文章目录

  • 使用Keras预训练模型完成猫狗识别
  • 1. 导入Keras库
  • 2. 导入数据集
  • 3. Keras内置经典网络实现
  • 4. 训练模型
  • 5. 分析模型
  • 附：系列文章


使用Keras预训练模型完成猫狗识别

VGG16是一种深度卷积神经网络，由牛津大学计算机视觉研究小组在2014年提出。它是ImageNet图像识别竞赛的冠军，拥有较好的图像识别和分类效果。VGG16架构非常简单，特征提取部分由13个卷积层和5个池化层组成，分类器部分有3个全连接层。VGG16中的卷积层均为3×3的卷积核，池化层为2×2的最大池化，卷积核数量逐层增加，以提取越来越复杂的特征。

VGG16可以分为两个部分：特征提取和分类。特征提取部分包括13个卷积层和5个池化层。其中前12个卷积层都是由两个卷积层和一个池化层组成，每个卷积层都有64个卷积核，激活函数为ReLU。这样的设计使得VGG16的特征提取能力更加强大，可以提取较为复杂的特征。第13个卷积层有512个卷积核，激活函数也为ReLU，该层的作用是将图像特征进行更深入的抽象。在特征提取部分之后，VGG16还包括一个分类器部分，即3个全连接层，其中第一个全连接层有4096个节点，第二个全连接层也有4096个节点。最后一个全连接层有1000个节点，对应ImageNet的1000个类别。

VGG16的优点是具有良好的表现，但是它的模型参数较多，需要较大的存储空间和计算资源。针对这个问题，VGG16的作者提出了VGG19模型，它在VGG16的基础上增加了几个卷积层和池化层，但是模型参数更多，计算资源更加消耗。

总的来说，VGG16是一个简单而有效的深度卷积神经网络，特征提取能力强，可以有效地提取图像的特征信息，从而得到较好的图像识别和分类效果。

1. 导入Keras库

from keras import layers
import tensorflow as tf
import keras
import numpy as np
import os
import shutil
import warnings
warnings.filterwarnings('ignore')
Using TensorFlow backend.

2. 导入数据集

base_dir = './dataset/cat_dog'
train_dir = base_dir + '/train'
train_dog_dir = train_dir + '/dog'
train_cat_dir = train_dir + '/cat'
test_dir = base_dir + '/test'
test_dog_dir = test_dir + '/dog'
test_cat_dir = test_dir + '/cat'
dc_dir = './dataset/dc/train'
if not os.path.exists(base_dir):

    os.mkdir(base_dir)

    os.mkdir(train_dir)
    os.mkdir(train_dog_dir)
    os.mkdir(train_cat_dir)
    os.mkdir(test_dir)
    os.mkdir(test_dog_dir)
    os.mkdir(test_cat_dir)

    fnames = ['cat.{}.jpg'.format(i) for i in range(1000)] 
    for fname in fnames:
        src = os.path.join(dc_dir, fname)
        dst = os.path.join(train_cat_dir, fname)
        shutil.copyfile(src, dst)

    fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)] 
    for fname in fnames:
        src = os.path.join(dc_dir, fname)
        dst = os.path.join(test_cat_dir, fname)
        shutil.copyfile(src, dst)

    fnames = ['dog.{}.jpg'.format(i) for i in range(1000)] 
    for fname in fnames:
        src = os.path.join(dc_dir, fname)
        dst = os.path.join(train_dog_dir, fname)
        shutil.copyfile(src, dst)

    fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)] 
    for fname in fnames:
        src = os.path.join(dc_dir, fname)
        dst = os.path.join(test_dog_dir, fname)
        shutil.copyfile(src, dst)
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255)                        
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(200, 200),
    batch_size=20,
    class_mode='binary'
)

test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(200, 200),
    batch_size=20,
    class_mode='binary'
)
Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.

