Python keras.layers.convolutional 模块，MaxPooling3D() 实例源码

我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用keras.layers.convolutional.MaxPooling3D()。

项目：keras 作者：NVIDIA | 项目源码 | 文件源码

def test_maxpooling_3d():
    pool_size = (3, 3, 3)

    for strides in [(1, 1, 1), (2, 2, 2)]:
        layer_test(convolutional.MaxPooling3D,
                   kwargs={'strides': strides,
                           'border_mode': 'valid',
                           'pool_size': pool_size},
                   input_shape=(3, 4, 11, 12, 10))

项目：Kaggle-DSB 作者：Wrosinski | 项目源码 | 文件源码

def preds3d_baseline(width):

    learning_rate = 5e-5
    #optimizer = SGD(lr=learning_rate, momentum = 0.9, decay = 1e-3, nesterov = True)
    optimizer = Adam(lr=learning_rate)

    inputs = Input(shape=(1, 136, 168, 168))
    conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)
    conv1 = BatchNormalization(axis = 1)(conv1)
    conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)
    conv1 = BatchNormalization(axis = 1)(conv1)
    pool1 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv1)

    conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)
    conv2 = BatchNormalization(axis = 1)(conv2)
    conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)
    conv2 = BatchNormalization(axis = 1)(conv2)
    pool2 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv2)

    conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)
    conv3 = BatchNormalization(axis = 1)(conv3)
    conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)
    conv3 = BatchNormalization(axis = 1)(conv3)
    pool3 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv3)

    output = GlobalAveragePooling3D()(pool3)
    output = Dense(2, activation='softmax', name = 'predictions')(output)
    model3d = Model(inputs, output)
    model3d.compile(loss='categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])
    return model3d

项目：keras 作者：GeekLiB | 项目源码 | 文件源码

def test_maxpooling_3d():
    pool_size = (3, 3, 3)

    for strides in [(1, 1, 1), (2, 2, 2)]:
        layer_test(convolutional.MaxPooling3D,
                   kwargs={'strides': strides,
                           'border_mode': 'valid',
                           'pool_size': pool_size},
                   input_shape=(3, 4, 11, 12, 10))

项目：keras-customized 作者：ambrite | 项目源码 | 文件源码

def test_maxpooling_3d():
    pool_size = (3, 3, 3)

    for strides in [(1, 1, 1), (2, 2, 2)]:
        layer_test(convolutional.MaxPooling3D,
                   kwargs={'strides': strides,
                           'border_mode': 'valid',
                           'pool_size': pool_size},
                   input_shape=(3, 4, 11, 12, 10))

项目：Kaggle-DSB 作者：Wrosinski | 项目源码 | 文件源码

def preds3d_baseline(width):

    learning_rate = 5e-5
    optimizer = SGD(lr=learning_rate, momentum = 0.9, decay = 1e-3, nesterov = True)
    #optimizer = Adam(lr=learning_rate)

    inputs = Input(shape=(1, 136, 168, 168))
    conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)
    conv1 = BatchNormalization(axis = 1)(conv1)
    conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)
    conv1 = BatchNormalization(axis = 1)(conv1)
    pool1 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv1)

    conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)
    conv2 = BatchNormalization(axis = 1)(conv2)
    conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)
    conv2 = BatchNormalization(axis = 1)(conv2)
    pool2 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv2)

    conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)
    conv3 = BatchNormalization(axis = 1)(conv3)
    conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)
    conv3 = BatchNormalization(axis = 1)(conv3)
    pool3 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv3)

    output = GlobalAveragePooling3D()(pool3)
    output = Dense(2, activation='softmax', name = 'predictions')(output)
    model3d = Model(inputs, output)
    model3d.compile(loss='categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])
    return model3d


