动手深度学习note-4(GoogLeNet)

GoogLeNet

模型结构

Inception块

可能有作用的东西并行训练——大力出奇迹

整体架构

代码实现

1	`import tensorflow as tf` PYTHON

# Inception
class Inception(tf.keras.Model):
    def _init_(self, c1, c2, c3, c4):
        super()._init_()
        self.p1_1 = tf.keras.layers.Conv2D(c1, kernel_size=1, activation='relu')

        self.p2_1 = tf.keras.layers.Conv2D(c2[0], kernel_size=1, activation='relu')
        self.p2_2 = tf.keras.layers.Conv2D(c2[1], kernel_size=3, padding='same', activation='relu')

        self.p3_1 = tf.keras.layers.Conv2D(c3[0], kernel_size=1, activation='relu')
        self.p3_2 = tf.keras.layers.Conv2D(c3[1], kernel_size=5, padding='same', activation='relu')

        self.p4_1 = tf.keras.layers.MaxPool2D(3, 1, padding='same')
        self.p4_2 = tf.keras.layers.Conv2D(c4, 1, activation='relu')

    def call(self, x):
        p1 = self.p1_1(x)
        p2 = self.p2_2(self.p2_1(x))
        p3 = self.p3_2(self.p3_1(x))
        p4 = self.p4_2(self.p4_1(x))
        return tf.keras.layers.Concatenate()([p1, p2, p3, p4])
PYTHON

# GoogLeNet
def b1():
    return tf.keras.models.Sequential([
        tf.keras.layers.Conv2D(64, 7, strides=2, padding='same', activation='relu'),
        tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding='same')
    ])
    
def b2():
    return tf.keras.Sequential([
        tf.keras.layers.Conv2D(64, kernel_size=1, activation='relu'),
        tf.keras.layers.Conv2D(192, kernel_size=3, padding='same', activation='relu'),
        tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding='same')
    ])

def b3():
    return tf.keras.models.Sequential([
        Inception(64, (96, 128), (16, 32), 32),
        Inception(128, (128, 192), (32, 96), 64),
        tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding='same')
    ])
    
def b4():
    return tf.keras.Sequential([
        Inception(192, (96, 208), (16, 48), 64),
        Inception(160, (112, 224), (24, 64), 64),
        Inception(128, (128, 256), (24, 64), 64),
        Inception(112, (144, 288), (32, 64), 64),
        Inception(256, (160, 320), (32, 128), 128),
        tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding='same')])
    
def b5():
    return tf.keras.layers.Sequential([
        Inception(256, (160, 320), (32, 128), 128),
        Inception(384, (192, 384), (48, 128), 128),
        tf.keras.layers.GlobalAvgPool2D(),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(10)
    ])
    
def model():
    model = tf.keras.layers.Sequential([
        b1(), b2(), b3(), b4(), b5()
    ])
    return model
PYTHON