Python keras.layers.convolutional 模块，AtrousConvolution2D() 实例源码

def constructNet(input_dim=784,n_hidden=1000,n_out=1000,nb_filter=50,prob=0.5,lr=0.0001):
    nb_filters=50
    input_img= Input(shape=list(input_dim))
    a = input_img

    a1 = AtrousConvolution2D(nb_filters, 3, 3,atrous_rate=(1,1),border_mode='same')(a)    
    b = AtrousConvolution2D(nb_filters, 3, 3,atrous_rate=(1,1),border_mode='same')(a)  #We only use the diagonal output from this, TODO: only filter diagonal
    a2=Lambda(GetDiag, output_shape=out_diag_shape)(b)
    comb=merge([a1,a2],mode='sum')
    comb = BatchNormalization()(comb)  
    a = Activation('relu')(comb)

    l=5
    for i in range(1,l):
        a1 = AtrousConvolution2D(nb_filters, 3, 3,atrous_rate=(l,l),border_mode='same')(a)    
        b = AtrousConvolution2D(nb_filters, 3, 3,atrous_rate=(l,l),border_mode='same')(a)  #We only use the diagonal output from this, TODO: only filter diagonal
        a2=Lambda(GetDiag, output_shape=out_diag_shape)(b)
        comb=merge([a1,a2],mode='sum')
        comb = BatchNormalization()(comb)  
        a = Activation('relu')(comb)

    decoded = Convolution2D(1, 1, 1, activation='sigmoid', border_mode='same')(a)
    final=Flatten()(decoded)
    model = Model(input_img, final)
    model.summary()
    model.compile(optimizer='adam', loss='binary_crossentropy')
    return model

def __transition_up_block(ip, nb_filters, type='upsampling', output_shape=None, weight_decay=1E-4):
    ''' SubpixelConvolutional Upscaling (factor = 2)
    Args:
        ip: keras tensor
        nb_filters: number of layers
        type: can be 'upsampling', 'subpixel', 'deconv', or 'atrous'. Determines type of upsampling performed
        output_shape: required if type = 'deconv'. Output shape of tensor
        weight_decay: weight decay factor
    Returns: keras tensor, after applying upsampling operation.
    '''

    if type == 'upsampling':
        x = UpSampling2D()(ip)
    elif type == 'subpixel':
        x = Convolution2D(nb_filters, 3, 3, activation="relu", border_mode='same', W_regularizer=l2(weight_decay),
                          bias=False, init='he_uniform')(ip)
        x = SubPixelUpscaling(scale_factor=2)(x)
        x = Convolution2D(nb_filters, 3, 3, activation="relu", border_mode='same', W_regularizer=l2(weight_decay),
                          bias=False, init='he_uniform')(x)
    elif type == 'atrous':
        # waiting on https://github.com/fchollet/keras/issues/4018
        x = AtrousConvolution2D(nb_filters, 3, 3, activation="relu", W_regularizer=l2(weight_decay),
                                bias=False, atrous_rate=(2, 2), init='he_uniform')(ip)
    else:
        x = Deconvolution2D(nb_filters, 3, 3, output_shape, activation='relu', border_mode='same',
                            subsample=(2, 2), init='he_uniform')(ip)

    return x