keras多种分类网络 keras实现多种分类网络的方式

#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, 3),
       strides=(2, 2),
       padding='valid'))
 # 第二段
 model.add(Conv2D(filters=256, kernel_size=(5, 5),
      strides=(1, 1), padding='same',
      activation='relu'))
 model.add(BatchNormalization())
 model.add(MaxPooling2D(pool_size=(3, 3),
       strides=(2, 2),
       padding='valid'))
 # 第三段
 model.add(Conv2D(filters=384, kernel_size=(3, 3),
      strides=(1, 1), padding='same',
      activation='relu'))
 model.add(Conv2D(filters=384, kernel_size=(3, 3),
      strides=(1, 1), padding='same',
      activation='relu'))
 model.add(Conv2D(filters=256, kernel_size=(3, 3),
      strides=(1, 1), padding='same',
      activation='relu'))
 model.add(MaxPooling2D(pool_size=(3, 3),
       strides=(2, 2), 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):
 '''
 自己写的函数,尝试keras另外一种写法
 :param inputs: 输入
 :param classes: 类别的个数
 :param prob: dropout的概率
 :return: 模型
 '''
 # Conv2D(32, (3, 3), dilation_rate=(2, 2), padding='same')(inputs)
 print "input shape:", inputs.shape
 
 conv1 = Conv2D(filters=96, kernel_size=(11, 11), strides=(4, 4), padding='valid')(inputs)
 conv1 = keras_batchnormalization_relu(conv1)
 print "conv1 shape:", conv1.shape
 pool1 = MaxPool2D(pool_size=(3, 3), strides=(2, 2))(conv1)
 print "pool1 shape:", pool1.shape
 
 conv2 = Conv2D(filters=256, kernel_size=(5, 5), padding='same')(pool1)
 conv2 = keras_batchnormalization_relu(conv2)
 print "conv2 shape:", conv2.shape
 pool2 = MaxPool2D(pool_size=(3, 3), strides=(2, 2))(conv2)
 print "pool2 shape:", pool2.shape
 
 conv3 = Conv2D(filters=384, kernel_size=(3, 3), padding='same')(pool2)
 conv3 = PReLU()(conv3)
 print "conv3 shape:", conv3.shape
 
 conv4 = Conv2D(filters=384, kernel_size=(3, 3), padding='same')(conv3)
 conv4 = PReLU()(conv4)
 print "conv4 shape:", conv4
 
 conv5 = Conv2D(filters=256, kernel_size=(3, 3), padding='same')(conv4)
 conv5 = PReLU()(conv5)
 print "conv5 shape:", conv5
 
 pool3 = MaxPool2D(pool_size=(3, 3), strides=(2, 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, activation='relu')(dense1)
 print "dense2 shape:", 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, classes=2, prob=0.5):
 model = Sequential()
 model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(resize, resize, 3), padding='same', activation='relu',
      kernel_initializer='uniform'))
 model.add(Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(128, (3, 2), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 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, classes=2, prob=0.5):
 model = Sequential()
 model.add(Conv2D(64, (3, 3), strides=(1, 1), input_shape=(resize, resize, 3), padding='same', activation='relu',
      kernel_initializer='uniform'))
 model.add(Conv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(128, (3, 2), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(256, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 2)))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(Conv2D(512, (3, 3), strides=(1, 1), padding='same', activation='relu', kernel_initializer='uniform'))
 model.add(MaxPooling2D(pool_size=(2, 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

#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, resize, resize, 3), dtype="int32")
 train_label = np.empty((5000, ), dtype="int32")
 test_data = np.empty((5000, resize, resize, 3), 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, 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, resize))
   test_label[i-5000] = 1
  else:
   test_data[i-5000] = cv2.resize(cv2.imread(path + '/train/' + 'cat.' + str(i) + '.jpg'), (resize, resize))
   test_label[i-5000] = 0
 return train_data, train_label, test_data, test_label
 
def main():
 
 train_data, train_label, test_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转码,如果使用 categorical_crossentropy,就需要用到to_categorical函数完成转码
 '''
 test_label = keras.utils.to_categorical(test_label, 2)
 
 inputs = Input(shape=(224, 224, 3))
 