# 1398 stage1 original examples

项目：Kaggle-DSB 作者：Wrosinski | 项目源码 | 文件源码

def preds3d_globalavg(width):

    learning_rate = 5e-5
    #optimizer = SGD(lr=learning_rate, momentum = 0.9, decay = 1e-3, nesterov = True)
    optimizer = Adam(lr=learning_rate)

    inputs = Input(shape=(1, 136, 168, 168))
    conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)
    conv1 = BatchNormalization(axis = 1)(conv1)
    conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)
    conv1 = BatchNormalization(axis = 1)(conv1)
    pool1 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv1)

    conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)
    conv2 = BatchNormalization(axis = 1)(conv2)
    conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)
    conv2 = BatchNormalization(axis = 1)(conv2)
    pool2 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv2)

    conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)
    conv3 = BatchNormalization(axis = 1)(conv3)
    conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)
    conv3 = BatchNormalization(axis = 1)(conv3)
    pool3 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv3)

    conv4 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(pool3)
    conv4 = BatchNormalization(axis = 1)(conv4)
    conv4 = Convolution3D(width*16, 3, 3, 3, activation = 'relu', border_mode='same')(conv4)
    conv4 = BatchNormalization(axis = 1)(conv4)
    pool4 = MaxPooling3D(pool_size=(8, 8, 8), border_mode='same')(conv4)

    output = GlobalAveragePooling3D()(conv4)
    output = Dense(2, activation='softmax', name = 'predictions')(output)
    model3d = Model(inputs, output)
    model3d.compile(loss='categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])
    return model3d

项目：Kaggle-DSB 作者：Wrosinski | 项目源码 | 文件源码

def unet_model():

    inputs = Input(shape=(1, max_slices, img_size, img_size))
    conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)
    conv1 = BatchNormalization(axis = 1)(conv1)
    conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)
    conv1 = BatchNormalization(axis = 1)(conv1)
    pool1 = MaxPooling3D(pool_size=(2, 2, 2), strides = (2, 2, 2), border_mode='same')(conv1)

    conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)
    conv2 = BatchNormalization(axis = 1)(conv2)
    conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)
    conv2 = BatchNormalization(axis = 1)(conv2)
    pool2 = MaxPooling3D(pool_size=(2, 2, 2), strides = (2, 2, 2), border_mode='same')(conv2)

    conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)
    conv3 = BatchNormalization(axis = 1)(conv3)
    conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)
    conv3 = BatchNormalization(axis = 1)(conv3)
    pool3 = MaxPooling3D(pool_size=(2, 2, 2), strides = (2, 2, 2), border_mode='same')(conv3)

    conv4 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(pool3)
    conv4 = BatchNormalization(axis = 1)(conv4)
    conv4 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv4)
    conv4 = BatchNormalization(axis = 1)(conv4)
    conv4 = Convolution3D(width*16, 3, 3, 3, activation = 'relu', border_mode='same')(conv4)
    conv4 = BatchNormalization(axis = 1)(conv4)

    up5 = merge([UpSampling3D(size=(2, 2, 2))(conv4), conv3], mode='concat', concat_axis=1)
    conv5 = SpatialDropout3D(dropout_rate)(up5)
    conv5 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv5)
    conv5 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv5)

    up6 = merge([UpSampling3D(size=(2, 2, 2))(conv5), conv2], mode='concat', concat_axis=1)
    conv6 = SpatialDropout3D(dropout_rate)(up6)
    conv6 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv6)
    conv6 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv6)

    up7 = merge([UpSampling3D(size=(2, 2, 2))(conv6), conv1], mode='concat', concat_axis=1)
    conv7 = SpatialDropout3D(dropout_rate)(up7)
    conv7 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv7)
    conv7 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv7)
    conv8 = Convolution3D(1, 1, 1, 1, activation='sigmoid')(conv7)

    model = Model(input=inputs, output=conv8)
    model.compile(optimizer=Adam(lr=1e-5), 
                  loss=dice_coef_loss, metrics=[dice_coef])

    return model

项目：Kaggle-DSB 作者：Wrosinski | 项目源码 | 文件源码

def preds3d_dense(width):

    learning_rate = 5e-5
    #optimizer = SGD(lr=learning_rate, momentum = 0.9, decay = 1e-3, nesterov = True)
    optimizer = Adam(lr=learning_rate)

    inputs = Input(shape=(1, 136, 168, 168))
    conv1 = Convolution3D(width, 3, 3, 3, activation = 'relu', border_mode='same')(inputs)
    conv1 = BatchNormalization(axis = 1)(conv1)
    conv1 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(conv1)
    conv1 = BatchNormalization(axis = 1)(conv1)
    pool1 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv1)

