Keras應該是最簡單的一種深度學習框架了，入門非常的簡單.

簡單記錄一下keras實現多種分類網路：如AlexNet、Vgg、ResNet

採用kaggle貓狗大戰的資料作為資料集.

由於AlexNet採用的是LRN標準化,Keras沒有內建函式實現,這裡用batchNormalization代替

收件建立一個model.py的檔案,裡面存放著alexnet,vgg兩種模型,直接匯入就可以了

#coding=utf-8
from keras.models import Sequential
from keras.layers import Dense,Dropout,Activation,Flatten
from keras.layers import Conv2D,MaxPooling2D,ZeroPadding2D,BatchNormalization
from keras.layers import *
from keras.layers.advanced_activations import LeakyReLU,PReLU
from keras.models import Model
 
def keras_batchnormalization_relu(layer):
 BN = BatchNormalization()(layer)
 ac = PReLU()(BN)
 return ac
 
def AlexNet(resize=227,classes=2):
 model = Sequential()
 # 第一段
 model.add(Conv2D(filters=96,kernel_size=(11,11),strides=(4,4),padding='valid',input_shape=(resize,resize,3),activation='relu'))
 model.add(BatchNormalization())
 model.add(MaxPooling2D(pool_size=(3,strides=(2,2),padding='valid'))
 # 第二段
 model.add(Conv2D(filters=256,kernel_size=(5,5),strides=(1,1),padding='same',padding='valid'))
 # 第三段
 model.add(Conv2D(filters=384,kernel_size=(3,activation='relu'))
 model.add(Conv2D(filters=384,activation='relu'))
 model.add(Conv2D(filters=256,activation='relu'))
 model.add(MaxPooling2D(pool_size=(3,padding='valid'))
 # 第四段
 model.add(Flatten())
 model.add(Dense(4096,activation='relu'))
 model.add(Dropout(0.5))
 
 model.add(Dense(4096,activation='relu'))
 model.add(Dropout(0.5))
 
 model.add(Dense(1000,activation='relu'))
 model.add(Dropout(0.5))
 
 # Output Layer
 model.add(Dense(classes,activation='softmax'))
 # model.add(Activation('softmax'))
 
 return model
 
def AlexNet2(inputs,classes=2,prob=0.5):
 '''
 自己寫的函式,嘗試keras另外一種寫法
 :param inputs: 輸入
 :param classes: 類別的個數
 :param prob: dropout的概率
 :return: 模型
 '''
 # Conv2D(32,(3,dilation_rate=(2,padding='same')(inputs)
 print "input shape:",inputs.shape
 
 conv1 = Conv2D(filters=96,padding='valid')(inputs)
 conv1 = keras_batchnormalization_relu(conv1)
 print "conv1 shape:",conv1.shape
 pool1 = MaxPool2D(pool_size=(3,2))(conv1)
 print "pool1 shape:",pool1.shape
 
 conv2 = Conv2D(filters=256,padding='same')(pool1)
 conv2 = keras_batchnormalization_relu(conv2)
 print "conv2 shape:",conv2.shape
 pool2 = MaxPool2D(pool_size=(3,2))(conv2)
 print "pool2 shape:",pool2.shape
 
 conv3 = Conv2D(filters=384,padding='same')(pool2)
 conv3 = PReLU()(conv3)
 print "conv3 shape:",conv3.shape
 
 conv4 = Conv2D(filters=384,padding='same')(conv3)
 conv4 = PReLU()(conv4)
 print "conv4 shape:",conv4
 
 conv5 = Conv2D(filters=256,padding='same')(conv4)
 conv5 = PReLU()(conv5)
 print "conv5 shape:",conv5
 
 pool3 = MaxPool2D(pool_size=(3,2))(conv5)
 print "pool3 shape:",pool3.shape
 
 dense1 = Flatten()(pool3)
 dense1 = Dense(4096,activation='relu')(dense1)
 print "dense2 shape:",dense1
 dense1 = Dropout(prob)(dense1)
 # print "dense1 shape:",dense1
 
 dense2 = Dense(4096,dense2
 dense2 = Dropout(prob)(dense2)
 # print "dense2 shape:",dense2
 
 predict= Dense(classes,activation='softmax')(dense2)
 
 model = Model(inputs=inputs,outputs=predict)
 return model
 
def vgg13(resize=224,prob=0.5):
 model = Sequential()
 model.add(Conv2D(64,activation='relu',kernel_initializer='uniform'))
 model.add(Conv2D(64,kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2,2)))
 model.add(Conv2D(128,kernel_initializer='uniform'))
 model.add(Conv2D(128,2)))
 model.add(Conv2D(256,kernel_initializer='uniform'))
 model.add(Conv2D(256,2)))
 model.add(Conv2D(512,kernel_initializer='uniform'))
 model.add(Conv2D(512,2)))
 model.add(Flatten())
 model.add(Dense(4096,activation='relu'))
 model.add(Dropout(prob))
 model.add(Dense(4096,activation='relu'))
 model.add(Dropout(prob))
 model.add(Dense(classes,activation='softmax'))
 return model
 
def vgg16(resize=224,activation='softmax'))
 return model

然後建立一個train.py檔案，用於讀取資料和訓練資料的.

