keras分类代码2（利用已有模型进行训练）

1.训练代码

import os
import sys
import glob
import argparse
import matplotlib.pyplot as plt

from keras import __version__
from keras.applications.inception_v3 import InceptionV3, preprocess_input
# from keras.applications.inception_v3_matt import InceptionV3, preprocess_input

from keras.applications.resnet50 import ResNet50, preprocess_input

from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD

def get_nb_files(directory):
  """Get number of files by searching directory recursively"""
  if not os.path.exists(directory):
    return 0
  cnt = 0
  for r, dirs, files in os.walk(directory):
    for dr in dirs:
      cnt += len(glob.glob(os.path.join(r, dr + "/*")))
  return cnt
# get_nb_files('/home/ubuntu/keras/animal5/train')

# 数据准备
IM_WIDTH, IM_HEIGHT = 299, 299 #InceptionV3指定的图片尺寸
FC_SIZE = 1024                # 全连接层的节点个数
NB_IV3_LAYERS_TO_FREEZE = 172  # 冻结层的数量


train_dir = '/home/yang/Documents/data/classifier//train'  # 训练集数据
val_dir = '/home/yang/Documents/data/classifier/val' # 验证集数据
nb_classes= 2
nb_epoch = 100
batch_size = 4

nb_train_samples = get_nb_files(train_dir)      # 训练样本个数
nb_classes = len(glob.glob(train_dir + "/*"))  # 分类数
nb_val_samples = get_nb_files(val_dir)       #验证集样本个数
nb_epoch = int(nb_epoch)                # epoch数量
batch_size = int(batch_size)

#　图片生成器
train_datagen =  ImageDataGenerator(
  preprocessing_function=preprocess_input,
  rotation_range=30,
  width_shift_range=0.2,
  height_shift_range=0.2,
  shear_range=0.2,
  zoom_range=0.2,
  horizontal_flip=True
)
test_datagen = ImageDataGenerator(
  preprocessing_function=preprocess_input,
  rotation_range=30,
  width_shift_range=0.2,
  height_shift_range=0.2,
  shear_range=0.2,
  zoom_range=0.2,
  horizontal_flip=True
)

# 训练数据与测试数据
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(IM_WIDTH, IM_HEIGHT),
batch_size=batch_size,class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
val_dir,
target_size=(IM_WIDTH, IM_HEIGHT),
batch_size=batch_size,class_mode='categorical')

# 添加新层
def add_new_last_layer(base_model, nb_classes):
  """
  添加最后的层
  输入
  base_model和分类数量
  输出
  新的keras的model
  """
  x = base_model.output
  x = GlobalAveragePooling2D()(x)
  x = Dense(FC_SIZE, activation='relu')(x) #new FC layer, random init
  predictions = Dense(nb_classes, activation='softmax')(x) #new softmax layer
  model = Model(input=base_model.input, output=predictions)
  return model

# 冻上base_model所有层，这样就可以正确获得bottleneck特征
def setup_to_transfer_learn(model, base_model):
  """Freeze all layers and compile the model"""
  for layer in base_model.layers:
    layer.trainable = False
  model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

# 定义网络框架
base_model = ResNet50(weights='imagenet', include_top=False) # 预先要下载no_top模型
model = add_new_last_layer(base_model, nb_classes)              # 从基本no_top模型上添加新层
setup_to_transfer_learn(model, base_model)                      # 冻结base_model所有层

# 模式一训练
history_tl = model.fit_generator(
train_generator,
nb_epoch=nb_epoch,
samples_per_epoch=nb_train_samples,
validation_data=validation_generator,
nb_val_samples=nb_val_samples,
class_weight='auto')