def get_feature_map_4(model, im):
im = im.astype(np.float32)
dim_ordering = K.image_dim_ordering()
if dim_ordering == 'th':
# 'RGB'->'BGR'
im = im[::-1, :, :]
# Zero-center by mean pixel
im[0, :, :] -= 103.939
im[1, :, :] -= 116.779
im[2, :, :] -= 123.68
else:
# 'RGB'->'BGR'
im = im[:, :, ::-1]
# Zero-center by mean pixel
im[:, :, 0] -= 103.939
im[:, :, 1] -= 116.779
im[:, :, 2] -= 123.68
im = im.transpose((2, 0, 1))
im = np.expand_dims(im, axis=0)
inputs = [K.learning_phase()] + model.inputs
_convout1_f = K.function(inputs, [model.layers[23].output])
feature_map = _convout1_f([0] + [im])
feature_map = np.array([feature_map])
feature_map = feature_map[0, 0, 0, :, :, :]
return feature_map
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