启用急切执行时不支持tf.gradients。使用tf.GradientTape代替
from tensorflow.keras.applications import VGG16
from tensorflow.keras import backend as K
model = VGG16(weights='imagenet',
include_top=False)
layer_name = 'block3_conv1'
filter_index = 0
layer_output = model.get_layer(layer_name).output
loss = K.mean(layer_output[:, :, :, filter_index])
grads = K.gradients(loss, model.input)[0]
我无法执行grads = K.gradients(loss, model.input)[0]，它产生一个错误：tf.gradients is not supported when eager execution is enabled. Use tf.GradientTape instead
回答
您有两个选项也可以解决此错误：
-
.gradients 在 TF2 中被 drepracted - 按照此处的建议用 GradientTape 替换梯度https://github.com/tensorflow/tensorflow/issues/33135
-
只需使用 tf1 的兼容模式禁用急切执行约束形式 tf2
解决方案 2 的示例运行代码：
from tensorflow.keras.applications import VGG16
from tensorflow.keras import backend as K
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
model = VGG16(weights='imagenet',
include_top=False)
layer_name = 'block3_conv1'
filter_index = 0
layer_output = model.get_layer(layer_name).output
loss = K.mean(layer_output[:, :, :, filter_index])
grads = K.gradients(loss, model.input)[0]
THE END
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