使用keras和plaidML在我的PC（Windows 10，i5 4440，r9 290）上训练U-net模型时，此消息显示：

INFO:plaidml:Analyzing Ops: 482 of 1013 operations complete
INFO:plaidml:Analyzing Ops: 671 of 1013 operations complete
ERROR:plaidml:Unable to allocate device-local memory: CL_INVALID_BUFFER_SIZE

驱动程序可能不想分配很大一部分的VRAM。或其他IDK。有帮助吗？

模型：

def unet(pretrained_weights = None,input_size = (512,512,1)):
    inputs = Input(input_size)
    conv1 = Conv2D(64,3,activation = 'relu',padding = 'same',kernel_initializer = 'he_normal')(inputs)
    conv1 = Conv2D(64,kernel_initializer = 'he_normal')(conv1)
    pool1 = MaxPooling2D(pool_size=(2,2))(conv1)
    conv2 = Conv2D(128,kernel_initializer = 'he_normal')(pool1)
    conv2 = Conv2D(128,kernel_initializer = 'he_normal')(conv2)
    pool2 = MaxPooling2D(pool_size=(2,2))(conv2)
    conv3 = Conv2D(256,kernel_initializer = 'he_normal')(pool2)
    conv3 = Conv2D(256,kernel_initializer = 'he_normal')(conv3)
    pool3 = MaxPooling2D(pool_size=(2,2))(conv3)
    conv4 = Conv2D(512,kernel_initializer = 'he_normal')(pool3)
    conv4 = Conv2D(512,kernel_initializer = 'he_normal')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2,2))(drop4)

    conv5 = Conv2D(1024,kernel_initializer = 'he_normal')(pool4)
    conv5 = Conv2D(1024,kernel_initializer = 'he_normal')(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(512,2,kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
    merge6 = concatenate([drop4,up6],axis = 3)
    conv6 = Conv2D(512,kernel_initializer = 'he_normal')(merge6)
    conv6 = Conv2D(512,kernel_initializer = 'he_normal')(conv6)

    up7 = Conv2D(256,2))(conv6))
    merge7 = concatenate([conv3,up7],axis = 3)
    conv7 = Conv2D(256,kernel_initializer = 'he_normal')(merge7)
    conv7 = Conv2D(256,kernel_initializer = 'he_normal')(conv7)

    up8 = Conv2D(128,2))(conv7))
    merge8 = concatenate([conv2,up8],axis = 3)
    conv8 = Conv2D(128,kernel_initializer = 'he_normal')(merge8)
    conv8 = Conv2D(128,kernel_initializer = 'he_normal')(conv8)

    up9 = Conv2D(64,2))(conv8))
    merge9 = concatenate([conv1,up9],axis = 3)
    conv9 = Conv2D(64,kernel_initializer = 'he_normal')(merge9)
    conv9 = Conv2D(64,kernel_initializer = 'he_normal')(conv9)
    conv9 = Conv2D(2,kernel_initializer = 'he_normal')(conv9)
    conv10 = Conv2D(1,1,activation = 'softmax')(conv9)

    model = Model(inputs = inputs,outputs = conv10)

    model.compile(optimizer = Adam(lr = 1e-4),loss = 'binary_crossentropy',metrics = ['accuracy'])

    #model.summary()

    if(pretrained_weights):
        model.load_weights(pretrained_weights)

    return model


my_model = unet(input_size=(512,1))