tensorflow2.0 Keras VGG16 VGG19 系列 代码实现

模型介绍参看：博文

VGG16

迁移模型

先看看标准答案

import  tensorflow as tf
from    tensorflow import keras

base_model = keras.applications.VGG16(weights='imagenet')
base_model.summary()

自建模型

import  tensorflow as tf
from    tensorflow import keras
from    tensorflow.keras import layers, models, Input
from    tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout

def VGG16(nb_classes, input_shape):
    input_tensor = Input(shape=input_shape)
    # 1st block
    x = Conv2D(64, (3,3), activation='relu', padding='same',name='conv1a')(input_tensor)
    x = Conv2D(64, (3,3), activation='relu', padding='same',name='conv1b')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'pool1')(x)
    # 2nd block
    x = Conv2D(128, (3,3), activation='relu', padding='same',name='conv2a')(x)
    x = Conv2D(128, (3,3), activation='relu', padding='same',name='conv2b')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'pool2')(x)
    # 3rd block
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='conv3a')(x)
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='conv3b')(x)
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='conv3c')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'pool3')(x)
    # 4th block
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv4a')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv4b')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv4c')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'pool4')(x)
    # 5th block
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv5a')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv5b')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv5c')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'pool5')(x)
    # full connection
    x = Flatten()(x)
    x = Dense(4096, activation='relu',  name='fc6')(x)
    # x = Dropout(0.5)(x)
    x = Dense(4096, activation='relu', name='fc7')(x)
    # x = Dropout(0.5)(x)
    output_tensor = Dense(nb_classes, activation='softmax', name='fc8')(x)

    model = Model(input_tensor, output_tensor)
    return model

model=VGG16(1000, (224, 224, 3))
model.summary()

VGG19

相比VGG16在3-5block分别增加了一层卷积层

迁移模型

import  tensorflow as tf
from    tensorflow import keras

base_model = keras.applications.VGG19(weights='imagenet')
base_model.summary()

自建模型

import  tensorflow as tf
from    tensorflow import keras
from    tensorflow.keras import layers, models, Input
from    tensorflow.keras.models import Model
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout

def VGG19(nb_classes, input_shape):
    input_tensor = Input(shape=input_shape)
    # 1st block
    x = Conv2D(64, (3,3), activation='relu', padding='same',name='conv1a')(input_tensor)
    x = Conv2D(64, (3,3), activation='relu', padding='same',name='conv1b')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'pool1')(x)
    # 2nd block
    x = Conv2D(128, (3,3), activation='relu', padding='same',name='conv2a')(x)
    x = Conv2D(128, (3,3), activation='relu', padding='same',name='conv2b')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'pool2')(x)
    # 3rd block
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='conv3a')(x)
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='conv3b')(x)
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='conv3c')(x)
    x = Conv2D(256, (3,3), activation='relu', padding='same',name='conv3d')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'pool3')(x)
    # 4th block
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv4a')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv4b')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv4c')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv4d')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'pool4')(x)
    # 5th block
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv5a')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv5b')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv5c')(x)
    x = Conv2D(512, (3,3), activation='relu', padding='same',name='conv5d')(x)
    x = MaxPooling2D((2,2), strides=(2,2), name = 'pool5')(x)
    # full connection
    x = Flatten()(x)
    x = Dense(4096, activation='relu',  name='fc6')(x)
    # x = Dropout(0.5)(x)
    x = Dense(4096, activation='relu', name='fc7')(x)
    # x = Dropout(0.5)(x)
    output_tensor = Dense(nb_classes, activation='softmax', name='fc8')(x)

    model = Model(input_tensor, output_tensor)
    return model

model=VGG19(1000, (224, 224, 3))
model.summary()