 modelAlex = model.AlexNet2(inputs, classes=2)
 '''
 导入模型
 '''
 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:优化器,为预定义优化器名或优化器对象,参考优化器
  loss: 损失函数,为预定义损失函数名或者一个目标函数
  metrics:列表,包含评估模型在训练和测试时的性能指标,典型用法是 metrics=['accuracy']
  sample_weight_mode:如果需要按时间步为样本赋值,需要将改制设置为"temoral"
  如果想用自定义的性能评估函数:如下
   def mean_pred(y_true, y_pred):
   return k.mean(y_pred)
  model.compile(loss = 'binary_crossentropy', metrics=['accuracy', mean_pred],...)
  损失函数同理,再看 keras内置支持的损失函数有
   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()
 '''
 # 打印模型信息
 '''
 modelAlex.fit(train_data, train_label,
    batch_size=batch_size,
    epochs=50,
    validation_split=0.2,
    shuffle=True)
 '''
 def fit(self, x=None,   # x:输入数据
   y=None,     # y:标签 Numpy array
   batch_size=32,   # batch_size:训练时,一个batch的样本会被计算一次梯度下降
   epochs=1,    # epochs: 训练的轮数,每个epoch会把训练集循环一遍
   verbose=1,    # 日志显示:0表示不在标准输入输出流输出,1表示输出进度条,2表示每个epoch输出
   callbacks=None,   # 回调函数
   validation_split=0.,  # 0-1的浮点数,用来指定训练集一定比例作为验证集,验证集不参与训练
   validation_data=None, # (x,y)的tuple,是指定的验证集
   shuffle=True,   # 如果是"batch",则是用来处理HDF5数据的特殊情况,将在batch内部将数据打乱
   class_weight=None,  # 字典,将不同的类别映射为不同的权值,用来在训练过程中调整损失函数的
   sample_weight=None,  # 权值的numpy array,用于训练的时候调整损失函数
   initial_epoch=0,   # 该参数用于从指定的epoch开始训练,继续之前的训练
   **kwargs):
 返回:返回一个History的对象,其中History.history损失函数和其他指标的数值随epoch变化的情况
 '''
 scores = modelAlex.evaluate(train_data, train_label, 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,
               target_size=(224, 224),
               batch_size=32,
               class_mode='binary')
 
 inputs = Input(shape=(224, 224, 3))
 # modelAlex = model.AlexNet2(inputs, classes=2)
 modelAlex = model.vgg13(resize=224, classes=2, prob=0.5)
 # modelAlex = modelResNet.ResNet50(shape=224, classes=2)
 modelAlex.compile(loss='sparse_categorical_crossentropy',
      optimizer='sgd',
      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__":
 '''
 如果数据是按照猫狗大战的数据,都在同一个文件夹下,使用main()函数
 如果数据按照猫和狗分成两类,则使用main2()函数
 '''
 main2()

#coding=utf-8
from keras.preprocessing.image import load_img#load_image作用是载入图片
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) # 三维（224，224，3）
 
x = np.expand_dims(x, axis=0) # 四维（1，224，224，3）#因为keras要求的维度是这样的，所以要增加一个维度
# x = preprocess_input(x) # 预处理
print(x.shape)
y_pred = modelAlex.predict(x) # 预测概率 t1 = time.time() print("测试图：", decode_predictions(y_pred)) # 输出五个最高概率(类名, 语义概念, 预测概率)
print y_pred

# coding: utf-8
"""残差连接 residual connection：
  是一种常见的类图网络结构，解决了所有大规模深度学习的两个共性问题：
   1、梯度消失
   2、表示瓶颈
  （甚至，向任何>10层的神经网络添加残差连接，都可能会有帮助）

  残差连接：让前面某层的输出作为后面某层的输入，从而在序列网络中有效地创造一条捷径。
       """
from keras import layers

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

y = layers.add([y, x]) # 将原始x与输出特征相加

# ---------------------如果特征图尺寸不同，采用线性残差连接-------------------
x = ...
y = layers.Conv2D(128, 3, activation='relu', padding='same')(x)
y = layers.Conv2D(128, 3, activation='relu', padding='same')(y)
y = layers.MaxPooling2D(2, strides=2)(y)

residual = layers.Conv2D(128, 1, strides=2, padding='same')(x) # 使用1*1的卷积，将原始张量线性下采样为y具有相同的形状

y = layers.add([y, residual]) # 将原始x与输出特征相加

# coding: utf-8
"""函数式子API：权重共享
  能够重复的使用同一个实例，这样相当于重复使用一个层的权重，不需要重新编写"""
from keras import layers
from keras import Input
from keras.models import Model


lstm = layers.LSTM(32) # 实例化一个LSTM层,后面被调用很多次

# ------------------------左边分支--------------------------------
left_input = Input(shape=(None, 128))
left_output = lstm(left_input) # 调用lstm实例

# ------------------------右分支---------------------------------
right_input = Input(shape=(None, 128))
right_output = lstm(right_input) # 调用lstm实例

# ------------------------将层进行连接合并------------------------
merged = layers.concatenate([left_output, right_output], axis=-1)

# -----------------------在上面构建一个分类器---------------------
predictions = layers.Dense(1, activation='sigmoid')(merged)

# -------------------------构建模型，并拟合训练-----------------------------------
model = Model([left_input, right_input], predictions)
model.fit([left_data, right_data], targets)