Keras GlobalAveragePooling2D 示例代码

GlobalAveragePooling2D层

keras.layers.pooling.GlobalAveragePooling2D(dim_ordering=‘default‘)

为空域信号施加全局平均值池化

参数

data_format：字符串，“channels_first”或“channels_last”之一，代表图像的通道维的位置。该参数是Keras 1.x中的image_dim_ordering，“channels_last”对应原本的“tf”，“channels_first”对应原本的“th”。以128x128的RGB图像为例，“channels_first”应将数据组织为（3,128,128），而“channels_last”应将数据组织为（128,128,3）。该参数的默认值是~/.keras/keras.json中设置的值，若从未设置过，则为“channels_last”。

输入shape

‘channels_first’模式下，为形如（samples，channels, rows，cols）的4D张量

‘channels_last’模式下，为形如（samples，rows, cols，channels）的4D张量

输出shape

形如(nb_samples, channels)的2D张量

示例代码

keras-finetuning

def build_model(nb_classes):

    base_model = InceptionV3(weights='imagenet', include_top=False)

    # add a global spatial average pooling layer

    x = base_model.output

    x = GlobalAveragePooling2D()(x)

    # let's add a fully-connected layer

    x = Dense(1024, activation='relu')(x)

    # and a logistic layer

    predictions = Dense(nb_classes, activation='softmax')(x)

    # this is the model we will train

    model = Model(input=base_model.input, output=predictions)

    # first: train only the top layers (which were randomly initialized)

    # i.e. freeze all convolutional InceptionV3 layers

    for layer in base_model.layers:

        layer.trainable = False

    # compile the model (should be done *after* setting layers to non-trainable)

    print "starting model compile"

    compile(model)

    print "model compile done"

    return model

Kaggle-Sea-Lions-Solution

def get_model():

    input_shape = (image_size, image_size, 3)

    model = Sequential()

    model.add(Conv2D(32, kernel_size=(3, 3), padding='same',

                     input_shape=input_shape))

    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(64, kernel_size=(3, 3), padding='same'))

    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(128, kernel_size=(3, 3), padding='same'))

    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Conv2D(n_classes, kernel_size=(3, 3), padding='same'))

    model.add(Activation('relu'))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(GlobalAveragePooling2D())

    print (model.summary())

    #sys.exit(0) #

    model.compile(loss=keras.losses.mean_squared_error,

            optimizer= keras.optimizers.Adadelta())

    return model

来源： https://www.programcreek.com/python/example/89688/keras.layers.GlobalAveragePooling2D

巴特西

Keras GlobalAveragePooling2D 示例代码

GlobalAveragePooling2D层

参数

输入shape

输出shape

示例代码

keras-finetuning

Kaggle-Sea-Lions-Solution

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