Python keras.layers.convolutional.MaxPooling3D() Examples

The following are 15 code examples of keras.layers.convolutional.MaxPooling3D(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.layers.convolutional , or try the search function

Example #1

Source File: liver_model.py From MCF-3D-CNN with MIT License

6 votes

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

Example #2

Source File: MSnetworks.py From CNNArt with Apache License 2.0

6 votes

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

Example #3

Source File: MSnetworks.py From CNNArt with Apache License 2.0

6 votes

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

Example #4