#coding=utf-8
import keras
import cv2
import os
import numpy as np
import model
import modelResNet
import tensorflow as tf
from keras.layers import Input,Dense
from keras.preprocessing.image import ImageDataGenerator
 
resize = 224
batch_size = 128
path = "/home/hjxu/PycharmProjects/01_cats_vs_dogs/data"
 
trainDirectory = '/home/hjxu/PycharmProjects/01_cats_vs_dogs/data/train/'
def load_data():
 imgs = os.listdir(path + "/train/")
 num = len(imgs)
 train_data = np.empty((5000,dtype="int32")
 train_label = np.empty((5000,),dtype="int32")
 test_data = np.empty((5000,dtype="int32")
 test_label = np.empty((5000,dtype="int32")
 for i in range(5000):
  if i % 2:
   train_data[i] = cv2.resize(cv2.imread(path + '/train/' + 'dog.' + str(i) + '.jpg'),(resize,resize))
   train_label[i] = 1
  else:
   train_data[i] = cv2.resize(cv2.imread(path + '/train/' + 'cat.' + str(i) + '.jpg'),resize))
   train_label[i] = 0
 for i in range(5000,10000):
  if i % 2:
   test_data[i-5000] = cv2.resize(cv2.imread(path + '/train/' + 'dog.' + str(i) + '.jpg'),resize))
   test_label[i-5000] = 1
  else:
   test_data[i-5000] = cv2.resize(cv2.imread(path + '/train/' + 'cat.' + str(i) + '.jpg'),resize))
   test_label[i-5000] = 0
 return train_data,train_label,test_data,test_label
 
def main():
 
 train_data,test_label = load_data()
 train_data,test_data = train_data.astype('float32'),test_data.astype('float32')
 train_data,test_data = train_data/255,test_data/255
 
 train_label = keras.utils.to_categorical(train_label,2)
 '''
  #one_hot轉碼,如果使用 categorical_crossentropy,就需要用到to_categorical函式完成轉碼
 '''
 test_label = keras.utils.to_categorical(test_label,2)
 
 inputs = Input(shape=(224,224,3))
 
 modelAlex = model.AlexNet2(inputs,classes=2)
 '''
 匯入模型
 '''
 modelAlex.compile(loss='categorical_crossentropy',optimizer='sgd',metrics=['accuracy'])
 '''
 def compile(self,optimizer,loss,metrics=None,loss_weights=None,sample_weight_mode=None,**kwargs):
  optimizer:優化器,為預定義優化器名或優化器物件,參考優化器
  loss: 損失函式,為預定義損失函式名或者一個目標函式
  metrics:列表,包含評估模型在訓練和測試時的效能指標,典型用法是 metrics=['accuracy']
  sample_weight_mode:如果需要按時間步為樣本賦值,需要將改制設定為"temoral"
  如果想用自定義的效能評估函式:如下
   def mean_pred(y_true,y_pred):
   return k.mean(y_pred)
  model.compile(loss = 'binary_crossentropy',metrics=['accuracy',mean_pred],...)
  損失函式同理,再看 keras內建支援的損失函式有
   mean_squared_error
  mean_absolute_error
  mean_absolute_percentage_error
  mean_squared_logarithmic_error
  squared_hinge
  hinge
  categorical_hinge
  logcosh
  categorical_crossentropy
  sparse_categorical_crossentropy
  binary_crossentropy
  kullback_leibler_divergence
  poisson
  cosine_proximity
 '''
 modelAlex.summary()
 '''
 # 列印模型資訊
 '''
 modelAlex.fit(train_data,batch_size=batch_size,epochs=50,validation_split=0.2,shuffle=True)
 '''
 def fit(self,x=None,# x:輸入資料
   y=None,# y:標籤 Numpy array
   batch_size=32,# batch_size:訓練時,一個batch的樣本會被計算一次梯度下降
   epochs=1,# epochs: 訓練的輪數,每個epoch會把訓練集迴圈一遍
   verbose=1,# 日誌顯示:0表示不在標準輸入輸出流輸出,1表示輸出進度條,2表示每個epoch輸出
   callbacks=None,# 回撥函式
   validation_split=0.,# 0-1的浮點數,用來指定訓練集一定比例作為驗證集,驗證集不參與訓練
   validation_data=None,# (x,y)的tuple,是指定的驗證集
   shuffle=True,# 如果是"batch",則是用來處理HDF5資料的特殊情況,將在batch內部將資料打亂
   class_weight=None,# 字典,將不同的類別對映為不同的權值,用來在訓練過程中調整損失函式的
   sample_weight=None,# 權值的numpy array,用於訓練的時候調整損失函式
   initial_epoch=0,# 該引數用於從指定的epoch開始訓練,繼續之前的訓練
   **kwargs):
 返回:返回一個History的物件,其中History.history損失函式和其他指標的數值隨epoch變化的情況
 '''
 scores = modelAlex.evaluate(train_data,verbose=1)
 print(scores)
 
