使用预训练的卷积神经网络__keras实现VGG16

#方法一:不使用数据增强的快速特征提取
import os
import numpy as np
from keras.preprocessing.image import ImageDataGenerator

base_dir = ‘cats_dogs_images‘

train_dir = os.path.join(base_dir,‘train‘)
validation_dir = os.path.join(base_dir,‘validation‘)
test_dir = os.path.join(base_dir,‘test‘)

datagen = ImageDataGenerator(rescale = 1./255)#将所有图像乘以1/255缩放
batch_size = 20

def extract_features(directory,sample_count):
    features = np.zeros(shape=(sample_count,4,4,512))
    labels = np.zeros(shape=(sample_count))
# 通过.flow或.flow_from_directory(directory)方法实例化一个针对图像batch的生成器，这些生成器
# 可以被用作keras模型相关方法的输入，如fit_generator，evaluate_generator和predict_generator
    generator = datagen.flow_from_directory(  
        ‘cats_dogs_images/train‘,
        target_size = (150,150),
        batch_size = batch_size,
        class_mode = ‘binary‘)
    i = 0
    for inputs_batch,labels_batch in generator:
        features_batch = conv_base.predict(inputs_batch)
        features[i * batch_size:(i+1) * batch_size] = features_batch
        labels[i * batch_size:(i+1)*batch_size] = labels_batch
        i += 1
        if i * batch_size >= sample_count:
            break
    return features,labels


train_features,train_labels = extract_features(train_dir,2000)
validation_features,validation_labels = extract_features(validataion_dir,1000)
test_features,test_labels = extract_features(test_dir,1000)

  # 要减特征(samples,4,4,512)输入密集连接分类器中，首先必须将其形状展平为(samples,8192)

train_features = np.reshape(train_features,(2000,4*4*512))
validation_features = np.reshape(validation_features,(2000,4*4*512))
test_features = np.reshape(test_features,(2000,4*4*512))

#定义并训练密集连接分类器
from keras import models
from keras import layers
from keras import optimizers

model = models.Sequential()
model.add(layers.Dense(256,activation=‘relu‘,input_dim=4*4*512))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1,activation=‘sigmoid‘))

model.compile(optimizers=optimizers.RMSprop(lr=2e-5),
              loss=‘binary_crossentropy‘,
              metrics = [‘acc‘])

history = model.fit(train_features,train_labels,
                    epochs=30,batch_size=20,
                    validation_data = (Validation_features,validation_labels) )

#在卷积基上添加一个密集连接分类器

from keras import models
from keras import layers

model = models.Sequential()
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dense(256,activation=‘relu‘))
model.add(layers.Dense(1,activation=‘sigmoid‘))

model.summary()

#使用冻结的卷积基端到端地训练模型
from keras.preprocessing.image import ImageDataGenerator
from kera import optimizers

train_datagen = ImageDataGenerator(
    rescale = 1./255,
    rotation_range = 40,
    width_shift_range = 0.2,
    height_shift_range = 0.2,
    shear_range = 0.2,
    zoom_range = 0.2,
    horizontal_flip = True,
    fill_mode = ‘nearest‘
)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_dirctory(
    train_dir,
    target_size = (150,150),
    batch_size = 20,
    class_mode = ‘binary‘
)

validation_generator = test_datagen.flow_from_dirctory(
    validation,
    target_size = (150,150),
    batch_size = 20,
    class_mode = ‘binary‘
)

model.compile(optimizer=optimizers.RMSprop(lr=2e-5),
             loss=‘binary_crossentropy‘,
             metrics = [‘acc‘])

history = model.fit_generator(
            train_generator,
            steps_per_epoch = 100,
            epochs = 30,
            validation_data = validation_generator,
            validation_steps=50
)