您可以使用實作自定義函數的^{}層完成整個操作：from keras.models import Model
from keras.layers import Input, Lambda
from keras import backend as K
import numpy as np
# Masking function factory
def mask_img(x_size, y_size=None):
if y_size is None:
y_size = x_size
# Masking function
def mask_func(tensors):
img, xy = tensors
img_shape = K.shape(img)
# Make indexing arrays
xx = K.arange(img_shape[1])
yy = K.arange(img_shape[2])
# Get coordinates
xy = K.cast(xy, img_shape.dtype)
x = xy[:, 0:1]
y = xy[:, 1:2]
# Make X and Y masks
mask_x = (xx >= x) & (xx < x + x_size)
mask_y = (yy >= y) & (yy < y + y_size)
# Make full mask
mask = K.expand_dims(mask_x, 2) & K.expand_dims(mask_y, 1)
# Add channels dimension
mask = K.expand_dims(mask, -1)
# Multiply image and mask
mask = K.cast(mask, img.dtype)
return img * mask
return mask_func
# Model
img = Input(shape=(10, 10, 3)) # Small size for test
xy = Input(shape=(2,))
output = Lambda(mask_img(3))([img, xy])
model = Model(inputs=[img, xy], outputs=output)
# Test
img_test = np.arange(100).reshape((1, 10, 10, 1)).repeat(3, axis=-1)
xy_test = np.array([[2, 4]])
output_test = model.predict(x=[img_test, xy_test])
print(output_test[0, :, :, 0])
輸出：
^{pr2}$