Python convolutional.MaxPooling3D方法代码示例

# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import MaxPooling3D [as 别名]
def cnn_3D(self, input_shape, modual=''):
        #建立Sequential模型
        model_in = Input(input_shape)    
        model = Convolution3D(
                filters = 6,
                kernel_size = (3, 3, 3),
                input_shape = input_shape,
                activation='relu',
                kernel_initializer='he_normal',
                name = modual+'conv1'
            )(model_in)# now 30x30x3x6
        model = MaxPooling3D(pool_size=(2,2,1))(model)# now 15x15x3x6
        model = Convolution3D(
                filters = 8,
                kernel_size = (4, 4, 3),
                activation='relu',
                kernel_initializer='he_normal',
                name = modual+'conv2'
            )(model)# now 12x12x1x8
        model = MaxPooling3D(pool_size=(2,2,1))(model)# now 6x6x1x8
        model = Flatten()(model)
        model = Dropout(0.5)(model)
        model_out = Dense(100, activation='relu', name = modual+'fc1')(model)
      
        return model_in, model_out 

# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import MaxPooling3D [as 别名]
def fSPP(inp, level=3):
    inshape = inp._keras_shape[2:]
    Kernel = [[0] * 3 for i in range(level)]
    Stride = [[0] * 3 for i in range(level)]
    SPPout = T.tensor5()
    for iLevel in range(level):
        Kernel[iLevel] = np.ceil(np.divide(inshape, iLevel+1, dtype = float)).astype(int)
        Stride[iLevel] = np.floor(np.divide(inshape, iLevel+1, dtype = float)).astype(int)
        if inshape[2]%3==2:
            Kernel[2][2] = Kernel[2][2] + 1
        poolLevel = MaxPooling3D(pool_size=Kernel[iLevel], strides=Stride[iLevel])(inp)
        if iLevel == 0:
            SPPout = Flatten()(poolLevel)
        else:
            poolFlat = Flatten()(poolLevel)
            SPPout = concatenate([SPPout,poolFlat], axis=1)
    return SPPout


# Models of FCN 

# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import MaxPooling3D [as 别名]
def InceptionBlock(inp, l1_reg=0.0, l2_reg=1e-6):
    KN = fgetKernelNumber()
    branch1 = Conv3D(filters=KN[0], kernel_size=(1,1,1), kernel_initializer='he_normal', weights=None,padding='same',
                     strides=(1,1,1),kernel_regularizer=l1_l2(l1_reg, l2_reg),activation='relu')(inp)

    branch3 = Conv3D(filters=KN[0], kernel_size=(1, 1, 1), kernel_initializer='he_normal', weights=None, padding='same',
                     strides=(1, 1, 1), kernel_regularizer=l1_l2(l1_reg, l2_reg), activation='relu')(inp)
    branch3 = Conv3D(filters=KN[2], kernel_size=(3, 3, 3), kernel_initializer='he_normal', weights=None, padding='same',
                     strides=(1, 1, 1), kernel_regularizer=l1_l2(l1_reg, l2_reg), activation='relu')(branch3)

    branch5 = Conv3D(filters=KN[0], kernel_size=(1, 1, 1), kernel_initializer='he_normal', weights=None, padding='same',
                     strides=(1, 1, 1), kernel_regularizer=l1_l2(l1_reg, l2_reg), activation='relu')(inp)
    branch5 = Conv3D(filters=KN[1], kernel_size=(5, 5, 5), kernel_initializer='he_normal', weights=None, padding='same',
                     strides=(1, 1, 1), kernel_regularizer=l1_l2(l1_reg, l2_reg), activation='relu')(branch5)

    branchpool = MaxPooling3D(pool_size=(3,3,3),strides=(1,1,1),padding='same',data_format='channels_first')(inp)
    branchpool = Conv3D(filters=KN[0], kernel_size=(1, 1, 1), kernel_initializer='he_normal', weights=None, padding='same',
                     strides=(1, 1, 1), kernel_regularizer=l1_l2(l1_reg, l2_reg), activation='relu')(branchpool)
    out = concatenate([branch1, branch3, branch5, branchpool], axis=1)
    return out 

# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import MaxPooling3D [as 别名]
def test_maxpooling_3d():
    pool_size = (3, 3, 3)

    layer_test(convolutional.MaxPooling3D,
               kwargs={'strides': 2,
                       'padding': 'valid',
                       'pool_size': pool_size},
               input_shape=(3, 11, 12, 10, 4))
    layer_test(convolutional.MaxPooling3D,
               kwargs={'strides': 3,
                       'padding': 'valid',
                       'data_format': 'channels_first',
                       'pool_size': pool_size},
               input_shape=(3, 4, 11, 12, 10)) 

# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import MaxPooling3D [as 别名]
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 

# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import MaxPooling3D [as 别名]
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 

# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import MaxPooling3D [as 别名]
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 

# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import MaxPooling3D [as 别名]
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 

# 需要导入模块: from keras.layers import convolutional [as 别名]
# 或者: from keras.layers.convolutional import MaxPooling3D [as 别名]
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