keras-VGG16模型

分手后的思念是犯贱 2021-11-17 20:52 439阅读 0赞

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 = img_to_array(img_path,target_size=(224,224))
    x = 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
    
    
    #将导入的图片变成224,224，然后将三维的图片变成四维的图片
    print("开始建模")
    
    def VGG16(height,width):
        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'))
        return model
    
    
    model = VGG16(height=height,width=width)
    model.load_weights('vgg16_weights_th_dim_ordering_th_kernels.h5')
    print("建模CNN完成 ...")
    
    # 构建VGG-16模型，并且导入权重，记住，如果你是theano就导入theano的权重，否则就导入tensorflow的权重。
    
    y_pred = model.predict(x)
    print(img_path,decode_predictions(y_pred))
    print(decode_predictions(y_pred)[0][0][1])
    
    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))