks
        self.init_ch = init_ch
        self.c1 = ConvBNRelu(init_ch)
        self.blocks = tf.keras.models.Sequential()
        for block_id in range(num_blocks):
            for layer_id in range(2):
                if layer_id == 0:
                    block = InceptionBlk(self.out_channels, strides=2)
                else:
                    block = InceptionBlk(self.out_channels, strides=1)
                self.blocks.add(block)
            # enlarger out_channels per block
            self.out_channels *= 2
        self.p1 = GlobalAveragePooling2D()
        self.f1 = Dense(num_classes, activation='softmax')

    def call(self, x):
        x = self.c1(x)
        x = self.blocks(x)
        x = self.p1(x)
        y = self.f1(x)
        return y


#model = Inception10(num_blocks=2, num_classes=10)
#model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['sparse_categorical_accuracy'])
#model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
#model.summary()

ResNet

class ResnetBlock(Model):

    def __init__(self, filters, strides=1, residual_path=False):
        super(ResnetBlock, self).__init__()
        self.filters = filters
        self.strides = strides
        self.residual_path = residual_path

        self.c1 = Conv2D(filters, (3, 3), strides=strides, padding='same', use_bias=False)
        self.b1 = BatchNormalization()
        self.a1 = Activation('relu')

        self.c2 = Conv2D(filters, (3, 3), strides=1, padding='same', use_bias=False)
        self.b2 = BatchNormalization()

        # residual_path為True時，對輸入進行下采樣，即用1x1的卷積核做卷積操作，保證x能和F(x)維度相同，順利相加
        if residual_path:
            self.down_c1 = Conv2D(filters, (1, 1), strides=strides, padding='same', use_bias=False)
            self.down_b1 = BatchNormalization()
        
        self.a2 = Activation('relu')

    def call(self, inputs):
        residual = inputs  # residual等于輸入值本身，即residual=x
        # 將輸入通過卷積、BN層、激活層，計算F(x)
        x = self.c1(inputs)
        x = self.b1(x)
        x = self.a1(x)

        x = self.c2(x)
        y = self.b2(x)

        if self.residual_path:
            residual = self.down_c1(inputs)
            residual = self.down_b1(residual)

        out = self.a2(y + residual)  # 最后輸出的是兩部分的和，即F(x)+x或F(x)+Wx,再過激活函數(shù)
        return out


class ResNet18(Model):

    def __init__(self, block_list, initial_filters=64):  # block_list表示每個block有幾個卷積層
        super(ResNet18, self).__init__()
        self.num_blocks = len(block_list)  # 共有幾個block
        self.block_list = block_list
        self.out_filters = initial_filters
        self.c1 = Conv2D(self.out_filters, (3, 3), strides=1, padding='same', use_bias=False)
        self.b1 = BatchNormalization()
        self.a1 = Activation('relu')
        self.blocks = tf.keras.models.Sequential()
        # 構(gòu)建ResNet網(wǎng)絡結(jié)構(gòu)
        for block_id in range(len(block_list)):  # 第幾個resnet block
            for layer_id in range(block_list[block_id]):  # 第幾個卷積層

                if block_id != 0 and layer_id == 0:  # 對除第一個block以外的每個block的輸入進行下采樣
                    block = ResnetBlock(self.out_filters, strides=2, residual_path=True)
                else:
                    block = ResnetBlock(self.out_filters, residual_path=False)
                self.blocks.add(block)  # 將構(gòu)建好的block加入resnet
            self.out_filters *= 2  # 下一個block的卷積核數(shù)是上一個block的2倍
        self.p1 = tf.keras.layers.GlobalAveragePooling2D()
        self.f1 = tf.keras.layers.Dense(10, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2())

    def call(self, inputs):
        x = self.c1(inputs)
        x = self.b1(x)
        x = self.a1(x)
        x = self.blocks(x)
        x = self.p1(x)
        y = self.f1(x)
        return y


#model = ResNet18([2, 2, 2, 2])
#model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['sparse_categorical_accuracy'])
#model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
#model.summary()

總結(jié)

LeNet (1998)
卷積網(wǎng)絡開篇之作，通過空間卷積核共享，減少了待訓練的參數(shù)。

AlexNet (2012)
使用了relu激活函數(shù)，提升了訓練速度；使用了Dropout，緩解了過擬合。

VGGNet (2014)
使用小尺寸卷積核減少待訓練參數(shù)和計算量，它的網(wǎng)絡結(jié)構(gòu)非常規(guī)整，適合硬件并行加速。

InceptionNet (2014)
在同一層中使用了不同尺寸的卷積核，提升了模型的感知力；使用了批標準化（batch normalization），緩解了梯度消失。

ResNet (2015)
通過層間殘差跳連，引入了前方信息，緩解了模型退化，使神經(jīng)網(wǎng)絡層數(shù)加深成為可能。