 scores = modelAlex.evaluate(test_data,test_label,verbose=1)
 print(scores)
 modelAlex.save('my_model_weights2.h5')
 
def main2():
 train_datagen = ImageDataGenerator(rescale=1. / 255,shear_range=0.2,zoom_range=0.2,horizontal_flip=True)
 test_datagen = ImageDataGenerator(rescale=1. / 255)
 train_generator = train_datagen.flow_from_directory(trainDirectory,target_size=(224,224),batch_size=32,class_mode='binary')
 
 validation_generator = test_datagen.flow_from_directory(trainDirectory,class_mode='binary')
 
 inputs = Input(shape=(224,3))
 # modelAlex = model.AlexNet2(inputs,classes=2)
 modelAlex = model.vgg13(resize=224,prob=0.5)
 # modelAlex = modelResNet.ResNet50(shape=224,classes=2)
 modelAlex.compile(loss='sparse_categorical_crossentropy',metrics=['accuracy'])
 modelAlex.summary()
 
 modelAlex.fit_generator(train_generator,steps_per_epoch=1000,epochs=60,validation_data=validation_generator,validation_steps=200)
 
 modelAlex.save('model32.hdf5')
 #
if __name__ == "__main__":
 '''
 如果資料是按照貓狗大戰的資料,都在同一個資料夾下,使用main()函式
 如果資料按照貓和狗分成兩類,則使用main2()函式
 '''
 main2()

得到模型後該怎麼測試一張影象呢？

建立一個testOneImg.py指令碼，程式碼如下

#coding=utf-8
from keras.preprocessing.image import load_img#load_image作用是載入圖片
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions
import numpy as np
import cv2
import model
from keras.models import Sequential
 
pats = '/home/hjxu/tf_study/catVsDogsWithKeras/my_model_weights.h5'
modelAlex = model.AlexNet(resize=224,classes=2)
# AlexModel = model.AlexNet(weightPath='/home/hjxu/tf_study/catVsDogsWithKeras/my_model_weights.h5')
 
modelAlex.load_weights(pats)
#
img = cv2.imread('/home/hjxu/tf_study/catVsDogsWithKeras/111.jpg')
img = cv2.resize(img,(224,224))
x = img_to_array(img/255) # 三維（224，224，3）
 
x = np.expand_dims(x,axis=0) # 四維（1，224，224，3）#因為keras要求的維度是這樣的，所以要增加一個維度
# x = preprocess_input(x) # 預處理
print(x.shape)
y_pred = modelAlex.predict(x) # 預測概率 t1 = time.time() print("測試圖：",decode_predictions(y_pred)) # 輸出五個最高概率(類名,語義概念,預測概率)
print y_pred

不得不說，Keras真心簡單方便。

補充知識：keras中的函式式API——殘差連線+權重共享的理解

1、殘差連線

# coding: utf-8
"""殘差連線 residual connection：
  是一種常見的類圖網路結構，解決了所有大規模深度學習的兩個共性問題：
   1、梯度消失
   2、表示瓶頸
  （甚至，向任何>10層的神經網路新增殘差連線，都可能會有幫助）

  殘差連線：讓前面某層的輸出作為後面某層的輸入，從而在序列網路中有效地創造一條捷徑。
       """
from keras import layers

x = ...
y = layers.Conv2D(128,3,padding='same')(x)
y = layers.Conv2D(128,padding='same')(y)
y = layers.Conv2D(128,padding='same')(y)

y = layers.add([y,x]) # 將原始x與輸出特徵相加

# ---------------------如果特徵圖尺寸不同，採用線性殘差連線-------------------
x = ...
y = layers.Conv2D(128,padding='same')(y)
y = layers.MaxPooling2D(2,strides=2)(y)

residual = layers.Conv2D(128,1,strides=2,padding='same')(x) # 使用1*1的卷積，將原始張量線性下采樣為y具有相同的形狀

y = layers.add([y,residual]) # 將原始x與輸出特徵相加

2、權重共享

即多次呼叫同一個例項

# coding: utf-8
"""函式式子API：權重共享
  能夠重複的使用同一個例項，這樣相當於重複使用一個層的權重，不需要重新編寫"""
from keras import layers
from keras import Input
from keras.models import Model


lstm = layers.LSTM(32) # 例項化一個LSTM層,後面被呼叫很多次

# ------------------------左邊分支--------------------------------
left_input = Input(shape=(None,128))
left_output = lstm(left_input) # 呼叫lstm例項

# ------------------------右分支---------------------------------
right_input = Input(shape=(None,128))
right_output = lstm(right_input) # 呼叫lstm例項

# ------------------------將層進行連接合並------------------------
merged = layers.concatenate([left_output,right_output],axis=-1)

# -----------------------在上面構建一個分類器---------------------
predictions = layers.Dense(1,activation='sigmoid')(merged)

# -------------------------構建模型，並擬合訓練-----------------------------------
model = Model([left_input,right_input],predictions)
model.fit([left_data,right_data],targets)

以上這篇keras實現多種分類網路的方式就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。