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Part1
tf.keras中提供了VGG16的网络实现, 可以直接学习
从0开始(官方示例提取):
# -*- coding: utf-8 -*-
"""
tf.keras 网络层命名
仿照示例实现一个自己的标准VGG16网络
"""
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense
input_shape = (224, 224, 3)
include_top = True
input_tensor = None
classes = 1000
# 输入层定义, 必须指定输入的shape,
# 注意: tf中数据格式为NHWC,切记!!
img_input = Input(shape=input_shape)
# Block 1
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
# Block 2
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
# Block 3
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)
x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
# Block 4
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
# Block 5
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)
x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)
x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
if include_top:
# Classification block
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1')(x)
x = Dense(4096, activation='relu', name='fc2')(x)
x = Dense(classes, activation='softmax', name='predictions')(x)
model = Model(img_input, x, name='vgg16')
model.summary()Part2
在个人使用时, 常需要在已有的网络上构建新的网络, 这里以VGG16基础网络为基础, 重新定义分类部分,给出两种基于Model类的示例
方式一:
# -*- coding: utf-8 -*-
"""
tf.keras 网络层定义
根据自己的数据集, 如何修改已有的网络,调整输出层
"""
from tensorflow.keras.models import Model
from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import Flatten, Dense
NUM_CLASSES = 10
# 自定义VGG16网络的输出层
# 自定义方式一
base_model = VGG16(include_top=False, weights=None, input_shape=(224, 224, 3))
x = base_model.output
# 分类
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1-')(x)
x = Dense(4096, activation='relu', name='fc2-')(x)
x = Dense(NUM_CLASSES, activation='softmax', name='predictions')(x)
model = Model(base_model.input, x, name="My-VGG16")
model.summary()
方式二(Model子类化):
# -*- coding: utf-8 -*-
"""
tf.keras 网络层定义
根据自己的数据集, 如何修改已有的网络,调整输出层
"""
from tensorflow.keras.models import Model
from tensorflow.keras.applications import VGG16
from tensorflow.keras.layers import Flatten, Dense
NUM_CLASSES = 10
# 自定义VGG16网络的输出层
class MyVGG16(Model):
def __init__(self, NUM_CLASSES=1000):
self.NUM_CLASSES = NUM_CLASSES
base_model = VGG16(include_top=False, weights=None, input_shape=(224, 224, 3))
x = base_model.output
# 分类
x = Flatten(name='flatten')(x)
x = Dense(4096, activation='relu', name='fc1-')(x)
x = Dense(4096, activation='relu', name='fc2-')(x)
x = Dense(NUM_CLASSES, activation='softmax', name='predictions')(x)
super(MyVGG16, self).__init__(base_model.input, x, name="My-VGG16")
# 复杂的网络必须自己控制前向传播的计算
# 简单网络不写也不会有问题
# def call(self, inputs, training=None):
# return self.output(inputs, training=training)
# model = VGG16(include_top=True, weights=None)
model = MyVGG16(NUM_CLASSES=NUM_CLASSES)
model.summary()打印输出:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 224, 224, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 224, 224, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 224, 224, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 112, 112, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 112, 112, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 112, 112, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 56, 56, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 56, 56, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 56, 56, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 28, 28, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 14, 14, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 7, 7, 512) 0
_________________________________________________________________
flatten (Flatten) (None, 25088) 0
_________________________________________________________________
fc1- (Dense) (None, 4096) 102764544
_________________________________________________________________
fc2- (Dense) (None, 4096) 16781312
_________________________________________________________________
predictions (Dense) (None, 10) 40970
=================================================================
Total params: 134,301,514
Trainable params: 134,301,514
Non-trainable params: 0
_________________________________________________________________