keras系列︱迁移学习：利用InceptionV3进行fine-tuning及预测、完美案例（五）

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.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/ubuntu/keras/animal5/train'  # 训练集数据
val_dir = '/home/ubuntu/keras/animal5/validation' # 验证集数据
nb_classes= 5
nb_epoch = 3
batch_size = 16

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 = InceptionV3(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')

# 冻上NB_IV3_LAYERS之前的层
def setup_to_finetune(model):
  """Freeze the bottom NB_IV3_LAYERS and retrain the remaining top layers.

  note: NB_IV3_LAYERS corresponds to the top 2 inception blocks in the inceptionv3 arch

  Args:
    model: keras model
  """
  for layer in model.layers[:NB_IV3_LAYERS_TO_FREEZE]:
     layer.trainable = False
  for layer in model.layers[NB_IV3_LAYERS_TO_FREEZE:]:
     layer.trainable = True
  model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])

# 设置网络结构
setup_to_finetune(model)

# 模式二训练
history_ft = model.fit_generator(
train_generator,
samples_per_epoch=nb_train_samples,
nb_epoch=nb_epoch,
validation_data=validation_generator,
nb_val_samples=nb_val_samples,
class_weight='auto')

# 模型保存
model.save(args.output_model_file)

# 正确
Epoch 1/3

25/25 [==============================] - 38s - loss: 0.0589 - acc: 0.9850 - val_loss: 0.0084 - val_acc: 0.9986
Epoch 2/3
25/25 [==============================] - 37s - loss: 0.0196 - acc: 0.9925 - val_loss: 0.0150 - val_acc: 0.9965
Epoch 3/3
25/25 [==============================] - 37s - loss: 0.0349 - acc: 0.9875 - val_loss: 0.0101 - val_acc: 0.9979

# 画图
def plot_training(history):
  acc = history.history['acc']
  val_acc = history.history['val_acc']
  loss = history.history['loss']
  val_loss = history.history['val_loss']
  epochs = range(len(acc))
  plt.plot(epochs, acc, 'r.')
  plt.plot(epochs, val_acc, 'r')
  plt.title('Training and validation accuracy')
  plt.figure()
  plt.plot(epochs, loss, 'r.')
  plt.plot(epochs, val_loss, 'r-')
  plt.title('Training and validation loss')
  plt.show()

# 训练的acc_loss图
plot_training(history_ft)

# 定义层
import sys
import argparse
import numpy as np
from PIL import Image
import requests
from io import BytesIO
import matplotlib.pyplot as plt

from keras.preprocessing import image
from keras.models import load_model
from keras.applications.inception_v3 import preprocess_input

# 狂阶图片指定尺寸
target_size = (229, 229) #fixed size for InceptionV3 architecture

# 预测函数
# 输入：model，图片，目标尺寸
# 输出：预测predict
def predict(model, img, target_size):
  """Run model prediction on image
  Args:
    model: keras model
    img: PIL format image
    target_size: (w,h) tuple
  Returns:
    list of predicted labels and their probabilities
  """
  if img.size != target_size:
    img = img.resize(target_size)

  x = image.img_to_array(img)
  x = np.expand_dims(x, axis=0)
  x = preprocess_input(x)
  preds = model.predict(x)
  return preds[0]

# 画图函数
# 预测之后画图，这里默认是猫狗，当然可以修改label

labels = ("cat", "dog")
def plot_preds(image, preds,labels):
  """Displays image and the top-n predicted probabilities in a bar graph
  Args:
    image: PIL image
    preds: list of predicted labels and their probabilities
  """
  plt.imshow(image)
  plt.axis('off')
  plt.figure()
  plt.barh([0, 1], preds, alpha=0.5)
  plt.yticks([0, 1], labels)
  plt.xlabel('Probability')
  plt.xlim(0,1.01)
  plt.tight_layout()
  plt.show()

# 载入模型
model = load_model(args.model)

# 本地图片
img = Image.open(image)
preds = predict(model, img, target_size)
plot_preds(img, preds)

# 图片URL
response = requests.get(image_url)
img = Image.open(BytesIO(response.content))
preds = predict(model, img, target_size)
plot_preds(img, preds)

    model = Sequential()
    model.add(Dense(2, input_dim=3, name="dense_1"))
    model.add(Dense(3, name="dense_2"))
    ...
    model.save_weights(fname)

# new model
model = Sequential()
model.add(Dense(2, input_dim=3, name="dense_1"))  # will be loaded
model.add(Dense(10, name="new_dense"))  # will not be loaded

# load weights from first model; will only affect the first layer, dense_1.
model.load_weights(fname, by_name=True)