keras-VGG16加载预训练模型

太过爱你忘了你带给我的痛 2024-04-19 11:14 76阅读 0赞

import os
    import numpy as np
    from keras.models import Sequential, Model
    from keras import layers
    from keras.preprocessing.image import ImageDataGenerator
    from keras import optimizers
    from keras.applications.vgg16 import VGG16
    from keras.utils.np_utils import to_categorical
    from scipy.misc import imread, imresize
    import matplotlib.pyplot as plt
    imgs = []
    labels = []
    img_shape =(150,150)
    # image generator
    files = os.listdir('data/test')
    # read 1000 files for the generator
    for img_file in files[:1000]:
        img = imread('data/test/' + img_file).astype('float32')
        img = imresize(img, img_shape)
        imgs.append(img)
    
    imgs = np.array(imgs)
    train_gen = ImageDataGenerator(
         # rescale = 1./255,
         featurewise_center=True,
         featurewise_std_normalization=True,
         rotation_range=20,
         width_shift_range=0.2,
         height_shift_range=0.2,
         horizontal_flip=True)
    val_gen = ImageDataGenerator(
         # rescale = 1./255,
         featurewise_center=True,
         featurewise_std_normalization=True)
    
    train_gen.fit(imgs)
    val_gen.fit(imgs)
    
    # 4500 training images 
    train_iter = train_gen.flow_from_directory('data/train',class_mode='binary',
                                                target_size=img_shape,   batch_size=16)
    # 501 validation images
    val_iter = val_gen.flow_from_directory('data/val', class_mode='binary', 
                                            target_size=img_shape, batch_size=16)
    
    '''
    # image generator debug
    for x_batch, y_batch in img_iter:
        print(x_batch.shape)
        print(y_batch.shape)
        plt.imshow(x_batch[0])
        plt.show()
    '''
    
    # finetune from the base model VGG16
    base_model = VGG16(include_top=False, weights='imagenet', input_shape=(150, 150, 3))
    base_model.summary()
    
    out = base_model.layers[-1].output
    out = layers.Flatten()(out)
    out  = layers.Dense(1024, activation='relu')(out)
    # 因为前面输出的dense feature太多了，我们这里加入dropout layer来防止过拟合
    out = layers.Dropout(0.5)(out)
    out = layers.Dense(512, activation='relu')(out)
    out = layers.Dropout(0.3)(out)
    out = layers.Dense(1, activation='sigmoid')(out)
    tuneModel = Model(inputs=base_model.input, outputs = out)
    for layer in tuneModel.layers[:19]: # freeze the base model only use it as feature extractors
        layer.trainable = False
    tuneModel.compile(loss='binary_crossentropy', optimizer=optimizers.RMSprop(lr=1e-4),
            metrics=['acc'])
    
    history = tuneModel.fit_generator(
            generator=train_iter,
            steps_per_epoch=100,
            epochs=100,
            validation_data=val_iter,
            validation_steps=32
            )
    
    acc = history.history['acc']
    val_acc = history.history['val_acc']
    loss = history.history['loss']
    val_loss = history.history['val_loss']
    epochs = range(1,101)
    plt.plot(epochs, acc, 'bo', label='Training acc')
    plt.plot(epochs, val_acc, 'r', label='Validation acc')
    plt.legend()
    plt.figure()
    plt.plot(epochs, loss, 'bo', label='Training loss')
    plt.plot(epochs, val_loss, 'r', label='Validation loss')
    plt.legend()
    plt.show()

### 案例1 ###

import time
    from keras.applications.imagenet_utils import preprocess_input, decode_predictions
    from keras.layers import Dropout, Flatten, Dense
    from keras.layers.convolutional import Convolution2D,MaxPooling2D
    from keras.models import Sequential
    from keras.preprocessing import image
    from keras import backend as K
    K.set_image_data_format('channels_first')
    import numpy as np
     
    t0 = time.time()
    height, width = 224, 224
    img_path = 'cat.jpg'
    img = image.load_img(img_path, target_size=(224, 224)) # 224×224
    x = image.img_to_array(img)  # 三维(3, 224,224)
    x = np.expand_dims(x, axis=0)  # 四维(1, 3,224, 224)
    x = preprocess_input(x)  # 预处理
    print("训练样例:", x.shape)
    print("取数据耗时: %.2f seconds ..." % (time.time() -t0))
     
    print("开始建模CNN ...")
    model = Sequential()
    # Block 1, 2层
    model.add(Convolution2D(64, 3, 3, activation='relu',
                        border_mode='same', input_shape=(3,height, width)))
    model.add(Convolution2D(64, 3, 3, activation='relu',border_mode='same'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
     
    # Block 2, 2层
    model.add(Convolution2D(128, 3, 3, activation='relu',border_mode='same'))
    model.add(Convolution2D(128, 3, 3, activation='relu',border_mode='same'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
     
    # Block 3, 3层
    model.add(Convolution2D(256, 3, 3, activation='relu',border_mode='same'))
    model.add(Convolution2D(256, 3, 3, activation='relu',border_mode='same'))
    model.add(Convolution2D(256, 3, 3, activation='relu',border_mode='same'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
     
    # Block 4, 3层
    model.add(Convolution2D(512, 3, 3, activation='relu',border_mode='same'))
    model.add(Convolution2D(512, 3, 3, activation='relu',border_mode='same'))
    model.add(Convolution2D(512, 3, 3, activation='relu',border_mode='same'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
     
    # Block 5, 3层
    model.add(Convolution2D(512, 3, 3, activation='relu',border_mode='same'))
    model.add(Convolution2D(512, 3, 3, activation='relu',border_mode='same'))
    model.add(Convolution2D(512, 3, 3, activation='relu',border_mode='same'))
    model.add(MaxPooling2D((2, 2), strides=(2, 2)))
     
    # Classification block, 全连接3层
    model.add(Flatten())
    model.add(Dense(2000, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(2000, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1000, activation='softmax'))
    model.load_weights('vgg16_weights_th_dim_ordering_th_kernels.h5')
    print("建模CNN完成 ...")
     
    y_pred = model.predict(x)
    print('预测%s (类名, 语义概念, 预测概率) = %s' % (img_path,decode_predictions(y_pred))) #前五个
    print('类预测:', decode_predictions(y_pred)[0][0][1])#概率最大类
    print("耗时: %.2f seconds【徐海蛟博士】 ..." % (time.time() -t0))