    conv2 = Convolution3D(width*2, 3, 3, 3, activation = 'relu', border_mode='same')(pool1)
    conv2 = BatchNormalization(axis = 1)(conv2)
    conv2 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(conv2)
    conv2 = BatchNormalization(axis = 1)(conv2)
    pool2 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv2)

    conv3 = Convolution3D(width*4, 3, 3, 3, activation = 'relu', border_mode='same')(pool2)
    conv3 = BatchNormalization(axis = 1)(conv3)
    conv3 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(conv3)
    conv3 = BatchNormalization(axis = 1)(conv3)
    pool3 = MaxPooling3D(pool_size=(2, 2, 2), border_mode='same')(conv3)

    conv4 = Convolution3D(width*8, 3, 3, 3, activation = 'relu', border_mode='same')(pool3)
    conv4 = BatchNormalization(axis = 1)(conv4)
    conv4 = Convolution3D(width*16, 3, 3, 3, activation = 'relu', border_mode='same')(conv4)
    conv4 = BatchNormalization(axis = 1)(conv4)
    pool4 = MaxPooling3D(pool_size=(8, 8, 8), border_mode='same')(conv4)

    output = Flatten(name='flatten')(pool4)
    output = Dropout(0.2)(output)
    output = Dense(128)(output)
    output = PReLU()(output)
    output = BatchNormalization()(output)
    output = Dropout(0.2)(output)
    output = Dense(128)(output)
    output = PReLU()(output)
    output = BatchNormalization()(output)
    output = Dropout(0.3)(output)
    output = Dense(2, activation='softmax', name = 'predictions')(output)
    model3d = Model(inputs, output)
    model3d.compile(loss='categorical_crossentropy', optimizer = optimizer, metrics = ['accuracy'])
    return model3d

项目：c3d-keras 作者：axon-research | 项目源码 | 文件源码

def get_model(summary=False, backend='tf'):
    """ Return the Keras model of the network
    """
    model = Sequential()
    if backend == 'tf':
        input_shape=(16, 112, 112, 3) # l, h, w, c
    else:
        input_shape=(3, 16, 112, 112) # c, l, h, w
    model.add(Convolution3D(64, 3, 3, 3, activation='relu',
                            border_mode='same', name='conv1',
                            input_shape=input_shape))
    model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),
                           border_mode='valid', name='pool1'))
    # 2nd layer group
    model.add(Convolution3D(128, 3, 3, 3, activation='relu',
                            border_mode='same', name='conv2'))
    model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
                           border_mode='valid', name='pool2'))
    # 3rd layer group
    model.add(Convolution3D(256, 3, 3, 3, activation='relu',
                            border_mode='same', name='conv3a'))
    model.add(Convolution3D(256, 3, 3, 3, activation='relu',
                            border_mode='same', name='conv3b'))
    model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
                           border_mode='valid', name='pool3'))
    # 4th layer group
    model.add(Convolution3D(512, 3, 3, 3, activation='relu',
                            border_mode='same', name='conv4a'))
    model.add(Convolution3D(512, 3, 3, 3, activation='relu',
                            border_mode='same', name='conv4b'))
    model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
                           border_mode='valid', name='pool4'))
    # 5th layer group
    model.add(Convolution3D(512, 3, 3, 3, activation='relu',
                            border_mode='same', name='conv5a'))
    model.add(Convolution3D(512, 3, 3, 3, activation='relu',
                            border_mode='same', name='conv5b'))
    model.add(ZeroPadding3D(padding=((0, 0), (0, 1), (0, 1)), name='zeropad5'))
    model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
                           border_mode='valid', name='pool5'))
    model.add(Flatten())
    # FC layers group
    model.add(Dense(4096, activation='relu', name='fc6'))
    model.add(Dropout(.5))
    model.add(Dense(4096, activation='relu', name='fc7'))
    model.add(Dropout(.5))
    model.add(Dense(487, activation='softmax', name='fc8'))

    if summary:
        print(model.summary())

    return model