3. Keras内置经典网络实现

covn_base = keras.applications.VGG16(weights=None, include_top=False)
WARNING:tensorflow:From /home/nlp/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.
covn_base.summary()
Model: "vgg16"
   _________________________________________________________________
   
   Layer (type)                 Output Shape              Param #   
   _________________________________________________________________
   input_1 (InputLayer)         (None, None, None, 3)     0         
   _________________________________________________________________
   block1_conv1 (Conv2D)        (None, None, None, 64)    1792      
   _________________________________________________________________
   block1_conv2 (Conv2D)        (None, None, None, 64)    36928     
   _________________________________________________________________
   block1_pool (MaxPooling2D)   (None, None, None, 64)    0         
   _________________________________________________________________
   block2_conv1 (Conv2D)        (None, None, None, 128)   73856     
   _________________________________________________________________
   block2_conv2 (Conv2D)        (None, None, None, 128)   147584    
   _________________________________________________________________
   block2_pool (MaxPooling2D)   (None, None, None, 128)   0         
   _________________________________________________________________
   block3_conv1 (Conv2D)        (None, None, None, 256)   295168    
   _________________________________________________________________
   block3_conv2 (Conv2D)        (None, None, None, 256)   590080    
   _________________________________________________________________
   block3_conv3 (Conv2D)        (None, None, None, 256)   590080    
   _________________________________________________________________
   block3_pool (MaxPooling2D)   (None, None, None, 256)   0         
   _________________________________________________________________
   block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   
   _________________________________________________________________
   block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   
   _________________________________________________________________
   block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   
   _________________________________________________________________
   block4_pool (MaxPooling2D)   (None, None, None, 512)   0         
   _________________________________________________________________
   block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   
   _________________________________________________________________
   block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   
   _________________________________________________________________
   block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   
   _________________________________________________________________
   block5_pool (MaxPooling2D)   (None, None, None, 512)   0         
   _________________________________________________________________
   Total params: 14,714,688
   Trainable params: 14,714,688
   Non-trainable params: 0
model = keras.Sequential()
model.add(covn_base)
model.add(layers.GlobalAveragePooling2D())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.summary()
Model: "sequential_1"
   _________________________________________________________________
   Layer (type)                 Output Shape              Param #   
   _________________________________________________________________
   vgg16 (Model)                (None, None, None, 512)   14714688  
   _________________________________________________________________
   global_average_pooling2d_1 ( (None, 512)               0         
   _________________________________________________________________
   dense_1 (Dense)              (None, 512)               262656    
   _________________________________________________________________
   dense_2 (Dense)              (None, 1)                 513       
   _________________________________________________________________
   Total params: 14,977,857
   Trainable params: 14,977,857
   Non-trainable params: 0
covn_base.trainable = False #设置权重不可变，卷积基不可变
model.summary()
Model: "sequential_1"
   _________________________________________________________________
   Layer (type)                 Output Shape              Param #   
   _________________________________________________________________
   vgg16 (Model)                (None, None, None, 512)   14714688  
   _________________________________________________________________
   global_average_pooling2d_1 ( (None, 512)               0         
   _________________________________________________________________
   dense_1 (Dense)              (None, 512)               262656    
   _________________________________________________________________
   dense_2 (Dense)              (None, 1)                 513       
   _________________________________________________________________
   Total params: 14,977,857
   Trainable params: 263,169
   Non-trainable params: 14,714,688
model.compile(optimizer=keras.optimizers.Adam(lr=0.001),
              loss='binary_crossentropy',
              metrics=['acc'])
WARNING:tensorflow:From /home/nlp/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where

4. 训练模型

history = model.fit_generator(
    train_generator,
    steps_per_epoch=10,
    epochs=15,
    validation_data=test_generator,
    validation_steps=50)
WARNING:tensorflow:From /home/nlp/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.

Epoch 1/15
 9/10 [==========================>...] - ETA: 5s - loss: 0.6912 - acc: 0.5500 
……

5. 分析模型

import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(history.epoch, history.history['loss'], 'r', label='loss')
plt.plot(history.epoch, history.history['val_loss'], 'b--', label='val_loss')
plt.plot(history.epoch, history.history['acc'], 'r')
plt.plot(history.epoch, history.history['val_acc'], 'b--')
plt.legend()

附：系列文章

序号

文章目录

直达链接

1

波士顿房价预测

2

鸢尾花数据集分析

3

特征处理

4

交叉验证

5

构造神经网络示例

6

使用TensorFlow完成线性回归

7

使用TensorFlow完成逻辑回归

8

TensorBoard案例

9

使用Keras完成线性回归

10

使用Keras完成逻辑回归

11

使用Keras预训练模型完成猫狗识别

12

使用PyTorch训练模型

13

使用Dropout抑制过拟合

14

使用CNN完成MNIST手写体识别(TensorFlow)

15

使用CNN完成MNIST手写体识别(Keras)

16

使用CNN完成MNIST手写体识别(PyTorch)

17

使用GAN生成手写数字样本

18

自然语言处理